Regular Papers

International Journal of Control, Automation, and Systems 2023; 21(10): 3382-3390

https://doi.org/10.1007/s12555-022-0745-9

© The International Journal of Control, Automation, and Systems

Neural Networks Based Optimal Tracking Control of a Delta Robot With Unknown Dynamics

Akram Gholami, Jian-Qiao Sun, and Reza Ehsani*

University of California Merced

Abstract

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.

Keywords Data-driven control, dynamics estimation, neural networks, optimal control.

Article

Regular Papers

International Journal of Control, Automation, and Systems 2023; 21(10): 3382-3390

Published online October 1, 2023 https://doi.org/10.1007/s12555-022-0745-9

Copyright © The International Journal of Control, Automation, and Systems.

Neural Networks Based Optimal Tracking Control of a Delta Robot With Unknown Dynamics

Akram Gholami, Jian-Qiao Sun, and Reza Ehsani*

University of California Merced

Abstract

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.

Keywords: Data-driven control, dynamics estimation, neural networks, optimal control.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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