Trajectory Tracking using Fuzzy Predictive Control for Nonholonomic Mobile Robot

Mobile robots have been rapidly evolving due to their ability to assist or even replace humans in various tasks. One of the main challenges in mobile robot development is the ability to follow a path accurately while considering time and speed (trajectory tracking). However, many existing methods face challenges such as high computational complexity, difficult parameter tuning, lack of jittering suppression, and limited tracking precision. Due to these issues, this paper proposes a trajectory tracking control method using Model Predictive Control (MPC) integrated with the Takagi-Sugeno fuzzy system (T-S fuzzy system). The fuzzy system is used to adjust the weight parameters of matrices Q and R and to model nonlinear systems into multiple local linear subsystems. These local models serve as the predictive models within the MPC framework. The proposed Fuzzy Predictive Control (FPC) outperforms conventional MPC in several aspects. FPC shortened the time to reach the steady state by up to 3 seconds, reduced cost function values, and eliminated jitter caused by large prediction and control horizons. These advantages suggest that FPC is wellsuited for dynamic environments requiring high precision, such as autonomous navigation or industrial automation. Compared to conventional MPC, FPC achieved lower cost function values, with reductions of 33.82% for circular, 38.12% for sinewave, and 33,95% for lemniscate trajectories.