基于闭环子空间辨识的电厂协调系统预测控制

Predictive Control for a Coordinated Control System of a Power Plant Based on Closed-loop Subspace Identification

针对工业控制系统由于模型失配或者被控对象特性在运行过程中发生改变造成的控制性能下降问题,设计了基于数据驱动的闭环子空间预测控制策略。将闭环子空间辨识算法与预测控制算法结合起来,直接根据输入输出数据对系统未来输出进行预测,避免了建模不准确所造成误差,提高了控制准确性,且被控对象特性发生改变时,能够及时根据输入输出数据对控制器进行调整,提高了系统的鲁棒性。将其应用于火电机组协调系统,仿真结果表明,相较于基于模型的预测控制和传统的PID控制,该算法能够提高系统的设定值跟踪性能和鲁棒性能。

Taking model mismatch into account,a data-driven closed-loop subspace predictive control strategy was developed.It is a combination of closed loop subspace identification and model predictive control method.It leaves out solve procedure of state space models,and predicts system outputs using input-output data.The method was applied in a coordinated control system(CCS) of a thermal power plant.Simulation results demonstrate its effectiveness and superiority on set-point tracking performance and system robustness.