NMPC Through qLPV Embedding: A Tutorial Review of Different Approaches

Nonlinear Model Predictive Control (NMPC) formulations through quasi-Linear Parameter Varying (qLPV) embeddings have been brought to focus in recent literature. The qLPV realisation of the nonlinear dynamics yields linear predictions at each sampling instant. Thereby, these strategies generate online programs with reduced numerical burden, much faster solve than Programs generated regular NMPC. general lines methods: (i) embedding is formulated state-dependent scheduling parameters; (ii) Recursive extrapolation procedures are used estimate values parameters along prediction horizon; (iii) These estimates compute predictions, which incorporated by constrained optimisation procedure. This paper details overall concept novel NMPC techniques and reviews two different (efficient) implementation options. Realistic academic examples provided illustrate their performances.