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Robust predictive control of uncertain intergrating linear systems with input constraints

Robust predictive control of uncertain intergrating linear systems with input constraints
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摘要 This paper presents a two-stage robust model predictive control (RMPC) aigorithm named as IRMFC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence of the resulted closed loop system is guaranteed under mild assumption. The simulation example shows its validity and better performance than conventional Min-Max RMPC strategies. This paper presents a two-stage robust model predictive control (RMPC) algorithm named as IRMPC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence of the resulted closed loop system is guaranteed under mild assumption. The simulation example shows its validity and better performance than conventional Min-Max RMPC strategies.
出处 《Journal of Zhejiang University Science》 CSCD 2002年第4期418-425,共8页 浙江大学学报(自然科学英文版)
关键词 鲁棒预测控制 不确定线性集成系统 输入约束 模型预测控制 Model predictive control, Robust control, Input constraints, Convex programming
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