Continuous-time nonlinear model predictive control with input/output linearization

Obtaining a feasible optimal solution is the key issue in designing the constrained nonlinear predictive control.The input/output feedback linearization is a conventional method for this purpose.Because the original linear input constraints are converted to nonlinear and state-dependent constraints in this method,it makes the quadratic programming(QP) method inapplicable to obtain an optimal solution.To find the optimal solution for a state-space continuous-time system,we present an iterative quadratic program(QP) method.To guarantee its convergence,another iterative approach that can guarantee a feasible solution over the entire prediction horizon is incorporated.Simulation results of application to a continuous stirred tank reactor(CSTR) demonstrate the effectiveness of the proposed method.