Nonlinear model predictive control optimization algorithm based on the trust-region quadratic programming
基于信赖域二次规划的非线性模型预测控制优化算法

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 conventional 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-quadraticprogramming 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.