Neural network-based flight control systems: Present and future

Åström, 2008 Abaspour, 2015, Nonlinear optimized adaptive trajectory control of helicopter, Control Theory and Technology, 13, 297, 10.1007/s11768-015-4062-1 Abbaspour, 2017, Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV, ISA Transactions, 67, 317, 10.1016/j.isatra.2016.11.005 Abbaspour, 2018, A neural adaptive approach for active fault-tolerant control design in UAV, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 1 Abbeel, 2010, Autonomous helicopter aerobatics through apprenticeship learning, International Journal of Robotics Research, 29, 1608, 10.1177/0278364910371999 Abbeel, Pieter, & Ng, Andrew Y. (2004). Apprenticeship learning via inverse reinforcement learning. In Proceedings of 21st international conference on machine learning. Banff, Canada. Abu-Khalaf, 2006, Nonlinear H2/H_Infinity constrained feedback control: a practical design approach using neural networks Abu-Khalaf, 2005, Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach, Automatica, 41, 779, 10.1016/j.automatica.2004.11.034 Akpan, 2011, Nonlinear model identification and adaptive model predictive control using neural networks, ISA Transactions, 50, 177, 10.1016/j.isatra.2010.12.007 Al-Tamimi, 2008, Discrete-time nonlinear HJB solution using approximate dynamic programming: Convergence proof, IEEE Transactions on Systems, Man and Cybernetics. Part B, Cybernetics, 38, 943, 10.1109/TSMCB.2008.926614 Alexandridis, 2013, Wavelet neural networks: A practical guide, Neural Networks, 42, 1, 10.1016/j.neunet.2013.01.008 Ali Emami, 2020, Fault–tolerant predictive trajectory tracking of an air vehicle based on acceleration control, IET Control Theory & Applications, 14, 750, 10.1049/iet-cta.2019.0596 Amari, 1998, Natural gradient works efficiently in learning, Neural Computation, 10, 251, 10.1162/089976698300017746 Artale, 2016, An integrated system for UAV control using a neural network implemented in a prototyping board, Journal of Intelligent and Robotic Systems, 10.1007/s10846-015-0324-x Bagnell, 2001, Autonomous helicopter control using reinforcement learning policy search methods Baldi, 2016, Combined geometric and neural network approach to generic fault diagnosis in satellite actuators and sensors, IFAC-PapersOnLine, 49, 432, 10.1016/j.ifacol.2016.09.074 Baldi, 2013, Satellite attitude active FTC based on geometric approach and RBF neural network, 667 Bansal, 2016, Learning quadrotor dynamics using neural network for flight control Baydin, 2017, 1 Bea, 1998, Successive Galerkin approximation algorithms for nonlinear optimal and robust control, International Journal of Control, 71, 717, 10.1080/002071798221542 Beard, 1997, Galerkin approximations of the generalized Hamilton-Jacobi-Bellman equation, Automatica, 33, 2159, 10.1016/S0005-1098(97)00128-3 Beer, 1995, On the dynamics of small continuous-time recurrent neural networks, Adaptive Behavior, 3, 469, 10.1177/105971239500300405 Behtash, 2007, Robust output tracking for non-linear systems, International Journal of Control, 51, 1381, 10.1080/00207179008934141 Besselmann, 2012, Explicit MPC for LPV systems: Stability and optimality, IEEE Transactions on Automatic Control, 57, 2322, 10.1109/TAC.2012.2187400 Boskovic, 2004, Adaptive control design for nonaffine models arising in flight control, Journal of Guidance, Control, and Dynamics, 27, 209, 10.2514/1.1106 Bosworth, 2007, Flight test results from the NF-15B intelligent flight control system (IFCS) project with adaptation to a simulated stabilator failure Brinker, 2000, Flight testing of a reconfigurable flight control law on the X-36 tailless fighter aircraft Brinker, 2001, Flight testing of reconfigurable control law on the X-36 Tailless aircraft, Journal of Guidance, Control, and Dynamics, 24, 903, 10.2514/2.4826 Bryson, 1975 Bu, 2018, Air-breathing hypersonic vehicles funnel control using neural approximation of non-affine dynamics, IEEE/ASME Transactions on Mechatronics, 23, 2099, 10.1109/TMECH.2018.2869002 Bu, 2018, A fuzzy wavelet neural network-based approach to hypersonic flight vehicle direct nonaffine hybrid control, Nonlinear Dynamics, 94, 1657, 10.1007/s11071-018-4447-z Bu, 2015, Nonsingular direct neural control of air-breathing hypersonic vehicle via back-stepping, Neurocomputing, 153, 164, 10.1016/j.neucom.2014.11.040 Bu, 2019, An adaptive critic design-based fuzzy neural controller for hypersonic vehicles: Predefined behavioral nonaffine control, IEEE/ASME Transactions on Mechatronics, 24, 1871, 10.1109/TMECH.2019.2928699 Burken, John, Hanson, Curt, Lee, Jim, & Kaneshige, John (2009). Flight Test Comparison of Different Adaptive Augmentations of Fault Tolerant Control Laws for a Modified F-15 Aircraft. In AIAA infotech@aerospace conference. Seattle, Washington. Butt, 2013, Adaptive dynamic surface control of a hypersonic flight vehicle with improved tracking, Asian Journal of Control, 15, 594, 10.1002/asjc.450 Butt, 2013, Adaptive integral dynamic surface control of a hypersonic flight vehicle, International Journal of Systems Science, 46, 1717, 10.1080/00207721.2013.828798 Calise, 1996, Neural networks in nonlinear aircraft flight control, IEEE Aerospace and Electronic Systems Magazine, 11, 5, 10.1109/62.533965 Calise, 2000, Development of a reconfigurable flight control law for the X-36 tailless fighter aircraft Calise, 2001, Adaptive output feedback control of nonlinear systems using neural networks, Automatica, 37, 1201, 10.1016/S0005-1098(01)00070-X Calise, 2001, Development of a reconfigurable flight control law for tailless aircraft, Journal of Guidance, Control, and Dynamics, 24, 896, 10.2514/2.4825 Camci, 2018, An aerial robot for rice farm quality inspection with type-2 fuzzy neural networks tuned by particle swarm optimization-sliding mode control hybrid algorithm, Swarm and Evolutionary Computation, 41, 1, 10.1016/j.swevo.2017.10.003 Campa, 2002, On-line learning neural networks for sensor validation for the flight control system of a B777 research scale model, International Journal of Robust and Nonlinear Control, 12, 987, 10.1002/rnc.728 Carrio, 2017, A review of deep learning methods and applications for unmanned aerial vehicles, Journal of Sensors, 2017, 1, 10.1155/2017/3296874 Castaldi, 2014, Robust trajectory tracking for underactuated VTOL aerial vehicles: Extended for adaptive disturbance compensation Chen, 2015, Constrained control allocation for overactuated aircraft using a neurodynamic model, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans) Chen, 2019, Nussbaum functions in adaptive control with time-varying unknown control coefficients, Automatica, 102, 72, 10.1016/j.automatica.2018.12.035 Chen, 2011, Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints, Automatica, 47, 452, 10.1016/j.automatica.2011.01.025 Chen, 2009, Direct adaptive fuzzy control of nonlinear strict-feedback systems, Automatica, 45, 1530, 10.1016/j.automatica.2009.02.025 Chen, 2016, Adaptive neural fault-tolerant control of a 3-DOF model helicopter system, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 46, 260, 10.1109/TSMC.2015.2426140 Chen, 2014, Robust tracking control for uncertain MIMO nonlinear systems with input saturation using RWNNDO, Neurocomputing, 144, 436, 10.1016/j.neucom.2014.04.032 Cheng, 2020, Self-repairing control of air-breathing hypersonic vehicle with actuator fault and backlash, Aerospace Science and Technology, 97 Cheng, 2011, Online-SVR-compensated nonlinear generalized predictive control for hypersonic vehicles, Science China. Information Sciences, 54, 551, 10.1007/s11432-011-4195-x Chowdhary, 2011, Theory and flight-test validation of a concurrent-learning adaptive controller, Journal of Guidance, Control, and Dynamics, 34, 592, 10.2514/1.46866 Chowdhary, 2013, Guidance and control of airplanes under actuator failures and severe structural damage, Journal of Guidance, Control, and Dynamics, 36, 1093, 10.2514/1.58028 Chowdhary, 2014, Exponential parameter and tracking error convergence guarantees for adaptive controllers without persistency of excitation, International Journal of Control, 87, 1583, 10.1080/00207179.2014.880128 Clawson, 2016, Spiking neural network (SNN) control of a flapping insect-scale robot, 3381 Clough, Bruce T., & Patterson, Wright (2002). Metrics, Schmetrics! How The Heck Do You Determine A UAV’s Autonomy Anyway?. In Proceedings of the 2002 performance metrics for intelligent systems workshop. Gaithersburg, MD. Coates, 2009, Apprenticeship learning for helicopter control, Communications of the ACM, 52, 97, 10.1145/1538788.1538812 Coza, 2011, Adaptive fuzzy control for a quadrotor helicopter robust to wind buffeting, Journal of Intelligent and Fuzzy Systems, 22, 267, 10.3233/IFS-2011-0488 da Costa Lopes, 2015, A control-oriented model of a PEM fuel cell stack based on NARX and NOE neural networks, IEEE Transactions on Industrial Electronics, 62, 5155, 10.1109/TIE.2015.2412519 Dalamagkidis, 2011, Nonlinear model predictive control with neural network optimization for autonomous autorotation of small unmanned helicopters, IEEE Transactions on Control Systems Technology, 19, 818, 10.1109/TCST.2010.2054092 Das, 2009, Backstepping approach for controlling a quadrotor using Lagrange form dynamics, Journal of Intelligent and Robotic Systems, 56, 127, 10.1007/s10846-009-9331-0 de Croon, 2016 Degris, Thomas, White, Martha, & Sutton, Richard S. (2012). Off-Policy Actor-Critic. In International conference on machine learning. Scotland, UK. Dierks, 2010, Optimal control of affine nonlinear continuous-time systems using an online Hamilton-Jacobi-Isaacs formulation, 3048 Dierks, 2010, Output feedback control of a quadrotor UAV using neural networks, IEEE Transactions on Neural Networks, 21, 50, 10.1109/TNN.2009.2034145 Dong, 2012, Command filtered adaptive backstepping, IEEE Transactions on Control Systems Technology, 20, 566, 10.1109/TCST.2011.2121907 Dreyfus, 2005 Efe, 2011, Neural network assisted computationally simple PID control of a quadrotor UAV, IEEE Transactions on Industrial Informatics, 7, 354, 10.1109/TII.2011.2123906 Emami, 2021, A self-organizing multi-model ensemble for identification of nonlinear time-varying dynamics of aerial vehicles, Proceedings of the Institution of Mechanical Engineers, Part I (Journal of Systems and Control Engineering), 235, 1164 Emami, 2019, Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances, International Journal of Robust and Nonlinear Control, 29, 5820, 10.1002/rnc.4698 Emami, 2019, Online identification of aircraft dynamics in the presence of actuator faults, Journal of Intelligent and Robotic Systems, 96, 541, 10.1007/s10846-019-00998-z Emami, 2019, Multimodel ELM-based identification of an aircraft dynamics in the entire flight envelope, IEEE Transactions on Aerospace and Electronic Systems, 55, 2181, 10.1109/TAES.2018.2883848 Enns, 2002, Apache helicopter stabilization using neural dynamic programming, Journal of Guidance, Control, and Dynamics, 25, 19, 10.2514/2.4870 Enns, 2003, Helicopter trimming and tracking control using direct neural dynamic programming, IEEE Transactions on Neural Networks, 14, 929, 10.1109/TNN.2003.813839 Eze, 2012, A generic approach towards measuring level of autonomicity in adaptive systems, International Journal on Advances in Intelligent Systems, 5 Fabri, 1996, Dynamic structure neural networks for stable adaptive control of nonlinear systems, IEEE Transactions on Neural Networks, 7, 1151, 10.1109/72.536311 Farrell, 2003, Adaptive backstepping with magnitude, rate, and bandwidth constraints: Aircraft longitude control, 3898 Farrell, 2009, Command filtered backstepping, IEEE Transactions on Automatic Control, 54, 1391, 10.1109/TAC.2009.2015562 Ferdaus, 2019, PALM: AN incremental construction of hyperplanes for data stream regression, IEEE Transactions on Fuzzy Systems, 27, 2115, 10.1109/TFUZZ.2019.2893565 Ferdaus, 2020, PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles, Information Sciences, 512, 481, 10.1016/j.ins.2019.10.001 Ferdaus, 2020, Generic evolving self-organizing neuro-fuzzy control of bio-inspired unmanned aerial vehicles, IEEE Transactions on Fuzzy Systems, 28, 1542, 10.1109/TFUZZ.2019.2917808 Ferrari, 2004, Online adaptive critic flight control, Journal of Guidance, Control, and Dynamics, 27, 777, 10.2514/1.12597 Floreano, 2005, From wheels to wings with evolutionary spiking circuits, Artificial Life, 11, 121, 10.1162/1064546053278900 Fu, 2018, Adaptive robust backstepping attitude control for a multi-rotor unmanned aerial vehicle with time-varying output constraints, Aerospace Science and Technology, 78, 593, 10.1016/j.ast.2018.05.021 Fu, 2016, Neural adaptive control of hypersonic aircraft with actuator fault using randomly assigned nodes, Neurocomputing, 174, 1070, 10.1016/j.neucom.2015.10.023 Gao, 2014, A singularly perturbed system approach to adaptive neural back-stepping control design of hypersonic vehicles, Journal of Intelligent and Robotic Systems, 73, 249, 10.1007/s10846-013-9992-6 Garcia, 2015, Robust and adaptive nonlinear model predictive controller for unsteady and highly nonlinear unmanned aircraft, IEEE Transactions on Control Systems Technology, 23, 1620, 10.1109/TCST.2014.2377711 Ge, 2010, Robust attitude control of helicopters with actuator dynamics using neural networks, IET Control Theory & Applications, 4, 2837, 10.1049/iet-cta.2009.0478 Ge, 2009, Approximation-based control of uncertain helicopter dynamics, IET Control Theory & Applications, 3, 941, 10.1049/iet-cta.2008.0103 Ge, 2002, Direct adaptive NN control of a class of nonlinear systems, IEEE Transactions on Neural Networks, 13, 214, 10.1109/72.977306 Ge, 2003, Robust adaptive tracking for time-varying uncertain nonlinear systems with unknown control coefficients, IEEE Transactions on Automatic Control, 48, 1463, 10.1109/TAC.2003.815049 Giusti, 2016, A machine learning approach to visual perception of forest trails for mobile robots, IEEE Robotics and Automation Letters, 1, 661, 10.1109/LRA.2015.2509024 Gomi, 1993, Neural network control for a closed-loop system using feedback-error-learning, Neural Networks, 6, 933, 10.1016/S0893-6080(09)80004-X Gottfredson, 1997, Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography, Intelligence, 24, 13, 10.1016/S0160-2896(97)90011-8 Gu, 2019, A survey of artificial neural networks with model-based control techniques for flight control of unmanned aerial vehicles, 362 Gu, 2020, UAV model-based flight control with artificial neural networks: A survey, Journal of Intelligent and Robotic Systems, 100, 1469, 10.1007/s10846-020-01227-8 Ha, 2019, Topology-guided path integral approach for stochastic optimal control in cluttered environment, Robotics and Autonomous Systems, 113, 81, 10.1016/j.robot.2019.01.001 Hagan, 2016 Hageman, 2003 Hagenaars, 2020, Evolved neuromorphic control for high speed divergence-based landings of MAVs, IEEE Robotics and Automation Letters, 5, 6239, 10.1109/LRA.2020.3012129 Hamel, 2014, Cessna citation X airplane grey-box model identification without preliminary data Han, 2002, State-constrained agile missile control with adaptive-critic-based neural networks, IEEE Transactions on Control Systems Technology, 10, 481, 10.1109/TCST.2002.1014669 Hanson, 2014 Hanson, 2012 He, 2014, Quasi-min–max MPC for constrained nonlinear systems with guaranteed input-to-state stability, Journal of the Franklin Institute, 351, 3405, 10.1016/j.jfranklin.2014.03.006 He, 2017, Adaptive neural network control of a flapping wing micro aerial vehicle with disturbance observer, IEEE Transactions on Cybernetics, 47, 3452, 10.1109/TCYB.2017.2720801 Hopfe, 2010, Funnel control with saturation: Linear MIMO systems, IEEE Transactions on Automatic Control, 55, 532, 10.1109/TAC.2009.2037475 Hopfe, 2010, Funnel control with saturation: Nonlinear SISO systems, IEEE Transactions on Automatic Control, 55, 2177, 10.1109/TAC.2010.2051735 Hovakimyan, 2010, Vol. 21 Hovakimyan, 2011, L1 adaptive control for safety-critical systems, IEEE Control Systems, 31, 54, 10.1109/MCS.2011.941961 Hovakimyan, 2001, Adaptive output feedback for high-bandwidth control of an unmanned helicopter Hovakimyan, 2002, Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks, IEEE Transactions on Neural Networks, 13, 1420, 10.1109/TNN.2002.804289 Huang, 2004, An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks, IEEE Transactions on Systems, Man and Cybernetics. Part B, Cybernetics, 34, 2284, 10.1109/TSMCB.2004.834428 Hwangbo, 2017, Control of a quadrotor with reinforcement learning, IEEE Robotics and Automation Letters, 2, 2096, 10.1109/LRA.2017.2720851 Ilchmann, 2007, Tracking with prescribed transient behavior for nonlinear systems of known relative degree, SIAM Journal on Control and Optimization, 46, 210, 10.1137/050641946 Ioannou, 2014, L1-adaptive control: Stability, robustness, and interpretations, IEEE Transactions on Automatic Control, 59, 3075, 10.1109/TAC.2014.2318871 1983, Vol. 47 Ioannou, 2012 Ismail, 2014, Improved neural-aided sliding mode controller for autolanding under actuator failures and severe winds, Aerospace Science and Technology, 33, 55, 10.1016/j.ast.2013.12.016 Jang, 2019, Q-learning algorithms: A comprehensive classification and applications, IEEE Access, 7, 133653, 10.1109/ACCESS.2019.2941229 Jia, 2016, Adaptive control of nonlinear system using online error minimum neural networks, ISA Transactions, 10.1016/j.isatra.2016.07.012 Jiang, 1994, Small-gain theorem for ISS systems and applications, Mathematics of Control, Signals, and Systems, 7, 95, 10.1007/BF01211469 Johnson, 2000, Neural network adaptive control of systems with input saturation, 3527 Johnson, 2003, Limited authority adaptive flight control for reusable launch vehicles, Journal of Guidance, Control, and Dynamics, 26, 906, 10.2514/2.6934 Johnson, Eric, Calise, Anthony, El-Shirbiny, Hesham, & Eysdyk, Rolf (2000). Feedback linearization with Neural Network augmentation applied to X-33 attitude control. In AIAA guidance, navigation, and control conference and exhibit. Denver, CO. Johnson, Eric N., & Kannan, Suresh K. (2002). Adaptive Flight Control for an Autonomous Unmanned Helicopter. In AIAA guidance, navigation, and control conference and exhibit. Monterey, California. Johnson, 2005, Adaptive trajectory control for autonomous helicopters, Journal of Guidance, Control, and Dynamics, 28, 524, 10.2514/1.6271 Johnson, 2006, Modeling, control, and flight testing of a small-ducted fan aircraft, Journal of Guidance, Control, and Dynamics, 29, 769, 10.2514/1.16380 Ju, 2013, Data-driven control of flapping flight, ACM Transactions on Graphics, 32, 1, 10.1145/2516971.2516976 Juang, 2006, Application of neural networks to disturbances encountered landing control, IEEE Transactions on Intelligent Transportation Systems, 7, 582, 10.1109/TITS.2006.884885 2010 Kakade, Sham, & Langford, John (2002). Approximately optimal approximate reinforcement learning. In 19th international conference on machine learning (pp. 267–274). Kalise, 2020, Robust feedback control of nonlinear PDEs by numerical approximation of high-dimensional Hamilton–Jacobi–Isaacs equations, SIAM Journal on Applied Dynamical Systems, 19, 1496, 10.1137/19M1262139 Kaminski, 2017 Kang, 2019, Deep convolutional identifier for dynamic modeling and adaptive control of unmanned helicopter, IEEE Transactions on Neural Networks and Learning Systems, 30, 524, 10.1109/TNNLS.2018.2844173 Kappen, 2005, Path integrals and symmetry breaking for optimal control theory, Journal of Statistical Mechanics: Theory and Experiment, 2005, P11011, 10.1088/1742-5468/2005/11/P11011 Kayacan, 2016 Kayacan, 2012, Sliding mode control approach for online learning as applied to type-2 fuzzy neural networks and its experimental evaluation, IEEE Transactions on Industrial Electronics, 59, 3510, 10.1109/TIE.2011.2182017 Kayacan, 2017, Type-2 fuzzy logic trajectory tracking control of quadrotor VTOL aircraft with elliptic membership functions, IEEE/ASME Transactions on Mechatronics, 22, 339, 10.1109/TMECH.2016.2614672 Khalil, 2002 Khan, 2012, Reinforcement learning and optimal adaptive control: An overview and implementation examples, Annual Reviews in Control, 36, 42, 10.1016/j.arcontrol.2012.03.004 Khanesar, 2015, Optimal sliding mode type-2 TSK fuzzy control of a 2-DOF helicopter, 1 Kim, 2003 Kim, 1997, Nonlinear flight control using neural networks, Journal of Guidance, Control, and Dynamics, 20, 26, 10.2514/2.4029 Kim, Dong Ki, & Chen, Tsuhan (0000). Deep neural network for real-time autonomous indoor navigation. Kober, 2013, Reinforcement learning in robotics: A survey, International Journal of Robotics Research, 32, 1238, 10.1177/0278364913495721 Koos, 2013, The transferability approach: Crossing the reality gap in evolutionary robotics, IEEE Transactions on Evolutionary Computation, 17, 122, 10.1109/TEVC.2012.2185849 Krstić, 1992, Adaptive nonlinear control without overparametrization, Systems & Control Letters, 19, 177, 10.1016/0167-6911(92)90111-5 Kuo, 2021, Intelligent leader-following consensus formation control using recurrent neural networks for small-size unmanned helicopters, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 51, 1288, 10.1109/TSMC.2019.2896958 Kurnaz, 2010, Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles, Expert Systems with Applications, 37, 1229, 10.1016/j.eswa.2009.06.009 Lai, 2016, Adaptive position/attitude tracking control of aerial robot with unknown inertial matrix based on a new robust neural identifier, IEEE Transactions on Neural Networks and Learning Systems, 27, 18, 10.1109/TNNLS.2015.2406812 Lambert, 2019, Low-level control of a quadrotor with deep model-based reinforcement learning, IEEE Robotics and Automation Letters, 4, 4224, 10.1109/LRA.2019.2930489 Ławryńczuk, 2014 Lee, 1990, Fuzzy logic in control systems: Fuzzy logic controller. I, IEEE Transactions on Systems, Man and Cybernetics, 20, 404, 10.1109/21.52551 Lee, 2020, Aggressive perception-aware navigation using deep optical flow dynamics and PixelMPC, IEEE Robotics and Automation Letters, 5, 1207, 10.1109/LRA.2020.2965911 Lee, 2005, Adaptive nonlinear control system design for helicopter robust command augmentation, Aerospace Science and Technology, 9, 241, 10.1016/j.ast.2004.12.007 Lee, 2001, Nonlinear adaptive flight control using backstepping and neural networks controller, Journal of Guidance, Control, and Dynamics, 24, 675, 10.2514/2.4794 Leung, 2003, Tuning of the structure and parameters of a neural network using an improved genetic algorithm, IEEE Transactions on Neural Networks, 14, 79, 10.1109/TNN.2002.804317 Levant, 1998, Robust exact differentiation via sliding mode technique, Automatica, 34, 379, 10.1016/S0005-1098(97)00209-4 Levant, 2003, Higher-order sliding modes, differentiation and output-feedback control, International Journal of Control, 76, 924, 10.1080/0020717031000099029 Levine, Sergey, & Koltun, Vladlen (2013). Guided Policy Search. In 30th international conference on machine learning. Atlanta, Georgia, USA. Lewis, 2009, Reinforcement learning and adaptive dynamic programming for feedback control, IEEE Circuits and Systems Magazine, 9, 32, 10.1109/MCAS.2009.933854 Li, 2014 Li, 2017, Adaptive neural tracking control for uncertain nonlinear systems with input and output constraints using disturbance observer, Neurocomputing, 235, 27, 10.1016/j.neucom.2016.12.032 Li, 2016, Trajectory-tracking control of mobile robot systems incorporating neural-dynamic optimized model predictive approach, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 46, 740, 10.1109/TSMC.2015.2465352 Li, 2020, Predefined-time adaptive fault-tolerant control of hypersonic flight vehicles without overparameterization, Aerospace Science and Technology, 104, 10.1016/j.ast.2020.105987 Li, 2001, Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks, Automatica, 37, 1293, 10.1016/S0005-1098(01)00090-5 Li, 2004, Robust neuro-h∞ controller design for aircraft auto-landing, IEEE Transactions on Aerospace and Electronic Systems, 40, 158, 10.1109/TAES.2004.1292150 Li, 2010, A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems, IEEE Transactions on Systems, Man and Cybernetics. Part B, Cybernetics, 40, 915, 10.1109/TSMCB.2009.2033563 Li, 2016, Adaptive RBFNNs/integral sliding mode control for a quadrotor aircraft, Neurocomputing, 216, 126, 10.1016/j.neucom.2016.07.033 Liang, 2006, A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Transactions on Neural Networks, 17, 1411, 10.1109/TNN.2006.880583 Liang, 2019, Learning to guide: Guidance law based on deep meta-learning and model predictive path integral control, IEEE Access, 7, 47353, 10.1109/ACCESS.2019.2909579 Lillicrap, Timothy P., Hunt, Jonathan J., Pritzel, Alexander, Heess, Nicolas, Erez, Tom, Tassa, Yuval, et al. (2016). Continuous control with deep reinforcement learning. In International conference on learning representations. San Juan, Puerto Rico. Lin, 2007, Robust adaptive critic control of nonlinear systems using fuzzy basis function networks: An LMI approach, Information Sciences, 177, 4934, 10.1016/j.ins.2007.06.017 Lin, 2009, H∞ reinforcement learning control of robot manipulators using fuzzy wavelet networks, Fuzzy Sets and Systems, 160, 1765, 10.1016/j.fss.2008.09.010 Lin, 2015, Autolanding control using recurrent wavelet Elman neural network, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 45, 1281, 10.1109/TSMC.2015.2389752 Lin, 2014, Intelligent control system design for UAV using a recurrent wavelet neural network, Neural Computing and Applications, 24, 487, 10.1007/s00521-012-1242-5 Liu, 2006, A new control scheme for nonlinear systems with disturbances, IEEE Transactions on Control Systems Technology, 14, 176, 10.1109/TCST.2005.860510 Liu, 2019, Composite learning adaptive sliding mode control of fractional-order nonlinear systems with actuator faults, Journal of the Franklin Institute, 356, 9580, 10.1016/j.jfranklin.2019.02.042 Liu, 2014, Neural-network-based online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems, IEEE Transactions on Cybernetics, 44, 2834, 10.1109/TCYB.2014.2357896 Liu, 2019, Integrated guidance and control with partial state constraints and actuator faults, Journal of the Franklin Institute, 356, 4785, 10.1016/j.jfranklin.2019.04.008 Liu, 2010, Direct adaptive NN control for a class of discrete-time nonlinear strict-feedback systems, Neurocomputing, 73, 2498, 10.1016/j.neucom.2010.06.001 Long, Lyle, & Kelley, Troy (2009). The Requirements and Possibilities of Creating Conscious Systems. In AIAA Infotech@ aerospace conference. Seattle, Washington. Lu, 1998, Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm, IEEE Transactions on Neural Networks, 9, 308, 10.1109/72.661125 Lungu, 2020, Auto-landing of UAVs with variable centre of mass using the backstepping and dynamic inversion control, Aerospace Science and Technology, 103 Lungu, 2016, Landing auto-pilots for aircraft motion in longitudinal plane using adaptive control laws based on neural networks and dynamic inversion, Asian Journal of Control Luo, 2015, Adaptive critic design-based robust neural network control for nonlinear distributed parameter systems with unknown dynamics, Neurocomputing, 148, 200, 10.1016/j.neucom.2013.08.049 Luo, 2015, Off-policy reinforcement learning for H∞ control design, IEEE Transactions on Cybernetics, 45, 65, 10.1109/TCYB.2014.2319577 Luo, 2018, Optimal output regulation for model-free quanser helicopter with multistep Q-learning, IEEE Transactions on Industrial Electronics, 65, 4953, 10.1109/TIE.2017.2772162 Macnab, 2006, Robust associative-memory adaptive control in the presence of persistent oscillations, Neural Information Processing, 10, 277 Mania, Horia, Guy, Aurelia, & Recht, Benjamin 0000. Simple random search provides a competitive approach to reinforcement learning. Mayne, 2013, An apologia for stabilising terminal conditions in model predictive control, International Journal of Control, 86, 2090, 10.1080/00207179.2013.813647 Mo, 2018, Nonlinear and adaptive intelligent control techniques for quadrotor UAV – A survey, Asian Journal of Control, 21, 989, 10.1002/asjc.1758 Mohd Basri, 2015, Intelligent adaptive backstepping control for MIMO uncertain non-linear quadrotor helicopter systems, Transactions of the Institute of Measurement and Control, 37, 345, 10.1177/0142331214538900 Napolitano, 2000, A fault tolerant flight control system for sensor and actuator failures using neural networks, Aircraft Design, 3, 103, 10.1016/S1369-8869(00)00009-4 Narendra, 1987, A new adaptive law for robust adaptation without persistent excitation, IEEE Transactions on Automatic Control, 1067 Ng, 2006, Autonomous inverted helicopter flight via reinforcement learning, Vol. 21, 363 Ng, Andrew Y., Harada, Daishi, & Russell, Stuart (1999). Policy invariance under reward transformations: Theory and application to reward shaping. In Ivan Bratko, & Sason Dzeroski (Eds.), Proceedings of the sixteenth international conference on machine learning. Bled, Slovenia: ISBN: 1-55860-612-2. Ngo, 2005, Integrator backstepping using barrier functions for systems with multiple state constraints, 8306 Nguyen, 2006, Dynamics and adaptive control for stability recovery of damaged asymmetric aircraft Nguyen, 2008, Flight dynamics and hybrid adaptive control of damaged aircraft, Journal of Guidance, Control, and Dynamics, 31, 751, 10.2514/1.28142 Nicol, C., Macnab, C. J. B., & Ramirez-Serranob, A. (2008). Robust neural network control of a quadrotor helicopter. In Canadian conference on electrical and computer engineering. Niagara Falls, Canada. Nie, 2019, Three-dimensional path-following control of a robotic airship with reinforcement learning, International Journal of Aerospace Engineering, 2019, 1, 10.1155/2019/7854173 Nodland, 2013, Neural network-based optimal adaptive output feedback control of a helicopter UAV, IEEE Transactions on Neural Networks and Learning Systems, 24, 1061, 10.1109/TNNLS.2013.2251747 Nussbaum, 1983, Some remarks on a conjecture in parameter adaptive control, Systems & Control Letters, 3, 243, 10.1016/0167-6911(83)90021-X Palunko, 2013, A reinforcement learning approach towards autonomous suspended load manipulation using aerial robots Pashilkar, 2006, A fault-tolerant neural aided controller for aircraft auto-landing, Aerospace Science and Technology, 10, 49, 10.1016/j.ast.2005.05.002 Pashilkar, 2006, Adaptive back-stepping neural controller for reconfigurable flight control systems, IEEE Transactions on Control Systems Technology, 14, 553, 10.1109/TCST.2005.863672 Patan, 2015, Neural network-based model predictive control: Fault tolerance and stability, IEEE Transactions on Control Systems Technology, 23, 1147, 10.1109/TCST.2014.2354981 Patan, 2012, Nonlinear model predictive control of a boiler unit: A fault tolerant control study, International Journal of Applied Mathematics and Computer Science, 22, 225, 10.2478/v10006-012-0017-6 Patre, 2008, Asymptotic tracking for uncertain dynamic systems via a multilayer neural network feedforward and RISE feedback control structure, IEEE Transactions on Automatic Control, 53, 2180, 10.1109/TAC.2008.930200 Peng, 2020, Adaptive fault tolerant control for hypersonic flight vehicle system with state constraints, Journal of the Franklin Institute, 357, 9351, 10.1016/j.jfranklin.2020.07.014 de Persis, 2001, A geometric approach to nonlinear fault detection and isolation, IEEE Transactions on Automatic Control, 45, 853, 10.1109/9.928586 Pi, 2020, Low-level autonomous control and tracking of quadrotor using reinforcement learning, Control Engineering Practice, 95 Polycarpou, 1996, Stable adaptive neural control scheme for nonlinear systems, IEEE Transactions on Automatic Control, 41, 447, 10.1109/9.486648 Pratama, 2014, GENEFIS: TOward an effective localist network, IEEE Transactions on Fuzzy Systems, 22, 547, 10.1109/TFUZZ.2013.2264938 Protti, Marco, & Barzan, Riccardo (2007). UAV Autonomy – Which level is desirable? – Which level is acceptable? Alenia Aeronautica Viewpoint. In Platform innovations and system integration for unmanned air, land and sea vehicles. Neuilly-sur-Seine, France. Rahideh, 2012, Real time adaptive nonlinear model inversion control of a twin rotor MIMO system using neural networks, Engineering Applications of Artificial Intelligence, 25, 1289, 10.1016/j.engappai.2011.12.006 Razmi, 2019, Neural network-based adaptive sliding mode control design for position and attitude control of a quadrotor UAV, Aerospace Science and Technology, 91, 12, 10.1016/j.ast.2019.04.055 Recht, 2019, A tour of reinforcement learning: The view from continuous control, Annual Review of Control, Robotics, and Autonomous Systems, 2, 253, 10.1146/annurev-control-053018-023825 Reddy, 2016, Learning to soar in turbulent environments, Proceedings of the National Academy of Sciences of the United States of America, 113, E4877 Reddy, 2018, Glider soaring via reinforcement learning in the field, Nature, 562, 236, 10.1038/s41586-018-0533-0 Rinaldi, 2013, Linear quadratic control for quadrotors UAVs dynamics and formation flight, Journal of Intelligent and Robotic Systems, 70, 203, 10.1007/s10846-012-9708-3 Rodriguez-Ramos, 2019, A deep reinforcement learning strategy for UAV autonomous landing on a moving platform, Journal of Intelligent and Robotic Systems, 93, 351, 10.1007/s10846-018-0891-8 Rysdyk, R., & Calise, A. (1998). Fault tolerant flight control via adaptive neural network augmentation. In Guidance, navigation, and control conference and exhibit. Boston, USA. Rysdyk, 2005, Robust nonlinear adaptive flight control for consistent handling qualities, IEEE Transactions on Control Systems Technology, 13, 896, 10.1109/TCST.2005.854345 Sadeghi, Fereshteh, & Levine, Sergey (2017). CAD2RL: Real Single-Image Flight without a Single Real Image. In Robotics: Science and systems conference. Cambridge MA, USA. Salimans, Tim, Ho, Jonathan, Chen, Xi, Sidor, Szymon, & Sutskever, Ilya (0000). Evolution strategies as a scalable alternative to reinforcement learning. Sampedro, 2018, Laser-based reactive navigation for multirotor aerial robots using deep reinforcement learning Santoso, 2018, State-of-the-art intelligent flight control systems in unmanned aerial vehicles, IEEE Transactions on Automation Science and Engineering, 15, 613, 10.1109/TASE.2017.2651109 Sarabakha, 2018, Type-2 fuzzy logic controllers made even simpler: From design to deployment for UAVs, IEEE Transactions on Industrial Electronics, 65, 5069, 10.1109/TIE.2017.2767546 Saratchandran, 2000, Analysis of minimal radial basis function network algorithm for real-time identification of nonlinear dynamic systems, IEE Proceedings D (Control Theory and Applications), 147, 476, 10.1049/ip-cta:20000549 Savran, 2006, Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks, ISA Transactions, 45, 225, 10.1016/S0019-0578(07)60192-X Scheper, 2020, Evolution of robust high speed optical-flow-based landing for autonomous MAVs, Robotics and Autonomous Systems, 124 Scheper, 2016, Behavior trees for evolutionary robotics, Artificial Life, 22, 23, 10.1162/ARTL_a_00192 Schulman, John, Levine, Sergey, Moritz, Philipp, Jordan, Michael, & Abbeel, Pieter (2015). Trust Region Policy Optimization. In 31 st international conference on machine learning. Lille, France. Schulman, John, Wolski, Filip, Dhariwal, Prafulla, Radford, Alec, & Klimov, Oleg (0000). Proximal policy optimization algorithms. Schumann, 2010, Application of neural networks in high assurance systems: A survey, Vol. 268, 1 Seshagiri, 2000, Output feedback control of nonlinear systems using RBF neural networks, IEEE Transactions on Neural Networks, 11, 69, 10.1109/72.822511 Shao, 2019, Adaptive discrete-time flight control using disturbance observer and neural networks, IEEE Transactions on Neural Networks and Learning Systems, 30, 3708, 10.1109/TNNLS.2019.2893643 Shi, 2018, Decoupled visual servoing with fuzzy Q-learning, IEEE Transactions on Industrial Informatics, 14, 241, 10.1109/TII.2016.2617464 Shi, 2014, Adaptive neural observer-based backstepping fault tolerant control for near space vehicle under control effector damage, IET Control Theory & Applications, 8, 658, 10.1049/iet-cta.2013.0404 Shin, 2011, Adaptive support vector regression for UAV flight control, Neural Networks, 24, 109, 10.1016/j.neunet.2010.09.011 Shin, 2004, Reconfigurable flight control system design using adaptive neural networks, IEEE Transactions on Control Systems Technology, 12, 87, 10.1109/TCST.2003.821957 Shin, 2006, Nonlinear discrete-time reconfigurable flight control law using neural networks, IEEE Transactions on Control Systems Technology, 14, 408, 10.1109/TCST.2005.863662 Shin, Jongho, Kim, H. J., Kim, Youdan, & Dixon, Warren E. (2012). Autonomous flight of the rotorcraft-based UAV using RISE feedback and NN feedforward terms 20(5) 1392–1399. Si, 2004, Vol. 2 Silva, 2016, Open issues in evolutionary robotics, Evolutionary Computation, 24, 205, 10.1162/EVCO_a_00172 Silver, David, Lever, Guy, Heess, Nicolas, Degris, Thomas, Wierstra, Daan, & Riedmiller, Martin (2014). Deterministic Policy Gradient Algorithms. In 31st international conference on machine learning: Vol. 32, Beijing, China. Singla, 2021, Memory-based deep reinforcement learning for obstacle avoidance in UAV with limited environment knowledge, IEEE Transactions on Intelligent Transportation Systems, 22, 107, 10.1109/TITS.2019.2954952 Smith, 2010, Design and flight test of an intelligent flight control system, Vol. 268, 57 Soares, 2016, An adaptive ensemble of on-line extreme learning machines with variable forgetting factor for dynamic system prediction, Neurocomputing, 171, 693, 10.1016/j.neucom.2015.07.035 Song, 2019, Neuroadaptive fault-tolerant control of quadrotor UAVs: A more affordable solution, IEEE Transactions on Neural Networks and Learning Systems, 30, 1975, 10.1109/TNNLS.2018.2876130 Sonneveldt, 2007, Nonlinear flight control design using constrained adaptive backstepping, Journal of Guidance, Control, and Dynamics, 30, 322, 10.2514/1.25834 Sonneveldt, 2009, Nonlinear adaptive trajectory control applied to an F-16 model, Journal of Guidance, Control, and Dynamics, 32, 25, 10.2514/1.38785 Srivastava, 2014, Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, 15, 1929 Steinberg, 2005, Historical overview of research in reconfigurable flight control, Proceedings of the Institution of Mechanical Engineers, Part G (Journal of Aerospace Engineering), 219, 263, 10.1243/095441005X30379 Suresh, 2005, Nonlinear adaptive neural controller for unstable aircraft, Journal of Guidance, Control, and Dynamics, 28, 1103, 10.2514/1.12974 Sutton, 1998 Swaroop, 2000, Dynamic surface control for a class of nonlinear systems, IEEE Transactions on Automatic Control, 45, 1893, 10.1109/TAC.2000.880994 Talebi, 2010 Talebi, 2009, A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite’s attitude control subsystem, IEEE Transactions on Neural Networks, 20, 45, 10.1109/TNN.2008.2004373 Tang, 2009, Methodologies for adaptive flight envelope estimation and protection Tang, 2003, Adaptive actuator failure compensation for parametric strict feedback systems and an aircraft application, Automatica, 39, 1975, 10.1016/S0005-1098(03)00219-X Tang, 2014, Nonlinear system identification of a small-scale unmanned helicopter, Control Engineering Practice, 25, 1, 10.1016/j.conengprac.2013.12.004 Tee, 2008, Adaptive neural network control for helicopters in vertical flight, IEEE Transactions on Control Systems Technology, 16, 753, 10.1109/TCST.2007.912242 Tee, 2009, Barrier Lyapunov functions for the control of output-constrained nonlinear systems, Automatica, 45, 918, 10.1016/j.automatica.2008.11.017 Tischler, 2006 Tomayko, 2003 Tzirkel-Hancock, 1992, Stable control of nonlinear systems using neural networks, International Journal of Robust and Nonlinear Control, 2, 63, 10.1002/rnc.4590020105 Vagia, 2016, A literature review on the levels of automation during the years. What are the different taxonomies that have been proposed?, Applied Ergonomics, 53 Pt A, 190, 10.1016/j.apergo.2015.09.013 Vamvoudakis, 2014, Event-triggered optimal adaptive control algorithm for continuous-time nonlinear systems, IEEE/CAA Journal of Automatica Sinica, 1, 282, 10.1109/JAS.2014.7004686 Vamvoudakis, 2010, Online actor–critic algorithm to solve the continuous-time infinite horizon optimal control problem, Automatica, 46, 878, 10.1016/j.automatica.2010.02.018 Vamvoudakis, 2017, Event-triggered optimal tracking control of nonlinear systems, International Journal of Robust and Nonlinear Control, 27, 598, 10.1002/rnc.3587 van der Schaft, 1992, L/sub 2/-gain analysis of nonlinear systems and nonlinear state-feedback H/sub infinity/control, IEEE Transactions on Automatic Control, 37, 770, 10.1109/9.256331 Verhaegen, 2010, Fault tolerant flight control - a survey Vijaya Kumar, 2009, A direct adaptive neural command controller design for an unstable helicopter, Engineering Applications of Artificial Intelligence, 22, 181, 10.1016/j.engappai.2008.07.004 Vrabie, 2009, Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems, Neural Networks, 22, 237, 10.1016/j.neunet.2009.03.008 Wang, 2010, Optimizing walking controllers for uncertain inputs and environments, ACM Transactions on Graphics, 29, 1 Wang, 2017, Improving the critic learning for event-based nonlinear H_infinity control design, IEEE Transactions on Cybernetics, 47, 3417, 10.1109/TCYB.2017.2653800 Wang, 2016, A novel extreme learning control framework of unmanned surface vehicles, IEEE Transactions on Cybernetics, 46, 1106, 10.1109/TCYB.2015.2423635 Wang, 2019, Deterministic policy gradient with integral compensator for robust quadrotor control, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 1 Wang, 2019, Autonomous navigation of UAVs in large-scale complex environments: A deep reinforcement learning approach, IEEE Transactions on Vehicular Technology, 68, 2124, 10.1109/TVT.2018.2890773 Wang, 2015, Neural control of hypersonic flight dynamics with actuator fault and constraint, Science China. Information Sciences, 58, 1 Wang, 2019, Adaptive finite-time reconfiguration control of unmanned aerial vehicles with a moving leader, Nonlinear Dynamics, 95, 1099, 10.1007/s11071-018-4618-y Wang, 2018, Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters, ISA Transactions, 73, 208, 10.1016/j.isatra.2017.12.011 Waslander, 2005, Multi-agent quadrotor testbed control design: Integral sliding mode vs. reinforcement learning, 3712 Wen, 2011, Robust adaptive control of uncertain nonlinear systems in the presence of input saturation and external disturbance, IEEE Transactions on Automatic Control, 56, 1672, 10.1109/TAC.2011.2122730 Werbos, 1992, Approximate dynamic programming for real-time control and neural modeling Williams, 2004, Selected flight test results for online learning neural network-based flight control system Williams, 2017, Information theoretic MPC for model-based reinforcement learning Won, 2017, How to train your dragon, ACM Transactions on Graphics, 36, 1, 10.1145/3130800.3130833 Wu, 2001, A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks, IEEE Transactions on Fuzzy Systems, 9, 578, 10.1109/91.940970 Wu, 2017, Modified adaptive neural dynamic surface control for morphing aircraft with input and output constraints, Nonlinear Dynamics, 87, 2367, 10.1007/s11071-016-3196-0 Xian, 2015, Nonlinear robust output feedback tracking control of a quadrotor UAV using quaternion representation, Nonlinear Dynamics, 79, 2735, 10.1007/s11071-014-1843-x Xu, 2015, Robust adaptive neural control of flexible hypersonic flight vehicle with dead-zone input nonlinearity, Nonlinear Dynamics, 80, 1509, 10.1007/s11071-015-1958-8 Xu, 2018, Composite learning finite-time control with application to quadrotors, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 48, 1806, 10.1109/TSMC.2017.2698473 Xu, 2015, Minimal-learning-parameter technique based adaptive neural control of hypersonic flight dynamics without back-stepping, Neurocomputing, 164, 201, 10.1016/j.neucom.2015.02.069 Xu, 2011, Adaptive neural control based on HGO for hypersonic flight vehicles, Science China. Information Sciences, 54, 511, 10.1007/s11432-011-4189-8 Xu, 2016, Fault-tolerant control using command-filtered adaptive back-stepping technique: Application to hypersonic longitudinal flight dynamics, International Journal of Adaptive Control and Signal Processing, 30, 553, 10.1002/acs.2596 Xu, Haojian, Mirmirani, Maj, & Ioannou, Petros A. (2003). Robust Neural Adaptive Control of a Hypersonic Aircraft. In AIAA guidance, navigation, and control conference and exhibit. Austin, Texas. Xu, 2014, Discrete-time hypersonic flight control based on extreme learning machine, Neurocomputing, 128, 232, 10.1016/j.neucom.2013.02.049 Xu, 2019, Barrier Lyapunov function based learning control of hypersonic flight vehicle with AOA constraint and actuator faults, IEEE Transactions on Cybernetics, 49, 1047, 10.1109/TCYB.2018.2794972 Xu, 2014, Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form, IEEE Transactions on Cybernetics, 44, 2626, 10.1109/TCYB.2014.2311824 Xu, 2013, Neural control of hypersonic flight vehicle model via time-scale decomposition with throttle setting constraint, Nonlinear Dynamics, 73, 1849, 10.1007/s11071-013-0908-6 Xu, 2011, Adaptive discrete-time controller design with neural network for hypersonic flight vehicle via back-stepping, International Journal of Control, 84, 1543, 10.1080/00207179.2011.615866 Xu, 2021, Robust intelligent control of SISO nonlinear systems using switching mechanism, IEEE Transactions on Cybernetics, 51, 3975, 10.1109/TCYB.2020.2982201 Xu, 2021, Robust adaptive neural control of nonminimum phase hypersonic vehicle model, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 51, 1107, 10.1109/TSMC.2019.2894916 Xu, 2012, Direct neural discrete control of hypersonic flight vehicle, Nonlinear Dynamics, 70, 269, 10.1007/s11071-012-0451-x Xu, 2013, Direct neural control of hypersonic flight vehicles with prediction model in discrete time, Neurocomputing, 115, 39, 10.1016/j.neucom.2012.12.028 Xu, 2017, DOB-Based neural control of flexible hypersonic flight vehicle considering wind effects, IEEE Transactions on Industrial Electronics, 64, 8676, 10.1109/TIE.2017.2703678 Xu, 2015, Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle, IEEE Transactions on Neural Networks and Learning Systems, 26, 2563, 10.1109/TNNLS.2015.2456972 Xu, 2018, Online recorded data-based composite neural control of strict-feedback systems with application to hypersonic flight dynamics, IEEE Transactions on Neural Networks and Learning Systems, 29, 3839, 10.1109/TNNLS.2017.2743784 Xu, 2015, Neural discrete back-stepping control of hypersonic flight vehicle with equivalent prediction model, Neurocomputing, 154, 337, 10.1016/j.neucom.2014.11.059 Xu, 2016, Neural network based dynamic surface control of hypersonic flight dynamics using small-gain theorem, Neurocomputing, 173, 690, 10.1016/j.neucom.2015.08.017 Xue, 2021, Event-triggered adaptive dynamic programming for unmatched uncertain nonlinear continuous-time systems, IEEE Transactions on Neural Networks and Learning Systems, 32, 2939, 10.1109/TNNLS.2020.3009015 Yan, 2012, Model predictive control of nonlinear systems with unmodeled dynamics based on feedforward and recurrent neural networks, IEEE Transactions on Industrial Informatics, 8, 746, 10.1109/TII.2012.2205582 Yan, 2014, Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks, IEEE Transactions on Neural Networks and Learning Systems, 25, 457, 10.1109/TNNLS.2013.2275948 Yang, 2004, A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 34, 406, 10.1109/TSMCA.2004.824870 Yang, 2006, Robust adaptive neural network control for strict-feedback nonlinear systems via small-gain approaches Yang, 2003, Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems, Applied Soft Computing, 3, 305, 10.1016/j.asoc.2003.05.001 Yu, 2018, Fault-tolerant aircraft control based on self-constructing fuzzy neural networks and multivariable SMC under actuator faults, IEEE Transactions on Fuzzy Systems, 26, 2324, 10.1109/TFUZZ.2017.2773422 Yu, 2019, Impact of random weights on nonlinear system identification using convolutional neural networks, Information Sciences, 477, 1, 10.1016/j.ins.2018.10.019 Yu, 2020, Decentralized fractional-order backstepping fault-tolerant control of multi-UAVs against actuator faults and wind effects, Aerospace Science and Technology, 104 Yu, 2020, Distributed adaptive fault-tolerant close formation flight control of multiple trailing fixed-wing UAVs, ISA Transactions, 106, 181, 10.1016/j.isatra.2020.07.005 Yuan, 2020, Barrier Lyapunov functions-based adaptive fault tolerant control for flexible hypersonic flight vehicles with full state constraints, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 50, 3391, 10.1109/TSMC.2018.2837378 Zeghlache, 2018, Actuator fault tolerant control using adaptive RBFNN fuzzy sliding mode controller for coaxial octorotor UAV, ISA Transactions, 80, 267, 10.1016/j.isatra.2018.06.003 Zhang, 2004, Nonlinear system identification with output error model through stabilized simulation, IFAC Proceedings Volumes, 37, 501, 10.1016/S1474-6670(17)31273-9 Zhang, 1999, Design and performance analysis of a direct adaptive controller for nonlinear systems, Automatica, 35, 1809, 10.1016/S0005-1098(99)00098-9 Zhang, 2016, Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search, 528 Zhang, 2018, Learning-based energy-efficient data collection by unmanned vehicles in smart cities, IEEE Transactions on Industrial Informatics, 14, 1666, 10.1109/TII.2017.2783439 Zhang, 2018, A varying-parameter convergent neural dynamic controller of multirotor UAVs for tracking time-varying tasks, IEEE Transactions on Vehicular Technology, 67, 4793, 10.1109/TVT.2018.2802909 Zhao, 2012, Online sequential extreme learning machine with forgetting mechanism, Neurocomputing, 87, 79, 10.1016/j.neucom.2012.02.003 Zheng, 2020, Improved adaptive NN backstepping control design for a perturbed pvtol aircraft, Neurocomputing, 410, 51, 10.1016/j.neucom.2020.05.065 Zhou, 2015, Dynamic surface control based on neural network for an air-breathing hypersonic vehicle, Optimal Control Applications and Methods, 36, 774, 10.1002/oca.2130 Zhu, 2015, Adaptive suboptimal output-feedback control for linear systems using integral reinforcement learning, IEEE Transactions on Control Systems Technology, 23, 264, 10.1109/TCST.2014.2322778 Zhu, 2011, Attitude stabilization of rigid spacecraft with finite-time convergence, International Journal of Robust and Nonlinear Control, 21, 686, 10.1002/rnc.1624 Zong, 2014, Robust adaptive dynamic surface control design for a flexible air-breathing hypersonic vehicle with input constraints and uncertainty, Nonlinear Dynamics, 78, 289, 10.1007/s11071-014-1440-z Zou, 2015, A robust adaptive RBFNN augmenting backstepping control approach for a model-scaled helicopter, IEEE Transactions on Control Systems Technology, 23, 2344, 10.1109/TCST.2015.2396851 Zuo, 2014, Adaptive trajectory tracking control of output constrained multi-rotors systems, IET Control Theory & Applications, 8, 1163, 10.1049/iet-cta.2013.0949