Frontiers of Information Technology & Electronic Engineering
>> 2021,
Volume 22,
Issue 2
doi:
10.1631/FITEE.1900610
Indirect adaptive fuzzy-regulated optimal control for unknown continuous-time nonlinear systems
Affiliation(s): State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China; Department of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China; less
Received: 2019-11-11
Accepted: 2021-02-01
Available online: 2021-02-01
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Abstract
We present a novel indirect adaptive fuzzy-regulated optimal control scheme for continuous-time nonlinear systems with unknown dynamics, mismatches, and disturbances. Initially, the Hamilton-Jacobi-Bellman (HJB) equation associated with its performance function is derived for the original nonlinear systems. Unlike existing adaptive dynamic programming (ADP) approaches, this scheme uses a special non-quadratic variable performance function as the reinforcement medium in the actor-critic architecture. An adaptive structure is correspondingly constructed to configure the weighting matrix of the performance function for the purpose of approximating and balancing the HJB equation. A concurrent self-organizing learning technique is designed to adaptively update the critic weights. Based on this particular critic, an adaptive optimal feedback controller is developed as the actor with a new form of augmented Riccati equation to optimize the fuzzy-regulated variable performance function in real time. The result is an online mechanism implemented as an , which involves continuous-time adaptation of both the optimal cost and the optimal control policy. The convergence and closed-loop stability of the proposed system are proved and guaranteed. Simulation examples and comparisons show the effectiveness and advantages of the proposed method.
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