As a typical nonlinear, time-varying, and strongly coupled system, the stability control of a self-balancing vehicle imposes high demands on control algorithms. Traditional PID control often relies on empiricalparameter tuning when dealing with complex dynamic environments, making it challenging to achieve optimalcontrol performance. Therefore, this study adopts an improved Particle Swarm Optimization (PSO) algorithm tooptimize the parameters of the PID controller. By enhancing the global search capability and local optimizationability of the PSO algorithm, the proportional, integral, and derivative parameters of the PID controller areautomatically adjusted to improve the system's response speed and stability. Experimental results demonstrate that the PID controller based on the improved PSO algorithm outperforms the traditional PID controller in controlling the self-balancing vehicle, effectively enhancing its stability and control accuracy.

Liu Nan, Lv Yue, You Cheng, Zhang Ranhua, Yao Qijun. Design of a Self-Balancing Vehicle with PID Control Based on Improved Particle Swarm Optimization Algorithm. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 7: 17-22. https://doi.org/10.25236/AJCIS.2024.070703.

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