Parallel Shooting Sequential Quadratic Programming for Nonlinear MPC Problems
In this paper, we propose a parallel shooting algorithm for solving nonlinear model predictive control problems using sequential quadratic programming. This algorithm is built on a two-phase approach where we first test and assess sequential convergence over many initial trajectories in parallel. However, if none converge, the algorithm starts varying the Newton step size in parallel instead. Through this parallel shooting approach, it is expected that the number of iterations to converge to an optimal solution can be decreased. Furthermore, the algorithm can be further expanded and accelerated by implementing it on GPUs. We illustrate the effectiveness of the proposed Parallel Shooting Sequential Quadratic Programming (PS-SQP) method in some benchmark examples for nonlinear model predictive control. The developed PS-SQP parallel solver converges faster on average and especially when significant nonlinear behaviour is excited in the NMPC horizon.
Code (0)
등록된 구현이 없습니다.
Tasks
Model Predictive ControlMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Dual-Regularized Riccati Recursions for Interior-Point Optimal Control
We derive closed-form extensions of the sequential and parallel Riccati recursions for solving dual-regularized linear-quadratic regulator (LQR) problems, with $O(N)$ sequential time and $O(\log(N))$ parallel time, respe…
Sequential Quadratic Programming-based Iterative Learning Control for Nonlinear Systems
Learning-based control methods for industrial processes leverage the repetitive nature of the underlying process to learn optimal inputs for the system. While many works focus on linear systems, real-world problems invol…
A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimization
A step-search sequential quadratic programming method is proposed for solving nonlinear equality constrained stochastic optimization problems. It is assumed that constraint function values and derivatives are available, …
Stochastic OptimizationOn the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control
We study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domain into a set of overlapping subdomains, …
Motion PlanningSensitivityStatistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
We consider online statistical inference of constrained stochastic nonlinear optimization problems. We apply the Stochastic Sequential Quadratic Programming (StoSQP) method to solve these problems, which can be regarded …
Second-order methodsStochastic Optimization