Nonlinear model predictive control optimization algorithm based on the trust-region quadratic programming

The nonlinear model predictive control(NMPC)requires the optimal or suboptimal solution of a nonlinear non-convex optimization problem at each sampling time,and the sequential-quadratic-programming(SQP)is the conven-tional algorithm for solving such a problem.By means of the simultaneous approach in nonlinear programming,an SQP sub-problem of NMPC is built,which considers the system state and the control as optimization variables simultaneously.Then,a new quadratic-programming(QP)sub-problem is established for which the step-length in each iteration is treated as an optimization variable and the linear inequalities are treated as constraints.After that,a trust-region-quadratic-programming approach is used to solve this sub-problem,and an update method that maintains the sparse structure for the Hessian matrix is used to reduce the computational complexity.Finally,simulation examples show the effectiveness of the presented approach.