Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4948428 | Neurocomputing | 2016 | 12 Pages |
Abstract
A novel learning-based optimal control approach is constructed to attain the decentralized guaranteed cost controller design for a class of continuous-time complex nonlinear systems with dynamical uncertainties and interconnections. This is performed by combining robust decentralized control formulation with adaptive critic learning technique. By expressing the interconnected subsystems as a whole system and introducing a new cost function for the overall plant, the decentralized guaranteed cost control problem is formulated as an optimal control problem for the nominal overall system. Then, a policy iteration based learning control algorithm is employed to solve the modified Hamilton-Jacobi-Bellman equation with respect to the nominal plant iteratively. A critic neural network is constructed to approximate the optimal state feedback control law and then the uniform ultimate boundedness stability issue is analyzed. Meanwhile, a simulation experiment is conducted to verify the good performance of the control approach.
Keywords
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Ding Wang, Derong Liu, Chaoxu Mu, Hongwen Ma,