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A block based ALADIN scheme for highly parallelizable direct Optimal Control

Nonlinear Model Predictive Control (NMPC) requires the online solution of a nonlinear Optimal Control Problem (OCP) at each sampling instant. This paper presents a novel, block based and highly parallelizable algorithm which solves nonlinear OCPs using a recently proposed Augmented Lagrangian based method (ALADIN). The latter employs techniques from standard Sequential Quadratic Programming (SQP) methods within a more parallelizable framework. An implementation tailored to optimal control is proposed where Nonlinear Programs (NLPs) are solved approximately and concurrently on each stage while a centralized consensus step is used to update the dual variables of the coupling constraints. The implementation also comprises algorithmic concepts to extend the parallelizability of the consensus step and a blocking technique to accelerate convergence. The performance of the resulting scheme is illustrated using as benchmark example the control of an overhead crane.