带变遗忘因子的自适应子空间预测控制器设计

针对实际工业过程具有非线性、时变和多变量的特点,提出一种数据驱动的带有变遗忘因子的自适应子空间预测控制方法。该方法将在线子空间辨识与模型预测控制相结合,同时利用期望输出值与实际输出值的误差实现变遗忘因子的自适应更新,并根据当前变遗忘因子构造了过去与将来的Hankel矩阵,从而实现了预测模型的在线更新,提高了控制器对非线性时变特征的辨识灵敏度和适应能力。最后,利用该控制器对四容水箱对象进行仿真研究,验证了算法的有效性。

Design of adaptive subspace predictive controller with variable forgetting factor

In order to overcome the nonlinear, time-varying and multivariate of actual industrial processes, a kind of data-driven adaptive subspace predictive control method with forgetting factor was proposed . This method combined model predictive control with online subspace identification, the adaptive updating of variable forgetting factor was designed on the distance value of desired output and actual output at the same time, then the past and future forms of Hankel matrices were designed with the current forgetting factor, thus the online updated of predictive model was realized and the identification sensitivity and adaptability of controller for nonlinear and time-varying characteristics was improved. Finally, a simulating example with the quadruple tank was given to verify the validity of this method.