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基于内模控制理论,针对火电厂主汽温被控对象的大惯性、大迟延、时变、多干扰的特点,设计了内模–比例串级控制系统,并将量子遗传算法应用于滤波器参数的寻优。并在此基础上结合T-S模糊建模和自适应控制技术,提出了模糊自适应内模控制(fuzzyad aptive internal model control,FAIMC)策略。该方案实现简单,对工况变化具有优良的适应性。对某超临界600MW直流锅炉主汽温系统4种典型工况进行仿真控制,其过渡过程时间短,超调量小,适用于大惯性、大迟延过程的控制,控制效果明显优于串级PID控制。为克服负荷变化对主汽温系统性能的影响,采用模糊自适应内模控制策略分别进行了升降负荷实验。仿真结果表明:提出的控制系统能较好的适应对象动态模型的大幅度变化,保持较优的调节性能。
Based on the theory of internal model control, aiming at the characteristics of large inertia, large delay, time-varying and multi-disturbance of the main steam temperature controlled object in thermal power plant, an internal model-proportional cascade control system is designed and the quantum genetic algorithm is applied to the filter Optimization of parameters. Based on this, combined with T-S fuzzy modeling and adaptive control technology, a fuzzyadaptive internal model control (FAIMC) strategy is proposed. The scheme is simple to implement and has excellent adaptability to changes of working conditions. Simulation of four typical conditions of the main steam temperature system of a supercritical 600 MW once-through boiler shows that the transient process is short and the overshoot is small, which is suitable for the control of large inertia and large delay. The control effect is obviously better than cascade PID control. In order to overcome the influence of load changes on the main steam temperature system performance, the fuzzy adaptive internal model control strategy was used to carry out the lifting load experiment. The simulation results show that the proposed control system can better adapt to the substantial changes of the dynamic model of the object and maintain the optimal adjustment performance.