Performance Evaluation of a PID Sliding Surface and Radial Basis Function Neural Network for Mobile Robot - Trần Trung Hiếu; Nguyễn Việt Trung; Trần Thị Thùy Trang; Phạm Thanh Tùng (Journal of Technical Education Science, 2024)
A proportional integral derivative sliding surface (PIDSS) and radial basis function neural network (RBF-NN) for Mobile robot are applied in this study. This robot has many advantages such as simple structure, energy saving, high moving speed, and low production costs. The sliding mode control (SMC) controller using PIDSS (PIDSS-SMC) is designed so that the robot’s actual output approaches the standard input and reduces the chattering phenomena around the sliding surface. The RBF-NN is used to approximate the nonlinear components in the Pw matrix of the PIDSS-SMC controller. The weights of this neural network are trained online using the Gradient Descent algorithm. Lyapunov theory is used to prove the stability of the system. The actual output of the xw and yw converges to the reference xd and yd with the steady-state error converges to zero, the rising time reaches 0.0832s and 0.0764s, the settling time is 0.1309s and 0.1226s, the overshoot is 0.0042% and 0.0055%, respectively, and the chattering phenomena was reduced.