含有自校正模型的加权多模型自适应控制

研究了含有大范围参数不确定性离散时间被控对象的加权多模型自适应控制问题(包括模型集构建和加权算法分析).通过构建含有自校正模型和多个固定模型的模型集覆盖并逼近被控对象,在模型输出误差可分的前提下,采用基于模型输出误差性能指标的加权算法,并依据固定模型中是否包含真实被控对象模型的不同情形分析加权算法的收敛性.在权值收敛的前提下,利用虚拟等价系统理论,分析了参数未知线性时不变和参数跳变的情形,在不依赖于特定局部控制算法的基础上,证明了此种模型集构建下的加权多模型自适应控制系统的稳定性和收敛性,放宽了先期加权多模型自适应控制系统稳定性分析中关于模型集构建的约束条件.最终,通过计算机MATLAB仿真,验证了此类加权多模型自适应控制系统的收敛性和闭环稳定性. 

Weighted multiple model adaptive control with self-tuning model

The issues of model set construction and weighting algorithm analysis in multiple model adaptive control of discrete-time systems with large parameter uncertainty are considered in this paper. First, to improve system performance by reducing the calculation burden and relaxing the convergence conditions for the classical weighting algorithm, a new weighting algorithm is adopted, which is based on the model output errors of the multi-model adaptive control system with a self-tuning model. Second, the weighting algorithm convergence is analyzed in two cases:when the model set contains the true model of plant and it tends to the fixed model, and when the model set does not contain the true model of plant and it tends to the self-tuning model. Third, according to the virtual equivalent system (VES) concept and methodology, the stability of weighted multiple model adaptive control (WMMAC) with a self-tuning model is presented under a unified framework. The analysis procedures for linear time-invariant (LTI) and parameter jump plants are independent of specific local control methods and weighting algorithm, which only require that each local controller stabilizes the corresponding local model, the output of the formed closed-loop system tracks the reference signal, and the weighting algorithm is convergent. The principal contributions of the paper are the analysis of global stability and the convergence of the overall system with a self-tuning model. Compared with the stability results of WMMAC in the early stage, the constraint condition that the model set only has fixed models is relaxed, which can enlarge the application range of the stability results in theory. In addition, because of the introduction of a self-tuning controller, the control performance of the system is significantly improved when the real model of the plant is not included in the model set. Finally, computer simulation results verify the feasibility and effectiveness of the proposed method.