Robust H∞ Control Synthesis with Learning in Additive Form for Autonomous Vehicles

This paper presents a method for improving the performance level of the robust H ∞ controllers. The improvement is achieved in an additive form, which contains a learning-based agent. The contribution of the presented method is that the design methods of the H ∞ controller and of the learning-based agent are formed in a joint optimization. This results in the iterative design of the controllers within a reinforcement learning algorithm. The developed design method is applied to an autonomous vehicle control problem for lap time minimization. The presented simulation-based analysis shows that the proposed method can provide improved performance level, compared to the conventional H ∞ control without extension or to H ∞ control with extension but without joint optimization.