In this paper, a wind energy conversion system is studied to improve the conversion efficiency and maximize power output. Firstly, a nonlinear state space model is established with respect to shaft current, turbine rotational speed and power output in the wind energy conversion system. As the wind velocity can be descried as a non-Gaussian variable on the system model, the survival information potential is adopted to measure the uncertainty of the stochastic tracking error between the actual wind turbine rotation speed and the reference one. Secondly, to minimize the stochastic tracking error, the control input is obtained by recursively optimizing the performance index function which is constructed with consideration of both survival information potential and control input constraints. To avoid those complex probability formulation, a data driven method is adopted in the process of calculating the survival information potential. Finally, a simulation example is given to illustrate the efficiency of the proposed maximum power point tracking control method. The results demonstrate that by following this method, the actual wind turbine rotation speed can track the reference speed with less time, less overshoot and higher precision, and thus the power output can still be guaranteed under the influence of non-Gaussian wind noises.

This paper studies a two-layer control strategy for optimal operational control which is prevalent in industrial production. The upper layer determines and adjusts the target set values, while the lower layer makes the loop output track the target value. In the two-layer structure optimal setting control system, the widely used PID controller is used in the bottom layer. Firstly, the parameters of the PID controller are obtained by solving linear matrix inequalities (LMI). Secondly, for industrial processes with nonlinear harmonic disturbances, a disturbance observer is designed to estimate these disturbances. Thirdly, the effects of disturbances or noises are minimized by dynamically adjusting the setting points. This method does not change the structure or parameters of the bottom controller, and thus meets the actual industrial requirements to a certain extent. Finally, in the numerical simulation section, the value of the performance index before set-points adjustment is compared with that after set-points adjustment.

In this article, a two‐layer control strategy is proposed for industrial processes where loop controllers are difficult to change. The upper layer performs the determination of the target value and the dynamic adjustment of the set points, while the lower layer realizes the tracking control of loop outputs. To optimize the performance index, observers are designed to estimate those uncertain external disturbances and a new set points adjuster is designed to minimize the influence of disturbances on the overall performance. It is noted that neither the loop structure nor the loop parameters are changed in the operational control. Finally, a simulation on coke oven gas system is given to demonstrate the effectiveness of this method.