Economic model predictive control of nonlinear systems using a linear parameter varying approach

This article proposes an economic model predictive control (EMPC) approach for linear parameter varying (LPV) systems. An efficient implementation of the associated MPC optimization problem is introduced based on transforming LPV into a time-varying one by using estimation scheduling variables along prediction horizon. optimal states/inputs determined from solution previous while running receding horizon strategy. Using this approach, proposed LPV-based EMPC scheme would be possible solving series quadratic programming problems at each time instant. allows reducing computational burden compared with nonlinear that result naturally formulation. The stability guaranteed forcing terminal state to converge towards equilibrium/working point system. Moreover, constraint relaxed set around instead value and adding penalty cost function. Besides, strict dissipativity established as sufficient condition prove stability. Finally, effectiveness strategy shown controlling small-scale pasteurization system in simulation. comparison between standard approaches performed. Results show advantages controller terms minimization.