基于在线优化的多模型自适应控制

针对常规多模型自适应控制中子模型数量过多问题,提出在线优化的多模型自适应控制算法。将整个控制系统分为基本工况级和控制模型级的两层递阶结构。在系统运行过程中,通过在线学习自动地建立多模型及相应的控制器,并对所建的动态模型库进行优化,以进一步减少子模型数量和计算时间。证明了该算法能够保证闭环系统的稳定性和跟踪误差的渐近收敛性。计算机仿真结果表明该算法的有效性。

Multiple models adaptive control based on online optimization

Aiming at the limitation of traditional multiple models adaptive control,such as a large number of sub-models,a multiple model adaptive control method based on online optimization is presented.The whole controlled system is divided into basic operating condition level and control model level.Multiple models and corresponding controllers are automatically built by online learning,and the built dynamic model bank is optimized so as to reduce both the sub-models in guantity and the computational load.The stability of the closed-loop system’s and its asymptotical convergence of tracking errors can be guaranteed.Simulation results show the efficiency of the proposed method.