Adaptive Neural Network Prescribed Time Control for Constrained Multi-Robotics Systems with Parametric Uncertainties

Ruizhi Tang, Hai Lin, Zheng Liu (), Xiaoyang Zhou and Yixiang Gu
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Ruizhi Tang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Hai Lin: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Zheng Liu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Xiaoyang Zhou: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Yixiang Gu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Mathematics, 2024, vol. 12, issue 12, 1-15

Abstract: This study designed an adaptive neural network (NN) control method for a category of multi-robotic systems with parametric uncertainties. In practical engineering applications, systems commonly face design challenges due to uncertainties in their parameters. Especially when a system’s parameters are completely unknown, the unpredictability caused by parametric uncertainties may increase control complexity, and even cause system instability. To address these problems, an adaptive NN compensation mechanism is proposed. Moreover, using backstepping and barrier Lyapunov functions (BLFs), guarantee that state constraints can be ensured. With the aid of the transform function, systems’ convergence speeds were greatly improved. Under the implemented control strategy, the prescribed time control of multi-robotic systems with parametric uncertainties under the prescribed performance was achieved. Finally, the efficacy of the proposed control strategy was verified through the application of several cases.

Keywords: adaptive neural network control; parametric uncertainties; multi-robotic systems; barrier Lyapunov functions (BLFs); prescribed time control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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