Path Tracking of a Two-Wheeled Self-Balancing Robot Based on Multi-objective Optimization with Artificial Immune Algorithm
Jiayuan Wang · 2023
In this paper, a multi-objective optimization-based path tracking method for a two-wheeled self-balancing robot (TWSBR) is proposed. First, the dynamics model of the system is established based on the Newton-Euler method and simulated in simulink. Then an artificial immune algorithm is used to find the optimal motion parameters for each control point based on the parameter feedback of the TWSBR, and finally a proportional-integral-derivative controller (PID) is used for control. The advantages of this study are as follows: (1) The stability of the TWSBR in high-speed working condition is improved and the efficiency of the robot is enhanced by the optimization algorithm. (2) The anti-jamming capability of the TWSBR is improved through parameter feedback. (3) The optimized PID controller is used to control the motor torque, which avoids manual adjustment of parameters and improves the control efficiency. The method can be applied to TWSBR with fixed paths such as logistics distribution and space disinfection.