Regular Papers

International Journal of Control, Automation, and Systems 2023; 21(9): 3116-3126

https://doi.org/10.1007/s12555-022-0626-2

© The International Journal of Control, Automation, and Systems

Adaptive Fuzzy Finite Time Prescribed Performance Control for Long Stroke Hybrid Robots

Qunpo Liu*, Ming Ye, Zhonghua Wu, Xuhui Bu, and Naohiko Hanajima

Henan Polytechnic University

Abstract

This paper focuses on the problem of finite-time trajectory tracking control of long-stroke hybrid robots with uncertain system parameters and external disturbances. A system filtering prescribed performance backstepping control based on adaptive fuzzy compensation is proposed. For the unknown terms corresponding to the parameter uncertainties and unknown disturbances existing in the system, a set of auxiliary filter variables is introduced in the adaptive fuzzy approximator and an estimation error function is constructed. An adaptive law based on the estimated error function is proposed to adjust the adaptive weight parameters of the fuzzy system. An adaptive fuzzy approximation algorithm based on the estimation error is finally constructed to compensate for the performance loss caused by the unknown term. For the system state convergence speed problem, a performance function that can transform the system tracking error into an unconstrained error is presented, and the unconstrained error is used as the backstepping control variables. To avoid the differential explosion problem, a new set of inverse control variables is defined by combining the filter variables obtained based on the low-pass filter and the joint velocity. Based on the above variables, a system filtering-based prescribed performance backstepping control strategy is proposed. Finally, the semi-global practical finite-time stability of the closed-loop system is proved by the Lyapunov function. Experiments on the MATLAB platform verify the effectiveness of the proposed method.

Keywords Adaptive fuzzy system, finite-time control, long stroke hybrid robots, unknown dynamic estimation.

Article

Regular Papers

International Journal of Control, Automation, and Systems 2023; 21(9): 3116-3126

Published online September 1, 2023 https://doi.org/10.1007/s12555-022-0626-2

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

Adaptive Fuzzy Finite Time Prescribed Performance Control for Long Stroke Hybrid Robots

Qunpo Liu*, Ming Ye, Zhonghua Wu, Xuhui Bu, and Naohiko Hanajima

Henan Polytechnic University

Abstract

This paper focuses on the problem of finite-time trajectory tracking control of long-stroke hybrid robots with uncertain system parameters and external disturbances. A system filtering prescribed performance backstepping control based on adaptive fuzzy compensation is proposed. For the unknown terms corresponding to the parameter uncertainties and unknown disturbances existing in the system, a set of auxiliary filter variables is introduced in the adaptive fuzzy approximator and an estimation error function is constructed. An adaptive law based on the estimated error function is proposed to adjust the adaptive weight parameters of the fuzzy system. An adaptive fuzzy approximation algorithm based on the estimation error is finally constructed to compensate for the performance loss caused by the unknown term. For the system state convergence speed problem, a performance function that can transform the system tracking error into an unconstrained error is presented, and the unconstrained error is used as the backstepping control variables. To avoid the differential explosion problem, a new set of inverse control variables is defined by combining the filter variables obtained based on the low-pass filter and the joint velocity. Based on the above variables, a system filtering-based prescribed performance backstepping control strategy is proposed. Finally, the semi-global practical finite-time stability of the closed-loop system is proved by the Lyapunov function. Experiments on the MATLAB platform verify the effectiveness of the proposed method.

Keywords: Adaptive fuzzy system, finite-time control, long stroke hybrid robots, unknown dynamic estimation.

IJCAS
June 2024

Vol. 22, No. 6, pp. 1761~2054

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