Event-Driven Guaranteed Cost Control Design for Nonlinear Systems With Actuator Faults via Reinforcement Learning Algorithm

This article presents a novel event-driven guaranteed cost control method for nonlinear systems subject to actuator faults. For the purpose of handling the problem of actuator faults and obtaining the event-driven approximate optimal guaranteed cost control approach for general nonlinear dynamics, t...

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Published in:IEEE transactions on systems, man, and cybernetics. Systems Vol. 50; no. 11; pp. 4135 - 4150
Main Authors: Zhang, Huaguang, Liang, Yuling, Su, Hanguang, Liu, Chong
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Actuators
Algorithms
Approximation algorithms
Control methods
Control systems
Cost function
Dynamical systems
Event-driven control
fault tolerant control
Faults
guaranteed cost control
Heuristic algorithms
Machine learning
Neural networks
Nonlinear control
Nonlinear dynamics
Nonlinear systems
Process control
reinforcement learning (RL)
Sliding mode control
sliding mode control (SMC)
ISSN:2168-2216, 2168-2232
Online Access:Get full text
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Summary:This article presents a novel event-driven guaranteed cost control method for nonlinear systems subject to actuator faults. For the purpose of handling the problem of actuator faults and obtaining the event-driven approximate optimal guaranteed cost control approach for general nonlinear dynamics, the reinforcement learning (RL) algorithm is utilized to develop a sliding-mode control (SMC) strategy. To begin with, the unknown faults can be estimated by designing a fault observer. Meanwhile, an SMC technique is presented aiming at countering the effect of abrupt faults. In addition, the optimal performance of the equivalent sliding mode dynamics is considered, then an event-driven guaranteed cost control mechanism is implemented by using RL principle. In the control process, a general cost function, which has a simpler structure, is given to reduce the computation complexity. At the same time, a modified cost function is approximated to obtain optimal guaranteed cost control by using a single critic neural network (NN). In addition, a modified weight update law for critic NN is presented to relax the persistence of excitation (PE) condition. Moreover, a newly triggering condition, which is easy to be implemented, is designed, and the critic NN update law makes sure that the system states are stable. Furthermore, in light of the Lyapunov analysis, it is demonstrated that the developed event-driven control method guarantees the uniformly ultimately bounded (UUB) property of all the signals. Finally, three simulation results are given to validate the designed control method.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2019.2946857