Abstract
In this paper, cooperative particle swarm optimization (CPSO)-based model predictive control (MPC) scheme is proposed to deal with the formation control problem of multiple nonholonomic mobile robots. In distributed MPC framework, control input of each robot needs to be optimized over a finite prediction interval considering control inputs of the other robots, where the objective function is coupled by the state variables of the neighboring robots. To solve the optimization problem on a prediction interval, we present a modified CPSO algorithm which finds a Nash equilibrium between multiple robots. Simulations are performed on a group of nonholonomic mobile robots to demonstrate the effectiveness of the proposed MPC scheme incorporating CPSO.
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Keywords
- Particle Swarm Optimization
- Nash Equilibrium
- Particle Swarm
- Model Predictive Control
- Nash Equilibrium Strategy
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References
Onnen, C., Babuska, R., Kaymak, U., Sousa, J.M., Verbruggen, H.B., Iserman, R.: Genetic algorithms for optimization in predictive control. Contr. Eng. Practice 5(10), 1363–1372 (1997)
Fravolini, M.L., Ficola, A., Cava, M.L.: Real-time evolutionary algorithms for constrained predictive control: Frontiers in Evolutionary Robotics, pp. 139–184. InTech (2008)
Martinez, M., Senent, J.S., Blasco, X.: Generalized predictive control using genetic algorithms. Eng. Appl. Artif. Intell. 11(3), 355–367 (1997)
Shin, S.C., Park, S.B.: GA based predictive control for nonlinear processes. Electron. Lett. 34(20), 1980–1981 (1998)
Newman, A.J., Martin, S.R., DeSena, J.T., Clarke, J.C., McDerment, J.W., Preissler, W.O., Peterson, C.K.: Receding horizon controller using particle swarm optimization for closed-loop ground target surveillance and tracking. In: Proc. SPIE 2009, vol. 7336 (2009)
Yousuf, M.S.: Nonlinear predictive control using particle swarm optimization: application to power systems. VDM Verlag Dr. Müller (2010)
Mercieca, J., Fabri, S.G.: Particle swarm optimization for nonlinear model predictive control. In: Proc. ADVCOMP, pp. 88–93 (2011)
Sedraoui, M., Abdelmalek, S.: Multivariable generalized predictive control using an improved particle swarm optimization algorithm. Informatica 35(3), 363–374 (2011)
van den Bergh, F., Engelbrecht, A.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)
Li, X., Yao, X.: Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms. In: Proc. IEEE CEC, pp. 1546–1553 (2009)
Li, X., Yao, X.: Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans. Evol. Comput. 16(2), 210–224 (2012)
Sefrioui, M., Periaux, J.: Nash genetic algorithms: examples and applications. In: Proc. IEEE CEC, pp. 509–516 (2000)
Gu, D.: A differential game approach to formation control. IEEE Trans. Control Syst. Technol. 16(1), 85–93 (2008)
Dong, W.: Flocking of multiple mobile robots based on backstepping. IEEE Trans. Syst. Man Cybern. Part B-Cybern. 41(2), 414–424 (2011)
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Lee, SM., Myung, H. (2013). Cooperative Particle Swarm Optimization-Based Predictive Controller for Multi-robot Formation. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33932-5_49
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DOI: https://doi.org/10.1007/978-3-642-33932-5_49
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