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扰动模型的准确性对模型预测控制算法的扰动抑制能力有重要影响,当前模型预测控制广泛采用的阶跃扰动模型不能准确描述进入系统的不可测扰动,扰动抑制能力有限。自适应扰动模型可以较好的描述不可测扰动,提高对扰动的预估和抑制能力。本文对采用自适应时间序列扰动模型的预测控制进行分析,研究了扰动自适应预测控制(DMCA)的闭环结构以及带宽、灵敏度函数等频域指标与控制器抗扰性能的关系。带宽大的系统抑制扰动的速度快,灵敏度函数幅值越小则对扰动的抑制能力越强。理论分析和仿真结果表明与动态矩阵控制(DMC)相比,采用自适应扰动模型的DMCA算法能够更好的预测和抑制扰动,被控变量偏离设定值的最大幅度降低60%,带宽是DMC的1.5倍、调节速度更快,在低频段有较小的灵敏度函数值。自适应扰动模型提升了DMCA控制器的扰动抑制性能,对保障系统安全平稳运行和增加效益有重要意义。
The accuracy of the disturbance model has an important influence on the disturbance suppression ability of the model predictive control algorithm. The current step-by-step disturbance model which is widely used in predictive control of the current model can not accurately describe the unpredictable disturbance entering the system, and the disturbance suppression capability is limited. The adaptive disturbance model can better describe the unpredictable disturbance and improve the prediction and suppression of the disturbance. In this paper, the predictive control using adaptive time series disturbance model is analyzed. The closed-loop structure of disturbance adaptive predictive control (DMCA) and the relationship between the bandwidth and sensitivity function and the performance of the controller are studied. The system with large bandwidth suppresses the disturbance quickly, and the smaller the amplitude of the sensitivity function is, the stronger the ability to restrain the disturbance. The theoretical analysis and simulation results show that the DMCA algorithm with adaptive disturbance model can better predict and suppress the disturbance than the dynamic matrix control (DMC). The maximum deviation of the controlled variable from the set value by 60% is DMC 1.5 times faster, faster adjustment, and lower sensitivity function at lower frequencies. The adaptive disturbance model improves the disturbance rejection performance of the DMCA controller, which is of great significance to ensure the safe and stable operation of the system and increase the efficiency.