Path tracking control for autonomous vehicles with saturated input: A fuzzy fixed‐time learning control approach

Abstract

This paper presents a fuzzy fixed‐time learning path tracking control strategy for autonomous vehicles with saturated input and uncertainties. First, Takagi‐Sugeno (T‐S) model is designed to realize an accurate description of the nonlinear path tracking systems. The T‐S model is the weighted sum of a series of linear dynamics models. The weighting coefficients are described by fuzzy membership functions. Then, with the consideration of the saturated input, a fixed‐time learning control is proposed. It contains a learning law to estimate the uncertainties of the system on‐line. The fixed‐time convergence and the robustness properties are analyzed through Lyapunov stability theory. Finally, the effectiveness of the proposed control method has been verified through the CarSim/Simulink co‐simulations under different driving conditions. The results demonstrate that the developed controller significantly improves the performance of path tracking and follows the desired path well even in the case of parameter uncertainties and external disturbances.

Model-Free Output Feedback Path Following Control for Autonomous Vehicle With Prescribed Performance Independent of Initial Conditions

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Neuro-Adaptive Path following Control of Autonomous Ground Vehicles with Input Deadzone

“…The actuator nonlinearities such as backlash, deadzone, and saturation can degrade the path following performance or even lead to instability of the control system [50][51][52]. Several studies have reported the path tracking control of AGV subjected to input saturation [53][54][55][56][57][58][59][60][61]. The authors in [62,63] studied the steering control of AGV with input backlash.…”

Path-following control method for articulated vehicles based on dual heuristic programming

“…Therefore, some scholars have adopted several intelligent optimisation algorithms. Aiming at the uncertain and saturated input factors of autonomous vehicles, a fuzzy fixed-time learning path-following control strategy is proposed 20 in this study, but the fuzzy membership function continues to require human experience for selection. Carlucho et al 21 presented an incremental Q-learning mobile robot control strategy with adaptive PID control, which can be applied to the real environment under variable conditions.…”