Gradient Projection Based Algorithm for Large Scale Real Time Model Predictive Control

摘要：

In model predictive control (MPC), the quadratic program (QP) is solved at each sampling time, thus a fast and effective on-line solver must be used for short sampling times. The multi-parametric quadratic programming (mp-QP) (explicit solution) is impossible to use for larger systems due to the memory limitation. The objective of this paper is to present an effective on-line solver for large-scale simple constrained quadratic programming which arises in the MPC framework. The presented algorithm uses the combination of gradient and Newton projection method to obtain super-linear convergent algorithm which is very close to optimum in very few iterations when many constraints are active in optimum and it does not involve the exact computation of the Newton step at each iteration.

关键词： Mathematical programming Quadratic programming Newton methods Gradient methods Projection algorithms Predictive control Real time systems

作者: Ond(r)ej (S)antin Vladimir Havlena

作者单位: with Faculty of Electrical Engineering,Department of Control Engineering, Czech Technical University in Prague,Prague,Czech Republic

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 3906-3911

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）