摘要：AbstractWe present an embeddable optimization-free application of a near-optimal MPC implementation with continuous tuning capabilities. We propose a strategy combining the advantages of explicit model predictive control with tunable properties that is implementable on embedded platforms with limited memory and computational resources. We consider a neural network (NN) learning procedure to mimic the control actions of an MPC strategy. While acknowledging limited guarantees on the constraints satisfaction with just the NN-based controller, we introduce an optimization-based corrector of the mimicked control action. Such a corrector then steers the control authority of the mimicked controller such that constraints on manipulated and process variables are enforced. To demonstrate the efficacy of the proposed control strategy, a case study implemented on an embedded platform is shown.
关键词：KeywordsData-based optimal controlReal-Time Control ProblemsOptimal Control
