LAUSR

In real-world driving scenarios, the model mismatch can severely impair the robustness of the tracking system controlled by Model Predictive Control (MPC). Tube-based MPC (TMPC) addresses this problem by keeping the model mismatch error in an invariant tube. The TMPC algorithms, however, cannot deal with state-dependent uncertainty since TMPC relies on the fixed tubes. This paper presents a practical algorithm for improving the capability of TMPC to handle multiplicative uncertainty. Firstly, this algorithm adopts a Homothetic Tube-based MPC (HTMPC) framework to optimize the system’s future trajectory and tube geometry simultaneously, which dynamically resizes tubes according to uncertainty and the system’s current state. Secondly, this algorithm provides both the feasible formulation of the tube and the homothetic factor with low computational complexity. Thirdly, we aim to systematically evaluate the algorithm’s robustness by the simulations of different scenarios where the system parameters and the measurement noises might change over time. We have conducted and analyzed the Monte-Carlo simulations to compare the robustness and tracking capability of the proposed algorithm and other control algorithms. The comparative analysis shows that the HTMPC algorithm provides a higher level of performance than MPC and TMPC, and it performs closely to the robust controller based on the immersion and invariance (I&I) principle.