Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Re-Training

arXiv:2404.05835v1 Announce Type: cross
Abstract: Model Predictive Control (MPC) is a method to control nonlinear systems with guaranteed stability and constraint satisfaction but suffers from high computation times. Approximate MPC (AMPC) with neural networks (NNs) has emerged to address this limitation, enabling deployment on resource-constrained embedded systems. However, when tuning AMPCs for real-world systems, large datasets need to be regenerated and the NN needs to be retrained at every tuning step. This work introduces a novel, parameter-adaptive AMPC architecture capable …

abstract arxiv computation control cs.lg cs.sy deployment eess.sy embedded enabling however math.oc mpc network networks neural networks nns predictive stability systems training type