Regular Papers

International Journal of Control, Automation and Systems 2022; 20(8): 2768-2778

Published online July 12, 2022

https://doi.org/10.1007/s12555-021-0451-z

© The International Journal of Control, Automation, and Systems

Control Design for Stochastic Nonlinear Systems with Full-state Constraints and Input Delay: A New Adaptive Approximation Method

Na Li, Yu-Qun Han, Wen-Jing He, and Shan-Liang Zhu*

Qingdao University of Science and Technology

Abstract

In this paper, the full state constraints and input delay of stochastic nonlinear systems are studied. A new adaptive control algorithm is proposed using backstepping approach and multi-dimensional Taylor network (MTN) method. Firstly, the input delay problem is dealt with by introducing a new variable using the Padé approximation with Laplace transform. Secondly, MTNs are employed to approximate unknown nonlinear functions, and the barrier Lyapunov functions (BLFs) are constructed to deal with the state constraints. Based on this, a new approximation-based adaptive controller is proposed. Thirdly, it is proved that the proposed control method can ensure that all signals in the closed-loop system are semi-global ultimately uniformly bounded (SGUUB) in probability and the tracking error converges to a small neighborhood of the origin. Finally, two simulation examples are given to illustrate the effectiveness of the proposed design method.

Keywords Adaptive control, full-state constraints, input delay, multi-dimensional Taylor networks, stochastic nonlinear systems.

Article

Regular Papers

International Journal of Control, Automation and Systems 2022; 20(8): 2768-2778

Published online August 1, 2022 https://doi.org/10.1007/s12555-021-0451-z

Copyright © The International Journal of Control, Automation, and Systems.

Control Design for Stochastic Nonlinear Systems with Full-state Constraints and Input Delay: A New Adaptive Approximation Method

Na Li, Yu-Qun Han, Wen-Jing He, and Shan-Liang Zhu*

Qingdao University of Science and Technology

Abstract

In this paper, the full state constraints and input delay of stochastic nonlinear systems are studied. A new adaptive control algorithm is proposed using backstepping approach and multi-dimensional Taylor network (MTN) method. Firstly, the input delay problem is dealt with by introducing a new variable using the Padé approximation with Laplace transform. Secondly, MTNs are employed to approximate unknown nonlinear functions, and the barrier Lyapunov functions (BLFs) are constructed to deal with the state constraints. Based on this, a new approximation-based adaptive controller is proposed. Thirdly, it is proved that the proposed control method can ensure that all signals in the closed-loop system are semi-global ultimately uniformly bounded (SGUUB) in probability and the tracking error converges to a small neighborhood of the origin. Finally, two simulation examples are given to illustrate the effectiveness of the proposed design method.

Keywords: Adaptive control, full-state constraints, input delay, multi-dimensional Taylor networks, stochastic nonlinear systems.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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