This paper presents a novel decentralized control strategy for a class of uncertain nonlinear large-scale systems with mismatched interconnections. First, it is shown that the decentralized controller for the overall system can be represented by an array of optimal control policies of auxiliary subsystems. Then, within the framework of adaptive dynamic programming, a
simultaneous policy iteration (SPI) algorithm is developed to solve the Hamilton–Jacobi–Bellman equations associated with auxiliary subsystem optimal control policies. The convergence of the SPI algorithm is guaranteed by an equivalence relationship. To implement the present SPI algorithm, actor and critic neural networks are applied to approximate the optimal control policies and the optimal value functions, respectively. Meanwhile, both the least squares method and the Monte Carlo integration technique are employed to derive the unknown weight parameters. Furthermore, by using Lyapunov’s direct method, the overall
system with the obtained decentralized controller is proved to be asymptotically stable. Finally, the effectiveness of the proposed decentralized control scheme is illustrated via simulations for nonlinear plants and unstable power systems.
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Neural Networks Based Optimal Tracking Control of a Delta Robot With Unknown Dynamics
This paper proposes a data-driven optimal tracking control scheme for unknown general nonlinear systems using neural networks. First, a new neural networks structure is established to reconstruct the unknown system dynamics of the form ˙ x(t) = f (x(t))+g(x(t))u(t). Two networks in parallel are designed to approximate the functions
f (x) and g(x). Then the obtained data-driven models are used to build the optimal tracking control. The developed control consists of two parts, the feed-forward control and the optimal feedback control. The optimal feedback control is developed by approximating the solution of the Hamilton-Jacobi-Bellman equation with neural networks. Unlike other studies, the Hamilton-Jacobi-Bellman solution is found by estimating the value function
derivative using neural networks. Finally, the proposed control scheme is tested on a delta robot. Two trajectory tracking examples are provided to verify the effectiveness of the proposed optimal control approach.
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- Award ID(s):
- 1924662
- PAR ID:
- 10456024
- Date Published:
- Journal Name:
- International Journal of Control, Automation and Systems
- Volume:
- 21
- ISSN:
- 1598-6446
- Page Range / eLocation ID:
- 1-9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s12555-022-0745-9
