Optimized Fault‐Tolerant Fixed‐Time Control for Nonlinear Strict‐Feedback Systems With Output Constraints
Focusing on the actuator fault and output constraints of a nonlinear strict‐feedback system, a neural network‐based optimized fixed‐time controller is investigated in this paper. An optimized backstepping framework is adopted for controller design, where a critic–actor architecture is integrated to...
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| Published in | International journal of robust and nonlinear control |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
31.08.2025
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| Online Access | Get full text |
| ISSN | 1049-8923 1099-1239 |
| DOI | 10.1002/rnc.70163 |
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| Abstract | Focusing on the actuator fault and output constraints of a nonlinear strict‐feedback system, a neural network‐based optimized fixed‐time controller is investigated in this paper. An optimized backstepping framework is adopted for controller design, where a critic–actor architecture is integrated to progressively approximate the optimal control policy. And the adaptive laws are developed to compensate for the disturbance and actuator failure. To address the output constraints, a nonlinear function related to these constraints is directly incorporated into the controller, thereby simplifying the computations. Furthermore, the fixed‐time stability of the closed‐loop system and each subsystem is analyzed, along with the fulfillment of output constraints. Lastly, the efficiency and correctness of the proposed algorithm are confirmed through two numerical simulations. |
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| AbstractList | Focusing on the actuator fault and output constraints of a nonlinear strict‐feedback system, a neural network‐based optimized fixed‐time controller is investigated in this paper. An optimized backstepping framework is adopted for controller design, where a critic–actor architecture is integrated to progressively approximate the optimal control policy. And the adaptive laws are developed to compensate for the disturbance and actuator failure. To address the output constraints, a nonlinear function related to these constraints is directly incorporated into the controller, thereby simplifying the computations. Furthermore, the fixed‐time stability of the closed‐loop system and each subsystem is analyzed, along with the fulfillment of output constraints. Lastly, the efficiency and correctness of the proposed algorithm are confirmed through two numerical simulations. |
| Author | Ju, Xiaozhe Ma, Guangfu Sun, Yue Li, Dongyu Gong, Youmin |
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