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In this paper, we propose a parallel shooting algorithm for solving nonlinearmodel predictive control problems using sequential quadratic programming. Thisalgorithm is built on a two-phase approach where we first test and assesssequential convergence over many initial trajectories in parallel. However, ifnone converge, the algorithm starts varying the Newton step size in parallelinstead. Through this parallel shooting approach, it is expected that thenumber of iterations to converge to an optimal solution can be decreased.Furthermore, the algorithm can be further expanded and accelerated byimplementing it on GPUs. We illustrate the effectiveness of the proposedParallel Shooting Sequential Quadratic Programming (PS-SQP) method in somebenchmark examples for nonlinear model predictive control. The developed PS-SQPparallel solver converges faster on average and especially when significantnonlinear behaviour is excited in the NMPC horizon.

Parallel Shooting Sequential Quadratic Programming for Nonlinear MPC Problems