A Nature Inspired Parameter Tuning Approach to Cascade Control for Hydraulically Driven Parallel Robot Platform
Tóm tắt
This paper presents the optimal tuning of cascade load force controllers for a parallel robot platform. A parameter search for the proposed cascade controller is difficult because there is no methodology to set the parameters and the search space is broad. The proposed parameter search scheme is based on a bat algorithm, which attracts a lot of attention in the evolutionary computation area due to the empirical evidence of its superiority in solving various nonconvex problems. The control design problem is formulated as an optimization problem under constraints. Typical constraints, such as mechanical limits on positions and maximal velocities of hydraulic actuators as well as on servo-valve positions, are included in the proposed algorithm. The simulation results indicate that the proposed optimal tuned cascade control is effective and efficient. These results clearly demonstrate that applied techniques exhibit a significant performance improvement over classical tuning methods.
Tài liệu tham khảo
Stewart, D.: A platform with six degrees of freedom. Proc. Inst. Mech. Eng. 180(15), 371–386 (1965)
Filipovic, V., Nedic, N., Stojanovic, V.: Robust identification of pneumatic servo actuators in the real situations. Forschung im Ingenieurwesen (Engineering Research) 75(4), 183–196 (2011)
Dihovicni, Dj, Nedic, N.: Simulation, animation and program support for a high performance pneumatic force actuator system. Math. Comput. Model. 48(5–6), 761–768 (2008)
Heintze, J., Teerhuis, P.C., Van der Weiden, A.J.J.: Controlled hydraulics for a direct drive brick laying robot. Autom. Constr. 5(1), 23–29 (1996)
Sepehri, N., Dumont, G.A.M., Lawrence, P.D., Sassani, F.: Cascade control of hydraulically actuated manipulators. Robotica 8(3), 207–216 (1990)
Filipovic, V., Nedic, N.: PID Regulators. Faculty of Mechanical Engineering, Kraljevo (2008)
Guo, H.B., Liu, Y.G., Liu, G.R., Li, H.R.: Cascade control of a hydraulically driven 6-DOF parallel robot manipulator based on a sliding mode. Control Eng. Pract. 16(9), 1055–1068 (2008)
Rao, S.S.: Engineering Optimization: Theory and Practice. Wiley, New York (2009)
Doncieux, S., Bredeche, N., Mouret, J.B.: New Horizons in Evolutionary Robotics: Extended Contributions from the 2009 EvoDeRob Workshop (Studies in Computational Intelligence). Springer, Berlin (2011)
Talbi, E.G.: Metaheuristics: From Design to Implementation. Wiley, New Jersey (2009)
Niknam, T., Farsani, E.A.: A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration. Eng. Appl. Artif. Intell. 23(8), 1340–1349 (2010)
Recioui, A.: Sidelobe level reduction in linear array pattern synthesis using particle swarm optimization. J. Optim. Theory Appl. 153, 497–512 (2012)
Zheng, Y.L., Nie, S.L., Ji, H., Hu, Z.: Application of a fuzzy programming through stochastic particle swarm optimization to assessment of filter management strategies in fluid power system under uncertainty. J. Optim. Theory Appl. 157, 276–286 (2013)
Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, New York (2010b)
Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)
Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)
Yang, X.S., Deb, S.: Cuckoo search via Levy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing. IEEE Publications, USA, pp. 210–214 (2009)
Yang, X.S., Deb, S.: Engineering optimisation by Cuckoo Search. Int. J. Math. Model. Numer. Optim. 2(4), 330–343 (2010)
Yang, X.S.: A New Metaheuristic Bat-Inspired Algorithm, Nature Inspired Cooperative Strategies for Optimization, Studies in Computational Intelligence, vol. 284, pp. 65–74. Springer, Berlin (2010)
Glattfelder, A.H., Schaufelberger, W.: Control Systems with Input and Output Constraints (Advanced Textbooks in Control and Signal Processing). Springer, London (2013)
Maruta, I., Kim, T.H., Sugie, T.: Fixed-structure H\(\infty \) controller synthesis: a meta-heuristic approach using simple constrained particle swarm optimization. Automatica 45(2), 553–559 (2009)
Kim, I., Maruta, T.H., Sugie, T.: A simple and efficient constrained particle swarm optimization and its application to engineering design problems. Proc. Inst. Mech Eng. Part C 224(2), 389–400 (2010)
Yang, X.S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 464–483 (2012)
Gandomi, A.H., Yang, X.S., Alavi, A.H.: Mixed variable structural optimization using firefly algorithm. Comput. Struct. 89(23–24), 2325–2336 (2011)
Yang, X.S.: Nature-Inspired Optimization Algorithms. Elsevier, Amsterdam (2014)
Jelali, M., Kroll, A.: Hydraulic Servo-systems. Springer, Berlin (2002)
Cominos, P., Munro, N.: PID controllers: recent tuning methods and design to specification. IEEE Proc. Control Theory Appl. 149(1), 46–53 (2002)