Adaptive dynamic programming-based stabilization of nonlinear systems with unknown actuator saturation

This paper addresses the stabilizing control problem for nonlinear systems subject to unknown actuator saturation by using adaptive dynamic programming algorithm. The control strategy is composed of an online nominal optimal control and a neural network (NN)-based feed-forward saturation compensator...

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Bibliographic Details
Published in:Nonlinear dynamics Vol. 93; no. 4; pp. 2089 - 2103
Main Authors: Zhao, Bo, Jia, Lihao, Xia, Hongbing, Li, Yuanchun
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.09.2018
Springer Nature B.V
Subjects:
Actuators
Adaptive algorithms
Adaptive control
Automotive Engineering
Classical Mechanics
Computer simulation
Control
Control stability
Control theory
Dynamic programming
Dynamical Systems
Engineering
Feedforward control
Liapunov direct method
Mechanical Engineering
Neural networks
Nonlinear analysis
Nonlinear control
Nonlinear systems
Nonlinearity
Optimal control
Original Paper
Saturation
Stability analysis
Vibration
Unknown actuator saturation
Continuous-time nonlinear systems
Stabilizing control
Adaptive dynamic programming
Neural networks
ISSN:0924-090X, 1573-269X
Online Access:Get full text
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Summary:This paper addresses the stabilizing control problem for nonlinear systems subject to unknown actuator saturation by using adaptive dynamic programming algorithm. The control strategy is composed of an online nominal optimal control and a neural network (NN)-based feed-forward saturation compensator. For nominal systems without actuator saturation, a critic NN is established to deal with the Hamilton–Jacobi–Bellman equation. Thus, the online approximate nominal optimal control policy can be obtained without action NN. Then, the unknown actuator saturation, which is considered as saturation nonlinearity by simple transformation, is compensated by employing a NN-based feed-forward control loop. The stability of the closed-loop nonlinear system is analyzed to be ultimately uniformly bounded via Lyapunov’s direct method. Finally, the effectiveness of the presented control method is demonstrated by two simulation examples.
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-018-4309-8