%0 Journal Article
%T Nonlinear model predictive control optimization algorithm based on the trust-region quadratic programming<br>基于信赖域二次规划的非线性模型预测控制优化算法
%A ZHAO Min
%A LI Shao-yuan
%A <br>赵 敏
%A 李少远
%J 控制理论与应用
%D 2009
%I 
%X 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.
%K nonlinear predictive control
%K nonlinear programming
%K sequential-quadratic-programming
%K trust-region approach<br>非线性预测控制
%K 非线性规划
%K 序列二次规划(SQP)
%K 信赖域
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=15043CCB2CDC98F951583BF4F873836E&yid=DE12191FBD62783C&vid=96C778EE049EE47D&iid=B31275AF3241DB2D&sid=20ED669EB429E15C&eid=DB7B2C790D19BE6E&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=2&reference_num=22