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出 处:《Journal of Systems Engineering and Electronics》2002年第1期42-47,共6页系统工程与电子技术(英文版)
基 金:ThisprojectwassupportedbytheNationalNaturalScienceFoundationofChina (KeyProjectNo . 6 9934 0 2 0 ;No .6 0 0 740 0 4) .
摘 要:The on line computational burden related to model predictive control (MPC) of large scale constrained systems hampers its real time applications and limits it to slow dynamic process with moderate number of inputs. To avoid this, an efficient and fast algorithm based on aggregation optimization is proposed in this paper. It only optimizes the current control action at time instant k , while other future control sequences in the optimization horizon are approximated off line by the linear feedback control sequence, so the on line optimization can be converted into a low dimensional quadratic programming problem. Input constraints can be well handled in this scheme. The comparable performance is achieved with existing standard model predictive control algorithm. Simulation results well demonstrate its effectiveness.The on line computational burden related to model predictive control (MPC) of large scale constrained systems hampers its real time applications and limits it to slow dynamic process with moderate number of inputs. To avoid this, an efficient and fast algorithm based on aggregation optimization is proposed in this paper. It only optimizes the current control action at time instant k , while other future control sequences in the optimization horizon are approximated off line by the linear feedback control sequence, so the on line optimization can be converted into a low dimensional quadratic programming problem. Input constraints can be well handled in this scheme. The comparable performance is achieved with existing standard model predictive control algorithm. Simulation results well demonstrate its effectiveness.
关 键 词:Model predictive control (MPC) Receding horizon control (RHC) Approximate computation.
分 类 号:TP13[自动化与计算机技术—控制理论与控制工程]
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