摘要
This paper considers a robust control strategy for main steam temperature system of thermal power plant that is affected by immeasurable bounded disturbances. An output-feedback min-max optimization approach is proposed to obtain a controller which could effectively reject disturbances and stabilize the system. This approach combines moving horizon estimation with model predictive control and is formulated into a min-max optimization. At each sampling time, the optimization criterion is maximized over worst-case estimate of disturbance sequence and minimized over optimal control sequence via a primal-dual interior-point method. In this way, the controller can improve robustness under constrained disturbances. Simulation results verify that the presented approach could provide significant anti-disturbance capability compared with a conventional DMC-PID strategy.
This paper considers a robust control strategy for main steam temperature system of thermal power plant that is affected by immeasurable bounded disturbances. An output-feedback min-max optimization approach is proposed to obtain a controller which could effectively reject disturbances and stabilize the system. This approach combines moving horizon estimation with model predictive control and is formulated into a min-max optimization. At each sampling time, the optimization criterion is maximized over worst-case estimate of disturbance sequence and minimized over optimal control sequence via a primal-dual interior-point method. In this way, the controller can improve robustness under constrained disturbances. Simulation results verify that the presented approach could provide significant anti-disturbance capability compared with a conventional DMC-PID strategy.
引文
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1 Given an integer N,0N denotes the N-vectors with all entries equal to 0.
2 *means the entry-wise product.