Autonomy Talks - Andrea Zanelli: Efficient inexact numerical methods for nonlinear MPC скачать в хорошем качестве

Autonomy Talks - Andrea Zanelli: Efficient inexact numerical methods for nonlinear MPC

Autonomy Talks - 15/11/2021 Speaker: Dr. Andrea Zanelli, Institute for Dynamic Systems and Control, ETH Zürich Title: Efficient inexact numerical methods for nonlinear model predictive control with feasibility guarantees with applications to autonomous racing Abstract: Model predictive control is an optimization-based control strategy that can inherently address multivariate, nonlinear and constrained control problems. However, it requires that a nonconvex program is solved within the time available between subsequent sam- pling instants. Although considerable progress has been made since its early application in the late 1970s in the process industry, MPC still requires a computational effort that is prohibitive for many applications due to fast dynamics or the low computational power available. For this reason, despite MPC being nowadays the state-of-the-art control strat- egy in many applications, its applicability to a broader range of systems, still hinges on the development of efficient methods for numerical optimization. In this talk, we propose inexact methods that can speed up the computations asso- ciated with the solution of the underlying nonconvex programs. Although drawing from rather diverse areas, from an abstract point of view, such methods exploit a common idea. In fact, in many cases, carefully chosen perturbations to exact solutions and formulations do not jeopardize fundamental properties such as stability and recursive feasibility and can be leveraged to alleviate the computational burden of MPC. In particular, two methods will be presented that can efficiently compute suboptimal, but feasible solutions to nonconvex programs arising from nominal and robust MPC for- mulations. The first method proposed gives rise to an ”anytime” sequential quadratic programming strategy with feasible intermediate iterates that can be early-terminated without affecting feasibility of the obtained solution. We show how this property can be used to design a suboptimal MPC controller for autonomous racing with guaranteed recursive feasibility properties. The proposed method has been implemented using the open-source package feasible sqp (https://github.com/zanellia/feasible_sqp) and experimentally validated on a physical miniature race track. The second method discussed in the talk aims at reducing the computational footprint of robust MPC with ellipsoidal uncertainty. An inexact sequential quadratic programming strategy is proposed that can efficiently compute suboptimal, but feasible solutions to the robustified nonconvex programs. Convergence properties and asymptotic behavior of the approximate solutions are investigated and the computational benefits of the algorithm are assessed in a numerical benchmark where speedups of up to 3 orders of magnitude can be obtained. To know more, visit the official webpage: https://idsc.ethz.ch/research-frazzol...