Due to the nonminimum phase characteristics and nonlinearity of boost converters, control design is always a challenging issue. A novel model predictive strategy proposed for converter in this work. First, Super-Twisting algorithm applied current control, input–output plant voltage derived based on linearization technique. All uncertainties are defined as lumped disturbances, generalized propor...

In this paper, a neural network based predictive controller is designed to govern the dynamics of a heat exchanger pilot plant. Heat exchanger is a highly nonlinear process; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. Advantages of neural networks for the process modeling are studied and a neural network based predictor is designed, trained a...

Abstract. Reference trajectory asymptotic tracking and disturbance rejection are an important field of control theory. In this paper, we are interested on the tracking of reference trajectories and the rejection of periodic disturbances with unknown frequency for multivariable systems. If the frequency is known, a classical solution is to use a multivariable repetitive generalized predictive co...

this paper presents a simple model predictive control (mpc) approach to control the power injection system (pis) for renewable energy applications. a dc voltage source and a single-phase inverter that is connected to the grid by an lcl filter form the pis. grid voltage is considered a disturbance for the system. for eliminating this disturbance, a modified model is proposed. it is usual to cont...