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Experimental verification of H∞ control with examples of the movement of a wheeled robot

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EN
Abstrakty
EN
The paper presents the results of experimental verification on using a zero-sum differential game and H∞ control in the problems of tracking and stabilizing motion of a wheeled mobile robot (WMR). It is a new approach to the synthesis of input-output systems based on the theory of dissipative systems in the sense of the possibility of their practical application. This paper expands upon the problem of optimal control of a nonlinear, nonholonomic wheeled mobile robot by including the reduced impact of changing operating condtions and possible disturbances of the robot’s complex motion. The proposed approach is based on the H∞ control theory and the control is generated by the neural approximation solution to the Hamilton-Jacobi-Isaacs equation. Our verification experiments confirm that the H∞ condition is met for reduced impact of disturbances in the task of tracking and stabilizing the robot motion in the form of changing operating conditions and other disturbances, which made it possible to achieve high accuracy of motion.
Rocznik
Strony
art. no. e139390
Opis fizyczny
Bibliogr. 42 poz., fot., wykr., tab.
Twórcy
  • Department of Applied Mechanics and Robotics, Faculty of Mechanical Engineering and Aeronautics, Rzeszów University of Technology, ul. Powstańców Warszawy 12, 35-959 Rzeszów, Poland
autor
  • Department of Applied Mechanics and Robotics, Faculty of Mechanical Engineering and Aeronautics, Rzeszów University of Technology, ul. Powstańców Warszawy 12, 35-959 Rzeszów, Poland
Bibliografia
  • [1] B. Kovács, G. Szayer, F. Tajti, M. Burdelis, and P. Korondi, “A novel potential field method for path planning of mobile robots by adapting animal motion attributes,” Rob. Auton. Syst., vol. 82, pp. 24–34, 2016, doi: 10.1016/j.robot.2016.04.007.
  • [2] A. Pandey, “Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review,” Int. Robotics Autom. J., vol. 2, no. 3, pp. 96–105, 2017, doi: 10.15406/iratj.2017.02.00023.
  • [3] R.C. Arkin, Behavior-based robotics. The MIT Press, 1998.
  • [4] M. Szuster and Z. Hendzel, Intelligent Optimal Adaptive Control for Mechatronic Systems. Springer, 2018.
  • [5] M.J. Giergiel, Z. Hendzel, and W. ˙ Zylski, Modeling and control of mobile wheeled robots. PWN, 2013, [in Polish].
  • [6] P. Bozek, Y.L. Karavaev, A.A. Ardentov and K.S. Yefremov, „Neural network control of a wheeled mobile robot based on optimal trajectories,” Int. J. Adv. Rob. Syst., vol. 17, no. 2, pp. 1–10, 2020, doi: 10.1177/1729881420916077
  • [7] P. Gierlak and Z. Hendzel, Control of wheeled and manipulation robots. Publishing House Rzeszow Univ. of Technology, 2011, [in Polish].
  • [8] B. Kiumarsi, K.G. Vamvoudakis, H. Modares, and F.L. Lewis, “Optimal and Autonomous Control Using Reinforcement Learning: A Survey,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 6, pp. 2042–2062, 2018.
  • [9] F.L. Lewis, D. Vrabie, and V.L. Syrmos, Optimal control. John Wiley & Sons, 2012.
  • [10] K.G. Vamvoudakis and F.L. Lewis, “Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem,” Automatica, vol. 46, no. 5, pp. 878.
  • [11] F.Y. Wang, H. Zhang, and D. Liu, Adaptive Dynamic Programming: An Introduction,” IEEE Comput. Intell. Mag., vol. 4, no. May, pp. 39-47, 2009.
  • [12] A.G. Barto, W. Powell, J. Si, and D.C. Wunsch, Handbook of learning and approximate dynamik programming. Wiley-IEEE Press, 2004.
  • [13] D. Liu, Q. Wei, D. Wang, X. Yang, and H. Li, Adaptive Dynamic Programming with Applications in Optimal Control. Springer, Advances in Industrial Control, 2017.
  • [14] A.J. van der Schaft, L-2 Gain and Passivity Techniques in Non-linear Control, Springer International Publishing, 2007.
  • [15] B. Brogliato, R. Lozano, B. Maschke, and O. Egeland, Dissipative Systems Analysis and Control, Springer-Verlag London, 2007.
  • [16] A.W. Starr and Y.C. Ho, “Nonzero-sum differential games,” J. Optim Theory Appl., vol. 3, no. 3, pp. 184-206, 1969.
  • [17] M. Abu-Khalaf, J. Huang, and F.L. Lewis, Nonlinear H2 Hinf Constrained Feedbacka Control, Springer-Verlag London, 2006.
  • [18] D. Liu, H. Li, and D. Wang, „Neural-network-based zero-sum game for discrete-time non linear systems via iterative adaptive dynamic programming algorithm,” Neurocomputing, vol. 110, pp. 92-100, 2013.
  • [19] C. Qin, H. Zhang, Y. Wang, Y. Luo, „Neural network-based online Hinf control for discrete-time nonlinear systems via iterative adaptive dynamic programming algorithm,” Neurocomputing, vol. 110, pp. 92-100, 2013.
  • [20] D. Liu, H. Li, and D. Wang, „Hinf control of unknown discrete-time nonlinear systems with control constraints using adaptive dynamic programming,” in the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, 2012, pp. 1-6.
  • [21] Z. Hendzel and P. Penar, “Zero-Sum Differential Game in Wheeled Mobile Robot Control, Int. Conf. Mechatron., vol. 934, pp. 151-161, 2017.
  • [22] Z. Hendzel, „Optimality in Control for Wheeled Robot,” Adv. Intell. Syst. Comput. Autom. 2018, vol. 743, pp. 431-440, 2018.
  • [23] Y. Fu and T. Chai, „Online solution of two player zero-sum game for continuous-time nonlinear systems with completely unknown dynamics,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 12, pp. 2577-2587, 2015.
  • [24] K.G. Vamvoudakis and F.L. Lewis, „Online solution of nonlinear two-player zero-sum games using synchronous policy iteration,” Int. J. Robust. Nonlinear Control, vol. 22, pp. 1460-1483, 2012.
  • [25] S. Yasini, A. Karimpour, M.-B. Naghibi Sisteni, and H. Modares, „Online concurrent reinforcement learning algorithm to solve two-player zero-sum games for partially unknown nonlinear continuous-time systems,” Int. J. Adapt. Control Signal Process., vol. 29, no. 4, pp. 473-493, 2015.
  • [26] B. Luo, H.-N. Wu, and T. Huang, „Off-policy reinforcement learning for Hinf control design,” IEEE Trans. Cybern., vol. 45, no. 1, pp. 65-76, 2014.
  • [27] H.-N. Wu and B. Luo, „Neural Network Based Online Simulationeous Policy Update Algoritm for Dolving the HJI Equation in Nonlinear Hinf Control,” IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 12, pp. 1884/1895, 2012.
  • [28] Y. Zhu, D. Zhao, and X. Li, „Iterative adaptive dynamic programming for solvimg unknown nonlinear zer-sum game based on online data,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 3, pp. 714-725, 2016.
  • [29] J. Zhao, M. Gan, and C. Zhang, “Event-triggered Hinf optimal control for continuous-time nonlinear systems using neurodynamic programming,” Neurocomputing, vol. 360, pp. 14–24, 2019.
  • [30] B. Dong, T. An, F. Zhou, S. Wang, Y. Jiang, K. Liu, F. Liu, H. Lu, and Y. Li, “Decentralized Robust Optimal Control for Modular Robot Manipulators Based on Zero-Sum Game with ADP,” in International Symposium on Neural Networks. Springer, 2019, pp. 3–14.
  • [31] H. Modares, F.L. Lewis, and Z.-P. Jiang, “Hinf Tracking Control of Completely Unknown Continuous-Time Systems via Off-Policy Reinforcement Learning,” IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 10, pp. 2550–2562, 2015.
  • [32] J.C. Willems, “Dissipative Dynamical Systems. Part I: General Theory,” Arch. Ration. Mech. Anal., vol. 45, pp. 321–351, 1972.
  • [33] D.J. Hill and P.J. Moylan, “Dissipative Dynamical Systems: Basic Input-Output and State Properties,” J. Franklin Inst., vol. 305, no. 5, pp. 327–357, 1980.
  • [34] A.J. van der Schaft, “L2-gain Analysis of Nonlinear Systems and Nonlinear State Feedback Hinf Control,” IEEE Trans. Autom. Control, vol. 37, no. 6, pp. 770–784, 1992.
  • [35] S. Boyd, L.E. Ghaoui, E. Feron, and V. Balakrishnam, Linear Matrix Inequalities in System and Control Theory. SIAM studies in applied mathematics: 15, 1994.
  • [36] S. Yasini, M.B.N. Sistani, and A. Karimpour, “Approximate dynamic programming for two-player zero-sum game related to Hinf control of unknown nonlinear continuous-time systems,” Int. J. Control Autom. Syst., vol. 13, no. 1, pp. 99–109, 2014.
  • [37] W. Zylski, Kinematics and dynamics of mobile wheeled robots. Publishing House Rzeszow Univ. of Technology, 1996, [in Polish].
  • [38] J. Giergiel and W. Żylski, “Description of motion of a mobile robot by Maggie’s equations,” J. Theor. Appl. Mech., vol. 43, no. 3, pp. 511–521, 2005.
  • [39] J. Garca De Jaln, A. Callejo, and A.F. Hidalgo, “Efficient solution of Maggi’s equations,” J. Comput. Nonlinear Dyn., vol. 7, no. 2, 2012, doi: 10.1115/1.4005238.
  • [40] A. Kurdila, J.G. Papastavridis, and M.P. Kamat, “Role of Maggi’s equations in computational methods for constrained multibody systems,” J. Guidance Control Dyn., vol. 13, no. 1, pp. 113–120, 1990, doi: 10.2514/3.20524.
  • [41] DS1103, Hardware Installation and Configuration. dSpace, 2009.
  • [42] ActiveMedia, Pioneer 2DX Operation Manual Peterborough, 1999.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c71257ad-ab42-4135-80bd-a5ee38b881f6
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