A double-worst-case formulation for improving the robustness of an MPC-based obstacle avoidance algorithm to parametric uncertainty
Jiechao Liu, Paramsothy Jayakumar, Jeffrey L. Stein, Tulga Ersal · 2017
Previous work by the authors developed a nonlinear model predictive control-based obstacle avoidance algorithm for large, high-speed autonomous ground vehicles. In the algorithm, a nonlinear dynamic model of the vehicle is used explicitly to predict and optimize further actions, but it is unknown how the uncertainties in the model parameters would affect the navigation performance of the algorithm. In this paper, it is first demonstrated that using nominal parameter values in the algorithm leads to safety issues in 24% of the evaluated scenarios with the considered parametric uncertainty distributions. Second, to improve the robustness of the algorithm, a novel double-worst-case formulation is developed for a robust satisfaction of the two safety requirements of high-speed obstacle avoidance: collision-free and no-wheel-lift-off. Results from simulations with Latin Hypercube Design scenarios and worst-case scenarios show that the proposed formulation renders the algorithm robust to all uncertainty realizations tested.