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Nonlinear Predictive Control with Constraint Propagation Strategy

In the paper, new nonlinear predictive control method is proposed, that takes into consideration constraints on input and state variables. The method relies on a model obtained through feedback linearization. Quadratic cost function with state variables predicted from linear model is used. Constraints of the variables of nonlinear system are included by the use of constraints propagation to the variables of the linear model. In result, at every step of predictive control, quadratic cost function is minimized with linear constraints, providing control signals for nonlinear system. As example for application, system of four tanks is presented, that consists of two inputs and four state variables. Proposed control method is simulated on the model of this system. Results show that presented method, with finite prediction horizon, provides better performance than applied, with the same weighting matrices, linear quadratic control for linear model where constraints on inputs of nonlinear system are considered only by saturation function. Proposed method allows to obtain better prediction for inputs that are needed in future, the inputs stay shorter at their bounds in comparison to the results of linear quadratic control. The method reduces overshoot and settling time of state variables.