Concurrent Learning Critic-Only NN-Based Robust Approximate Optimal Control of Nonlinear Systems With Experimental Verification

This article addresses the optimal control problem for a class of continuous-time nonlinear systems with time-varying bounded disturbances. A novel approximate optimal control policy that incorporates a continuous robust control command is developed within the framework of adaptive dynamic programmi...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 72; no. 8; pp. 8492 - 8502
Main Authors: Zhang, Haichao, Wang, Xin, Xiao, Bing, Wu, Xiwei, Li, Bo
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive dynamic programming
Closed loops
concurrent learning
Control systems
Control theory
Cost function
Dynamic programming
Iron
Learning
Mathematical models
Neural networks
Nonlinear control
Nonlinear systems
Optimal control
Optimization
perturbed control system
robust approximate optimal control
Robust control
Space vehicles
Stability analysis
Switches
Time-varying systems
Uncertainty
wheeled mobile robot
ISSN:0278-0046, 1557-9948
Online Access:Get full text
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Summary:This article addresses the optimal control problem for a class of continuous-time nonlinear systems with time-varying bounded disturbances. A novel approximate optimal control policy that incorporates a continuous robust control command is developed within the framework of adaptive dynamic programming. It makes the controlled system robust to the bounded time-varying disturbance rather than just the disturbance that vanishes as the system state converges. Moreover, an improved concurrent learning neural network (NN) weight updating algorithm that involves a switching mechanism is presented, and it can work without persistent/finite assumptions. Then, with the uniformly ultimately bounded stability guaranteed by Lyapunov theory, the closed-loop controlled system achieves approximate optimality and strong robustness. The superiority and effectiveness of the suggested control approach have been demonstrated via robotic trajectory tracking experiments.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2025.3528477