Nonlinear model predictive control algorithm based on filter-trust-region method

In this paper, a filter-trust-region method is used in nonlinear model predictive control (NMPC) problem. By means of simultaneous approach based on nonlinear programming, an SQP sub-problem, which treats the iterate step Δu as an optimal variable, is built. After that, a trust region quadratic programming approach is used to solve the sub-problem, and the filter method is used to decide whether the trial point is better or not as an approximate solution to the optimization problem. And the Hessian matrix update method can also keep the sparse structure which is used to reduce the computational complexity. At last, the simulation result proves that the nonlinear predictive control algorithm based on filter-trust-region SQP method can get feasible solution within limited iterations at each time instant.