基于增量状态空间模型的双层预测控制

Double-layered Predictive Control Based on Incremental State-space Model

为了提高对干扰系统建模的准确性,将被控输出作为状态建立增广的状态方程,提出基于增量状态空间模型和人工干扰模型的双层模型预测控制方案。该方案给出了增广模型的可检测性条件。利用稳态Kalman滤波得到增广的状态估计,在Kalman预报中对当前输出值和估计值的偏差进行补偿,获得准确的开环预测值。引入稳态目标计算获得最优的目标值以保证无静差控制。采用增量状态空间模型避免了工作平衡点的影响。仿真算例证实该方案能保证系统在有干扰出现的情况下可靠地跟踪稳态目标值,验证了该方案的有效性。

In order to improve the model accuracy for the disturbance system, the controlled output of the system is taken as a state to establish an augmented state equation in this paper. A new scheme is proposed for double-layered model predictive control based on the incremental state-space model and the artificial disturbance model. In the scheme, the conditions that guarantee detectability of the augmented model are provided. The augmented state estimate values are obtained by steady-state Kalman filter. The deviation between the current output value and the estimated value is compensated to obtain an accurate open-loop prediction value by Kalman predictor. The steady-state target calculation is introduced to obtain the optimal target value to ensure no static error control. The influence of equilibrium points is avoided by the incremental state-space model. The simulation example shows that the system can reliably track the steady-state target value and demonstrates the effectiveness of the proposed scheme.