Neural Network-Based Adaptive Robust Fractional PID Control for Robotic Systems - Thi, Yen-Vu; Yao, Nan-Wang; Huu, Hai-Nguyen; Van, Cuong-Pham; Manh, Tung-Ngo (Universitas Muhammadiyah Yogyakarta, 2025)
This paper proposed an Adaptive Robust Fractional Oder PID controller based on neural networks (ARFONNs) in order to improve the trajectory tracking of Robotic systems. Robots are nonlinear objects with uncertain models, they are always affected by noise in the working process such as the payload variation, nonlinear friction, external disturbances, ect. To address this problem of robot, a proposed controller inherits the advantages of neural network, adaptive method and sliding mode controller to achieve fast and accurate control. The neural network controller has simple architecture, better approximation for the unknown dynamic of robotic systems, and fast training capability. Moreover, due to its robust nature, Sliding Mode Control (SMC) is a widely adopted nonlinear control approach. Furthermore, the quality of the robot control system is improved based on combining the flexibility of Fractional Order PID. The adaptive laws of the ARFONNs are defined by selecting a suitable Lyapunov function to the control system obtain global stability. In addition, Simulation and experimental results of the ARFONNs controller are conducted on a two-link Cleaning and Detecting Robot. The simulation and experimental results have compared with the Adaptive Robust Neural networks (ARNNs) and The neural networks controller (NNs) to demonstrate the stability and robustness as well as the performance of the ARFONNs controller.