Development of an Adaptive PI Controller for Autonomous Mobility Based on Multiple RLS Algorithms with a Selective Update Rule

This paper presents a universal, model-independent Adaptive PI (A-PI) control framework using multiple Recursive Least Squares (RLS) algorithms, primarily focused on the longitudinal velocity control of autonomous vehicles. To overcome the limitations of fixed-gain controllers, the proposed system self-tunes control parameters in real-time based on gradient descent and Lyapunov stability theories, requiring no complex vehicle dynamics. Particularly, to address the multivariable coupling effect during real-time estimation, a selective update rule is proposed, ensuring the theoretical validity of independent gain self-tuning. Furthermore, a novel error-based covariance scaling logic is introduced to dynamically and selectively update the proportional and integral scale factors across three distinct error areas. This mechanism ensures rapid initial convergence in the transient region and smooth settling without overshoot near the target. To evaluate the feasibility of the proposed universal and adaptive framework, longitudinal velocity tracking performance was analyzed through a MATLAB/Simulink version 2023b and CarMaker co-simulation environment. Simulation results demonstrate that the proposed A-PI controller significantly reduces the Root Mean Square (RMS) control error compared with conventional fixed PI controllers under dynamic scenarios, proving its robust adaptability and paving the way for future integrated longitudinal and lateral vehicle control.