双线性随机系统广义预报控制算法

以模型递推法实现双线性随机系统的多步预报,即重复利用受控对象模型自身包含的一步递推预报关系,依次得出对象输出的多步预报值,并证明了在一定条件下此预报值具有近似最小方差性。进而,利用适当的简化假设,按多步广义最小方差控制性能指标导出了双线性随机系统的广义预报控制算法。数字仿真证明,此控制算法在线计算量少,稳态精度高,通过适当地选取参数估计初值和调整预报步长,可以作到启动平稳、迅捷、几乎无超调,且抗阶跃扰动性能强,对变时延有良好的鲁棒性。

Generalized Predictive Control Algorithm for Bilinear Stochastic Systems

The successive one-step ahead predictions are used for the realization o: multi-step ahead predictions for bilinear stochastic system ( BLSS ) , i. e. the multi-step ahead predictions may simply be obtained via the system model recursions Furthermore, under some assumptions for the simplification of analysis, a Generalized Predictive Caontrol ( GPC ) algorithm for BLSS can be obtained according to the cos function of generalized minimum variance. Numerical simulations have shown that the algorithm proposed is feasible, requires less on-line calculation, and possesses high steady state control precision. By adjusting the prediction horizon and properly setting the initial values of parameter estimation, fast starting can be gained withou overshoot. The robustness against stepwise disturbance and time - delay variation is satisfactory.