Adaptive Learning-Based Path-Tracking Control for Unknown Vehicle Systems Under Performance Optimization

The state-of-the-art path-tracking control approaches for existing vehicle systems mostly rely on the accurate system dynamics and an initial stabilizing control policy assumption. To overcome those challenges, this paper presents an adaptive learning-based path-tracking control algorithm designed specifically for a completely unknown vehicle system without an initial stabilizing control strategy assumption. Firstly, a new variable is introduced to construct a new matrix thereby affording greater flexibility in selecting controller gains. Subsequently, leveraging this new matrix, a new policy iteration algorithm and an imitation-based policy iteration algorithm are concurrently proposed to achieve model-free learning path-tracking control in an optimal manner. In addition, an advanced data-driven switching policy iteration learning algorithm is developed to inherit the advantages of existing mainstream learning algorithms. When compared to several existing learning algorithms, the proposed algorithm not only eliminates the need for an initial stabilizing policy assumption but also exhibits faster convergence and reduced computational complexity. Finally, numerical simulations and comparisons are conducted to demonstrate the validity of the theoretical analysis.