Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 3
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

In this paper we propose a sensor-based navigation method for navigation of wheeled mobile robot, based on the Kohonen self-organising map (SOM). We discuss a sensor-based approach to path design and control of wheeled mobile robot in an unknown 2-D environment with static obstacles. A strategy of reactive navigation is developed including two main behaviours: a reaching the middle of a collision-free space behaviour, and a goal-seeking behaviour. Each low-level behaviour has been designed at design stage and then fused to determine a proper actions acting on the environment at running stage. The combiner can fuse low-level behaviours so that the mobile robot can go for the goal position without colliding with obstacles one for the convex obstacles and one for the concave ones. The combiner is a softswitch, based on the idea of artificial potential fields, that chooses more then one action to be active with diRerent degrees at each time step. The output of the navigation level is fed into a neural tracking controller that takes into account the dynamics of the mobile robot. The purpose of the neural controller is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. Computer simulation has been conducted to illustrate the performance of the proposed solution by a series of experiments on the emulator of wheeled mobile robot Pioneer-2DX.

Go to article

Authors and Affiliations

Z. Hendzel
Download PDF Download RIS Download Bibtex

Abstract

In this work, a novel approach to designing an on-line tracking controller for a nonholonomic wheeled mobile robot (WMR) is presented. The controller consists of nonlinear neural feedback compensator, PD control law and supervisory element, which assure stability of the system. Neural network for feedback compensation is learned through approximate dynamic programming (ADP). To obtain stability in the learning phase and robustness in face of disturbances, an additional control signal derived from Lyapunov stability theorem based on the variable structure systems theory is provided. Verification of the proposed control algorithm was realized on a wheeled mobile robot Pioneer–2DX, and confirmed the assumed behavior of the control system.

Go to article

Authors and Affiliations

Zenon Hendzel
Marcin Szuster
Download PDF Download RIS Download Bibtex

Abstract

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.
Go to article

Bibliography

  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. Żylski, 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 tra- jectories,” 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. 878–888, 2010.
  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 dynamic 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, L2-Gain and Passivity Techniques in Nonlinear Control. Springer International Publishing, 2017.
  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 nonlinear systems via iterative adaptive dynamic programming algorithm,” Neurocomputing, vol. 110, pp.  92–100, 2013.
  19.  C. Qin, H. Zhang, Y. Wang, and Y. Luo, “Neural network-based online Hinf control for discrete-time affine nonlinear system using adaptive dynamic programming,” Neurocomputing, vol. 198, pp.  91–99, 2016.
  20.  D. Liu, H. Li, and D. Wang, “Hinf control of unknown discretetime 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 games 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. Robust. Nonlinear Control, vol. 22, pp. 1460–1483, 2012.
  25.  S. Yasini, A. Karimpour, M.-B. Naghibi Sistani, 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 Simultaneous Policy Update Algorithm for Solving 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 solving unknown nonlinear zero-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 pro- gramming,” 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.
Go to article

Authors and Affiliations

Zenon Hendzel
1
ORCID: ORCID
Paweł Penar
1

  1. Department of Applied Mechanics and Robotics, Faculty of Mechanical Engineering and Aeronautics, Rzeszów University of Technology, ul. Powstańców Warszawy 12, 35-959 Rzeszów, Poland

This page uses 'cookies'. Learn more