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Obtaining the optimal control low for nonlinear systems is one of the most active subjects in the control theory. Solutions become more complicate in the presence of physical constraints, especially input constrains. In this paper, a new method has been developed to design a constrained nonlinear controller. In this method, the performance index is developed based on the predictive approach. Then, an optimization problem is solved to obtain the optimal control law in the presence of input constraints. Two constrained optimization methods have been used in this paper. The KKT-based method provides an analytical solution for obtaining the control law. The other method is based on the genetic algorithm (GA) which is considered a numerical optimization technique. These methods are implemented on an example and the results are compared

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This paper presents a robust model predictive control scheme for a class of discrete-time nonlinear systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive uncertain non-linear time-varying function which satisfies a quadratic constraint. Using the dual-mode MPC stability theory, a sufficient condition is constructed for synthesizing the MPC’s stabilizing components; i.e. the local terminal cost function and the corresponding terminal set. The proposed control approach is applied to a CSTR. Simulation results show that the proposed robust MPC scheme is quite effective and it has a remarkable performance.