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A task decomposition method for iterative learning model predictive controlis presented. We consider a constrained nonlinear dynamical system and assumethe availability of state-input pair datasets which solve a task T1. Ourobjective is to find a feasible model predictive control policy for a secondtask, T2, using stored data from T1. Our approach applies to tasks T2 which arecomposed of subtasks contained in T1. In this paper we propose a formaldefinition of subtasks and the task decomposition problem, and provide proofsof feasibility and iteration cost improvement over simple initializations. Wedemonstrate the effectiveness of the proposed method on autonomous racing androbotic manipulation experiments.

Task Decomposition for Iterative Learning Model Predictive Control