Paper Info
Title
Adaptive Fuzzy Fault-Tolerant Tracking Control for Partially Unknown Systems with Actuator Faults via Integral Reinforcement Learning Method
Abstract
In this paper, the fuzzy reinforcement learning based tracking control algorithm is first proposed for partially unknown systems with actuator faults. Based on Takagi-Sugeno fuzzy model, a novel fuzzy-augmented tracking dynamic is developed and the overall fuzzy control policy with corresponding performance index is designed, where four kinds of actuator faults, including actuator loss of effectiveness and bias fault, are considered. Combining the reinforcement learning technique and fuzzy-augmented model, the new fuzzy integral reinforcement learning based fault-tolerant control algorithm is designed and it runs in real time for the system with actuator faults. The dynamic matrices can be partially unknown and the online algorithm requires less information transmissions or computational load along with the learning process. Under the overall fuzzy fault-tolerant policy, the tracking objective is achieved and the stability is proved by Lyapunov theory. Finally, the applications in the single-link robot arm system and the complex pitch-rate control problem of F-16 fighter aircraft demonstrate the effectiveness of the proposed method.
Year
DOI
Venue
2019
10.1109/TFUZZ.2019.2893211
IEEE Transactions on Fuzzy Systems
Keywords
Field
DocType
Actuators,Fault tolerance,Fault tolerant systems,Heuristic algorithms,Reinforcement learning,Standards,System dynamics
Lyapunov function,Online algorithm,Robotic arm,Control theory,Fuzzy logic,Fault tolerance,Fuzzy control system,Mathematics,Actuator,Reinforcement learning
Journal
Volume
Issue
ISSN
27
10
1063-6706
Citations 
PageRank 
References 
11
0.49
16
Authors
4
Name
Order
Citations
PageRank
huaguang zhang150539.89
Kun Zhang217131.04
Yuliang Cai3173.93
jian han414012.21