Fuzzy PID Control for Trajectory Correction Based on Motor Current Variation

Abstract In this study, an attempt was made to interpolate linear models to approximate an entire nonlinear system, based on the traditional theory of fuzzy PID control. This approach considered the discrepancies between current detection and linear modeling, in conjunction with variations in motor current and the laws governing trajectory motion. The difference at a specific operating point between the obtained linear model and the actual nonlinear system was integrated into the linear system. Subsequently, a fuzzy controller was introduced for hall data within the cascade PID controller loop to synthesize a state feedback controller. Thereafter, variations in motor current were managed through fuzzy PID control. The theory, initially based on a single-wheel ramp scenario, was generalized to adapt to operational control on a general bump ramp. This method demonstrated satisfactory tracking responses upon testing. The paper also discusses the application of PID control in linear systems and how intelligent control, through its nonlinear approximation capabilities, addresses complex issues. However, the algorithms of intelligent control are often computationally intensive and structurally complex, making them impractical for real-world applications. In light of the nonlinear characteristics of certain systems, where accurate mathematical models are unattainable, the features of nonlinear PID control are advantageous for systems with low model accuracy. This scheme exhibits robust anti-interference capabilities and effectively enhances system control precision. Nevertheless, it has been noted to have a slower tracking speed for input signals. To evaluate the efficacy of fuzzy control, a tool capable of real-time positioning of the mobile robot was employed. The optimization design of the fuzzy PID controller was based on variations in motor current. Under different conditions, tests and observations were conducted, and the proposed fuzzy control method provided a favorable tracking response. The actual test outcomes were also effective, offering a novel perspective for the control of path stability.