Chiến Lược Phân Tích Độ Ổn Định Cho Hệ Thống Điều Khiển Neural Thích Ứng: Xác Thực Thực Tiễn Qua Một Bộ Phản Ứng Transester hóa

Springer Science and Business Media LLC - Tập 45 - Trang 1395-1409 - 2021
Yassin Farhat1, Fatma Ezzahra Rhili1, Asma Atig1, Ali Zribi1, Ridha Ben Abdennour1
1National Engineering School of Gabes, Gabes, Tunisia

Tóm tắt

Trong bài báo này, một chiến lược phân tích độ ổn định cho các hệ thống điều khiển phi tuyến được đề xuất. Một sơ đồ điều khiển neural thích ứng bao gồm một bộ giả lập và một bộ điều khiển với các tỷ lệ thích ứng tách biệt được xem xét. Một hàm Lyapunov dựa trên động lực theo dõi lỗi được duy trì và một kỹ thuật điều chỉnh trực tuyến tỷ lệ thích ứng của bộ điều khiển neural được áp dụng để cải thiện hiệu suất vòng kín về độ ổn định, tốc độ và độ chính xác. Các nghiên cứu so sánh và xác thực thực nghiệm trên một bộ phản ứng bán liên tục được thực hiện để chứng minh tính hiệu quả của chiến lược đã phát triển.

Từ khóa

#độ ổn định #hệ thống điều khiển phi tuyến #điều khiển neural thích ứng #hàm Lyapunov #phản ứng transester hóa

Tài liệu tham khảo

Atig A, Druaux F, Lefebvre D, Abderrahim K, BEN Abdennour R (2010) new neural adaptive control based on neural emulation of complex square MIMO systems. Int Rev Autom Control 3(6):612–623 Atig A, Druaux F, Lefebvre D, Abderrahim K, BEN Abdennour R (2012) Adaptive control design using stability analysis and tracking errors dynamics for nonlinear square MIMO systems. Eng Appl Artif Intell 25(7):1450–1459 Atig A, Druaux F, Lefebvre D, Abderrahim K, BEN Abdennour R (2012) On lyapunov stability of nonlinear adaptive control based on neural networks emulator and controller. In: 2012 \(20^{th}\) Mediterranean conference on control & automation (MED), IEEE, pp 272–277 Bahri N, Atig A, BEN Abdennour R, Druaux F, Lefebvre D (2012) Multimodel and neural emulators for non-linear systems: application to an indirect adaptive neural control. Int J Model Ident Control 17(4):348–359 Druaux F, Leclercq E, Lefebvre D (2004) Adaptive neural network control for uncertain or unknown non linear systems. In: Proceedings of IEEE MMAR, Poland, 2004. IEEE, New York, pp 1309–1314 Emaletdinova L, Kabirova A (2018) Development of neural network model of regulator for automatic control system of technical object in absence of mathematical model of object. In: 2018 international conference on industrial engineering, applications and manufacturing (ICIEAM) (2018), IEEE, pp 1–5 Fei J, Wang H (2020) Recurrent neural network fractional-order sliding mode control of dynamic systems. J Franklin Inst 357(8):4574–4591 Hassanpour H, Corbett B, Mhaskar P (2020) Integrating dynamic neural network models with principal component analysis for adaptive model predictive control. Chem Eng Res Des 161:26–37 He W, Chen Y, Yin Z (2015) Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans Cybern 46(3):620–629 Jiang K, Wang X, Niu B, Wang Z, Li J, Duan P, Yang D (2020) Finite-time adaptive neural control and almost disturbance decoupling for disturbed MIMO non-strict-feedback nonlinear systems. J Franklin Inst 357(16):11750–11772 Jurado F, Caño A, Ortega M (2003) Neural networks and fuzzy logic in electrical engineering control courses. Int J Electr Eng Educ 40(1):1–12 Khromushin V, Vasiliy P, Eskov V, Ilyashenko L, Vokhmina Y (2019) New principles in the operation of neural emulators in medical diagnosis. Biomed Eng 53(2):117–120 Kulawski GJ, Brdyś MA (2000) Stable adaptive control with recurrent networks. Automatica 36(1):5–22 Li D-P, Liu Y-J, Tong S, Chen CP, Li D-J (2018) Neural networks-based adaptive control for nonlinear state constrained systems with input delay. IEEE Trans Cybern 49(4):1249–1258 Liu D, Liu Z, Chen CP, Zhang Y (2020) Distributed adaptive neural control for uncertain multi-agent systems with unknown actuator failures and unknown dead zones. Nonlinear Dyn 99(2):1001–1017 Liu L, Li X, Liu Y-J, Tong S (2021) Neural network based adaptive event trigger control for a class of electromagnetic suspension systems. Control Eng Practice 106:104675 Luo C, Lei H, Li J, Zhou C (2020) A new adaptive neural control scheme for hypersonic vehicle with actuators multiple constraints. Nonlinear Dyn 100:3529–3553 Ma F, Hanna MA (1999) Biodiesel production: a review. Bioresour Technol 70(1):1–15 Maulik R, Egele R, Lusch B, Balaprakash P (2020) Recurrent neural network architecture search for geophysical emulation. arXiv preprint arXiv:2004.10928 Pan J, Pottimurthy Y, Wang D, Hwang S, Patil S, Fan L-S (2020) Recurrent neural network based detection of faults caused by particle attrition in chemical looping systems. Powder Technol 367:266–276 Patan K, Patan M (2020) Neural-network-based iterative learning control of nonlinear systems. ISA Trans 98:445–453 Patre PM, Bhasin S, Wilcox ZD, Dixon WE (2010) Composite adaptation for neural network-based controllers. IEEE Trans Autom Control 55(4):944–950 Pirasteh-Moghadam M, Gh Saryazdi M, Loghman E, Kamali E, Bakhtiari-Nejad F (2020) Development of neural fractional order PID controller with emulator. ISA Trans 106:293–302 Razzaghian A, Kardehi Moghaddam R, Pariz N (2020) Adaptive neural network conformable fractional-order nonsingular terminal sliding mode control for a class of second-order nonlinear systems. IETE J Res. https://doi.org/10.1080/03772063.2020.1791743 Rhili FE, Atig A, BEN Abdennour R (2018) A new strategy for neural emulator learning rate tuning. In: 2018 \(15^{th}\) international multi-conference on systems, signals & devices (SSD), IEEE, pp 952–957 Rhili FE, Atig A, Abdennour R (2019a) Fuzzy adapting rate for a neural emulator of nonlinear systems: real application on a chemical process. Trans Inst Measure Control 41(8):2214–2222 Rhili FE, Atig A, BEN Abdennour R (2019b) Fuzzy supervisor for neural emulation of MIMO nonlinear processes. In: 2019 \({19^{th}}\) international conference on sciences and techniques of automatic control and computer engineering (STA) , IEEE, pp 298–302 Rovithakis GA (1999) Robust neural adaptive stabilization of unknown systems with measurement noise. IEEE Trans Syst, Man, Cybern, Part B (Cybernetics) 29(3):453–459 Shen Q, Shi P, Agarwal RK, Shi Y (2020) Adaptive neural network-based filter design for nonlinear systems with multiple constraints. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.3009391 Wang H, Liu S, Yang X (2020) Adaptive neural control for non-strict-feedback nonlinear systems with input delay. Inf Sci 514:605–616 Wang Q, Dai W, Ma X, Yang C (2017) Multiple models and neural networks based adaptive PID decoupling control of mine main fan switchover system. IET Control Theory Appl 12(4):446–455 Williams RJ (1990) Adaptive state representation and estimation using recurrent connectionist networks. In: Neural networks for control, vol. 4. MIT Press, Cambridge, MA, pp 97–114 Wright A, Damskägg E-P, Välimäki V, et al (2019) Real-time black-box modelling with recurrent neural networks. In: 22nd international conference on digital audio effects (DAFx-19) Wu Z, Rincon D, Christofides P (2020) Process structure-based recurrent neural network modeling for model predictive control of nonlinear processes. J Process Control 89:74–84 Xin L-P, Yu B, Zhao L, Yu J (2020) Adaptive fuzzy backstepping control for a two continuous stirred tank reactors process based on dynamic surface control approach. Appl Math Comput 377:125–138 Zerkaoui S (2007) Commande neuronale adaptative des systemes non linéaires. PhD thesis, Le Havre Zhang K, Li Y, Yin Y, Zhang S, Dong J (2018) Multiple-neural-networks-based adaptive control for bilateral teleoperation systems with time-varying delays. In: 2018 \(37^{th}\) Chinese control conference (CCC) , IEEE, pp 543–548 Zhang T, Wang M, Xia M, Yang Y (2020) Observer-based decentralized adaptive neural control for uncertain interconnected systems with input quantization and time-varying output constraints. Int J Robust Nonlinear Control 30(13):4979–5003 Zhu Q, Liu Y, Wen G (2020) Adaptive neural network output feedback control for stochastic nonlinear systems with full state constraints. ISA Trans 101:60–68