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A Discrete-Time Least-Squares Adaptive State Tracking Control Scheme with A Mobile-Robot System Study (2403.17235v1)

Published 25 Mar 2024 in eess.SY and cs.SY

Abstract: This paper develops an adaptive state tracking control scheme for discrete-time systems, using the least-squares algorithm, as the new solution to the long-standing discrete-time adaptive state tracking control problem to which the Lyapunov method (well-developed for the continuous-time adaptive state tracking problem) is not applicable. The new adaptive state tracking scheme is based on a recently-developed new discrete-time error model which has been used for gradient algorithm based state tracking control schemes, and uses the least-squares algorithm for parameter adaptation. The new least-squares algorithm is derived to minimize an accumulative estimation error, to ensure certain optimality for parameter estimation. The system stability and output tracking properties are studied. Technical results are presented in terms of plant-model matching, error model, adaptive law, optimality formulation, and stability and tracking analysis. The developed adaptive control scheme is applied to a discrete-time multiple mobile robot system to meet an adaptive state tracking objective. In addition, a collision avoidance mechanism is proposed to prevent collisions in the whole tracking process. Simulation results are presented, which verify the desired system state tracking properties under the developed least-squares algorithm based adaptive control scheme.

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References (24)
  1. R. Almadhoun, T. Taha, L. Seneviratne, and Y. Zweiri, “A survey on multi-mobile-robot coverage path planning for model reconstruction and mapping,” SN Applied Sciences, vol. 1, no. 8, pp. 1-24, July 2019.
  2. N. Bezzo, P. J. Cruz, F. Sorrentino, and R. Fierro, “Decentralized identification and control of networks of coupled mobile platforms through adaptive synchronization of chaos,” Physica D: Nonlinear Phenomena, vol. 267, pp. 94–103, January 2014.
  3. X. Chen, T. Fukuda and K. D. Young, “Adaptive quasi-sliding-mode tracking control for discrete uncertain input-output systems,” IEEE Transactions on Industrial Electronics, vol. 48, no. 1, pp. 216-224, February 2001.
  4. T. Fong, C. Thorpe and C. Baur, “Multi-mobile-robot remote driving with collaborative control,” IEEE Transactions on Industrial Electronics, vol. 50, no. 4, pp. 699-704, August 2003.
  5. F. Gao, A. Clare, J. Macbeth, and M. Cummings, “Modeling the impact of operator trust on performance in multiple robot control,” Proceedings of AAAI Spring Symposium: Trust and Autonomous Systems, Stanford, CA, March 2013.
  6. W. Hong and G. Tao, “An adaptive control scheme for three-phase grid-connected inverters in photovoltaic power generation systems,” Proceedings of 2018 Annual American Control Conference, pp. 899-904, Milwaukee, WI, 2018.
  7. O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” Proceedings. Proceedings of 1985 IEEE International Conference on Robotics and Automation, pp. 500-505, St. Louis, MO, 1985.
  8. Y. Koren and J. Borenstein, “Potential field methods and their inherent limitations for mobile robot navigation,” Proceedings of 1991 IEEE International Conference on Robotics and Automation, Sacramento, CA, 1991.
  9. K. Kosuge and T. Oosumi, “Decentralized control of multiple robots handling an object,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol.1, pp. 318-323, Osaka, Japan, 1996.
  10. Y. Kantaros and M. M. Zavlanos, “Stylus*: a temporal logic optimal control synthesis algorithm for large-scale multi-mobile-robot systems,” International Journal of Robotics Research, vol. 39, no. 7, pp. 812–836, April 2020.
  11. Q. Li, F. Gama, A. Ribeiro and A. Prorok, “Graph neural networks for decentralized multi-mobile-robot path planning,” Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 11785-11792, Las Vegas, NV, USA, October 2020.‌
  12. C. E. Luis, M. Vukosavljev and A. P. Schoellig, “Online trajectory generation with distributed model predictive control for multi-mobile-robot motion planning,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 604-611, April 2020.
  13. L. Liu, Z. Wang and H. Zhang, “Adaptive fault-tolerant tracking control for MIMO discrete-time systems via reinforcement learning algorithm with less learning parameters,” IEEE Transactions on Automation Science and Engineering, vol. 14, no. 1, pp. 299-313, January 2017.
  14. R. Marino and P. Tomei, “Robust adaptive state-feedback tracking for nonlinear systems,” IEEE Transactions on Automatic Control, vol. 43, no. 1, pp. 84-89, January 1998.
  15. B. Rivière, W. Hönig, Y. Yue, and S. Chung, “GLAS: global-to-local safe autonomy synthesis for multi-mobile-robot motion planning with end-to-end learning,” IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4249–4256, July 2020.
  16. G. Song and G. Tao, “A partial-state feedback model reference adaptive control scheme,” IEEE Transactions on Automatic Control, vol. 65, no. 1, pp. 44-57, January 2020.
  17. G. Tao, “Discrete-Time adaptive state tracking control schemes using gradient algorithms,” arXiv: 2308.02484 [eess.SY], 2023 (https://arxiv.org/abs/2308.02484).
  18. G. Tao, S. M. Joshi and X. Ma, “Adaptive state feedback and tracking control of systems with actuator failures,” IEEE Transactions on Automatic Control, vol. 46, no. 1, pp. 78-95, January 2001.
  19. G. Tao and Y. Ling, “Parameter estimation for coupled multivariable error models,” International Journal of Adaptive Control and Signal Processing, vol. 13, no. 2, pp. 145–159, April 1999.
  20. G. Tao and Q. H. Zhao, “Koopman system approximation based optimal control of multiple robots—Part I: Concepts and formulations,” arXiv: 2305.03777 [eess.SY], 2023 (https://arxiv.org/abs/2305.03777).
  21. J. Wang, Q. Dai, D. Shen, X. Song, X. Chen, S. Huang, and D. Filev, “Multi-agent navigation with collision avoidance: game theory approach,” Ford Report 2020-X, October 2020.
  22. Z. Wang and D. Gu, “Cooperative target tracking control of multiple robots,” IEEE Transactions on Industrial Electronics, vol. 59, no. 8, pp. 3232-3240, August 2012.
  23. Q. H. Zhao and G. Tao, “Autonomous vehicle tracking and collision avoidance using adaptive control algorithms,” Proceedings of 2022 Systems and Information Engineering Design Symposium (SIEDS), pp. 387-392, Charlottesville, VA, 2022.
  24. Q. H. Zhao and G.Tao, “Koopman system approximation based optimal control of multiple robots—Part II: Simulations and evaluations,” arXiv: 2305.03778 [eess.SY], 2023 (https://arxiv.org/abs/2305.03778).
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Authors (2)
  1. Qianhong Zhao (3 papers)
  2. Gang Tao (9 papers)

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