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This article presents a nonlinear model predictive control (NMPC) approach based on quasi-linear parameter varying (quasi-LPV) representations of the model and constraints. Stability of the proposed algorithm is ensured by the offline solution of an optimization problem with linear matrix inequality constraints in conjunction with an online terminal state constraint. Furthermore, an iterative approach is presented with which the NMPC optimization problem can be handled by solving a series of Quadratic Programs at each time step, this being highly computationally efficient. A practical and simple way of obtaining quasi-LPV representations of the system using velocity-based linearization is presented in two examples. K E Y W O R D S efficient algorithms, linear matrix inequality, nonlinear model predictive control, quasi-linear parameter varying systems 1 Int J Robust Nonlinear Control. 2020;30:3945-3959.wileyonlinelibrary.com/journal/rnc

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