Fuzzy PDC-Based LQR Sliding Neural Network Control for Two-Wheeled Self-Balancing Cart

This paper proposes a fuzzy PDC (parallel distributed compensation)-based LQR (Linear Quadratic Regulator) sliding neural network methodology to control a two-wheeled self-balancing cart. Firstly, a mathematical model of a two-wheeled self-balancing cart is described to explain some parameter meanings. Then, we detail how a simulation was implemented according to these reasonable parameter settings under the fuzzy PDC-based LQR sliding neural network control algorithm. Secondly, the algorithm is developed by setting four controllable LQR controllers. Then, a ReLU-based neural network (ReNN) is developed to tune the fuzzy degrees for these four LQR controllers. This means that an intelligent controller is designed by using the fuzzy PDC concept. Subsequently, a sliding surface is designed, and the sliding mode is utilized to compensate and enhance its stability. Simulation was conducted to verify the feasibility of this proposed algorithm. The simulation results demonstrate good effectiveness and stability. Finally, a cart equipped with an STM32 MCU (microcontroller unit) was implemented to verify the feasibility of this proposed algorithm. The empirical experimental results show that the two-wheeled self-balancing cart exhibited good self-balancing performance and stability.