摘要
论文研究了非线性离散时间系统无模型自适应控制理论中的若干问题及应用,丰富和完善了无模型自适应控制理论。论文主要研究内容和创新点总结如下:
一、证明了基于偏格式线性化方法的无模型自适应控制方案(PFL-MFAC)的输出调节问题的BIBO稳定性和收敛性:并将相应的结果应用到永磁直线电机速度控制和三容水箱液位控制中。
二、针对滞后非线性离散时间系统,提出了基于紧格式线性化方法的改进无模型自适应控制方案(TFL-iMFAC)和基于偏格式线性化方法的改进无模型自适应控制方案(PFL-iMFAC),理论分析及仿真验证了TFL-iMFAC和PFL-iMFAC的有效性。
三、针对一般非线性离散时间系统,基于迭代轴上的偏格式线性化方法和迭代轴上的全格式线性化方法,提出了基于偏格式线性化方法的无模型自适应最优迭代学习控制方案(PFL-MFAOILC)和基于全格式线性化方法的无模型自适应最优迭代学习控制方案(FFL-MFAOILC),证明了所提出方案的稳定性和收敛性,并将相应的结果应用到快速路入口匝道控制中。
四、针对一般非线性离散时间系统,提出了动态时变参数的迭代辨识方案,证明了该种迭代学习辨识方案下的算法收敛性和鲁棒性,并将相应结果应用到快速路的宏观交通流模型参数辨识中。
五、将一种基于投影算法的参数化自适应迭代学习控制方案(P-DAILC)应用到快速路交通系统和永磁直线电机(PMLM)控制系统中,仿真结果表明当系统的初始条件和参考轨迹均迭代变化时,该控制方案仍能保证跟踪误差沿迭代轴的渐近收敛性能。
This dissertation focuses on some issues on the model-flee adaptive control theory and its applications for nonlinear discrete-time systems.The main work and key innovations are summarized as the following:
1.The stability and convergence of the model flee adaptive control based on partial form linearization(PFL-MFAC) are proved,and corresponding results are applied to permanent magnet linear motor(PMLM) and three-tank water.
2.The improved MFAC based on tight form linearization(TFL-iMFAC) and improved MFAC based on partial form linearization(PFL-iMFAC) are developed for a class of large lag nonlinear discrete-time system.Theoretical analysis and simulation results show that the TFL-iMFAC and PFL-iMFAC are efficient.
3.Based on the partial form linearization method along iteration axis and the full form linearization method along iteration axis,the model flee adaptive optimal iterative learning control based on PFL(PFL-MFAOILC) and the model free adaptive optimal iterative learning control based on FFL(FFL-MFAOILC) are developed for a class of SISO nonlinear discrete-time systems.The stability and convergence of the PFL-MFAOILC and FFL-MFAOILC are proved,and the corresponding results are applied to freeway traffic system.
4.An iterative learning identification method is developed to estimate the parameters of the more general nonlinear discrete-time system.The stability and convergence of the identification algorithm are proved,and the corresponding results are applied to freeway traffic system.
5.A novel parametric discrete-time adaptive iterative learning control(P-DAILC) by cooperating projection algorithm is applied to freeway traffic system and PMLM control system,and the simulation results show that the P-DAILC can deal with random initial conditions and iteration-varying reference trajectories,in the sequel achieving an almost perfect tracking performance over a finite interval.
引文
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