一种基于滑模的自适应预测函数控制新算法

New Algorithm of the Sliding-model Based Adaptive Predictive Function Control

提出了一种应用带时变遗忘因子的基于滑模的自适应预测函数控制新算法。该算法采用带时变遗忘因子的递推最小二乘算法在线辨识模型参数,将滑模控制与预测函数控制(PFC)相结合对系统进行控制。与其他模型预测控制不同,预测函数控制可以克服其他模型预测控制可能出现规律不明的控制输入问题,具有良好的跟踪能力和较强的鲁棒性,离散滑模控制中的滑动模态对干扰具有不变性;最后分析了控制系统的闭环渐近稳定性。仿真结果验证了该方法的有效性。

An adaptive predictive function control algorithm based on the sliding-model is presented. This method estimates model parameters on-line by the least squares identification with time-varying forgettable factor, and combines predictive function control （PFC）with sliding-model control （SMC）. The proposed schemes, which have advantages of PFC and SMC, have the ability of tracking the reference signal and the strong robustness on the sliding surface. The robust performance of controlled system is improved and the computational speed is higher. The asymplotical stability of closed-loop system is analyzed. The simulation results show the effectiveness of the proposed algorithm.