非最小相位逐维定位多模型自适应解耦控制器

针对多变量系统中多个参数同时跳变导致模型数目巨大、计算时闻长等问题.提出了采用逐维定位技术的多模型自适应解耦控制器.该方法将多维空间的并行寻优问题转化为多个一维空间的串行寻优问题,可大大减少系统模型集的数量.针对非最小相位系统,将系统的耦合作用视为可测干扰,采用前馈方法予以消除.最后给出全局收敛性分析.仿真结果表明当采用相同数目的模型时,其控制效果明显优于常规多模型控制器.     

非最小相位逐维定位多模型自适应解耦控制器

针对多变量系统中多个参数同时跳变导致模型数目巨大,计算时间长等问题,提出了采用逐维定位技术的多模型自适应解耦控制器.该方法将多维空间的并行寻优问题转化为多个一维空间的串行寻优问题,可大大减少系统模型集的数量.针对非最小相位系统,将系统的耦合作用视为可测干扰,采用前馈方法予以消除.最后给出全局收敛性分析.仿真结果表明当采用相同数目的模型时,其控制效果明显优于常规多模型控制器.

     

多模型自适应控制的分层递阶构造与覆盖性质分析

针对一类非最小相位系统,设计一种多模型自适应控制器.该控制器由固定控制器模型、常规自适应模型和可重新赋值自适应模型构成.固定控制器模型采用分层递阶结构用来减少模型集的数目,根据切换指标选出的上一层最优控制器,动态设计本层固定控制器模型实现对其参数变化范围的覆盖.该控制器采用直接自适应算法,通过加权多项式的选取,消除了稳态误差.文末对系统的覆盖性、模型数目等进行了分析.仿真结果表明当采用相同数目的模型时,其控制效果明显优于常规多模型控制器.     

一类非最小相位系统分层递阶多模型解耦控制器

针对多模型控制方法中模型数目巨大,计算时间长等问题,提出了分层递阶结构多模型自适应前馈解耦控制器.该控制器中固定模型集采用分层递阶结构,基于切换准则逐层搜索最优模型,动态构造下一层固定模型集实现完全覆盖.最后一层添加自适应模型消除稳态误差.针对非最小相位系统,将系统的耦合作用视为可测干扰,采用前馈方法予以消除.最后给出全局收敛性分析.仿真结果表明,与常规多模型控制方法相比,极大地减少了固定模型的数量.而当模型数目相同时,系统的暂态响应、解耦效果得到极大改善.     

多模型分层递阶自适应前馈解耦控制器

针对参数跳变系统,提出一种基于分层递阶结构的多模型自适应前馈解耦控制器.该控制器采用多模型方法来提高系统的暂态性能;采用自适应方法消除系统的稳态误差,采用分层递阶结构减少系统模型集的数量和计算时间.为了在分布式计算机集散控制系统(DCS)中得到应用,该控制器根据耦合的形成机理和DCS的结构特点,将系统变量之间的耦合作用视为可测干扰,采用前馈结构予以消除.通过加权多项式的选取,不仅实现了极点配置,而且可以动态解耦.最后给出了全局收敛性分析.仿真结果表明,与常规多模型控制方法相比,大大减少了固定模型的数量;而当模型数目相同时,系统的暂态响应、解耦效果都大为改善.     

随机系统的多模型直接自适应解耦控制器

针对多变量离散时间随机系统, 提出了一种采用广义最小方差性能指标的多模型直接自适应解耦控制器. 该多模型控制器由多个固定控制器和两个自适应控制器构成. 固定控制器用以覆盖系统参数的可能变化范围, 自适应控制器用以保证系统的稳定性和提高暂态性能. 该多模型控制器利用矩阵的伪交换性和拟Diophantine方程性质, 基于广义最小方差性能指标, 将随机系统辨识算法和最优控制器设计相结合, 直接辨识出控制器的参数, 通过广义最小方差性能指标中加权多项式的选取,不但实现了多变量系统的动态解耦控制, 而且消除了稳态误差、配置了闭环极点. 文末给出了全局收敛性分析. 仿真结果表明该方法明显优于常规自适应控制器.     

基于切换的多模型二阶段自适应控制器设计

针对一类具有参数跳变特性的离散时间系统,设计一类基于切换策略的新型多模型二阶段自适应控制器.该控制器首先将系统不确定参数的变化空间划分为多个子空间,在每个空间内建立多个自适应模型.为了克服多模型退化,保持模型的多样性以应对参数跳变,采用带约束的二阶段自适应方法对未知参数进行实时估计,并据此设计相应的子控制器;然后基于切换策略,选取该时刻的最优子控制器作为系统的控制器,从而减小系统暂态误差,提高系统动态性能;最后进行数值仿真研究,仿真结果表明该控制器结合了切换机制和二阶段自适应的优点,在相同模型数量的情况下,能够快速逼近参数跳变以后系统新的工作点,显著地缩短系统的过渡过程,提高暂态性能.     

基于参数辨识的自适应解耦控制算法的研究

多变量模型的复杂结构、强耦合性、被控对象参数的未知、慢时变等问题要求控制器必须具有良好的自适应性,针对以上问题提出了一种基于改进的广义最小方差闭环自适应解耦控制器实现更好的自适应,其由参数可调的控制器和自适应控制律组成,此控制器通过将闭环系统方程的传递函数矩阵等于期望的对角矩阵来实现解耦,同时改进的辨识算法可进行在线辨识控制器的参数实现同步自适应解耦。通过以CARMA为多变量控制模型,采用该方法进行仿真有效的解决了多变量之间的耦合性。结果表明该方法能够适应相应的变化,跟踪性能较好,且具备良好的解耦能力,进而保证了闭环系统的稳定性,从而验证了此方法能够效提高控制系统的稳定性和鲁棒性。     

一类非最小相位系统的多变量多模型解耦控制器

为解决系统暂态响应变差问题，提出一种基于多模型切换的多变量直接自适应控制器，通过加权多项式矩阵的选择，可消除稳态误差，实现静态解耦控制，该控制器由多个参数已知固定模型和两个自适应模型构成，多个固定参数控制器模型可由系统参数模型通过映射直接得到，并与邻城一起完全覆盖控制器参数模型集，仿真结果表明，对于非最小相位系统，暂态响应可得到明显改善。     

一类非线性系统的多模型神经网络解耦控制器

针对多变量非线性离散时间系统设计多模型神经网络解耦控制器，在每个平衡点处用一神经网络离线辨识非线性系统的线性部分，利用另一神经网络在线辨识非线性部分，将非线性部分视为可测干扰并采用前馈的方法予以消除，所有平衡点处得到的系统模型汇集起来构成多模型集，在每一采样时刻基于切换指标选出最优模型作为当前模型，并据此设计解耦控制器实现控制，仿真结果表明系统在多个平衡点处仍然可以得到较好的控制效果。     

分层递阶多模型自适应解耦控制器

To solve the problem such as too many models, long computing time and so on, a hierarchical multiple models direct adaptive decoupling controller is designed. It consists of multiple levels. In the upper level, the best model is chosen according to the switching index. Then multiple fixed models are constructed on line to cover the region which the above chosen fixed model lies in. In the last level, one free-running and one re-initialized adaptive model are added to guarantee the stability and improve the transient response. By selection of the weighting polynomial matrix, it not only eliminates the steady output error and places the poles of the closed loop system arbitrarily, but also decouples the system dynamically. At last, for this multiple models switching system, global convergence is obtained under common assumptions. Compared with the conventional multiple models adaptive controller, it reduces the number of the fixed models greatly. If the same number of the fixed models is used, the system transient response and decoupling result are improved. The simulation example illustrates the power of the derived controller.     

Multivariable direct adaptive decoupling controller using multiple models and a case study

In this paper, a multivariable direct adaptive controller using multiple models without minimum phase assumption is presented to improve the transient response when the parameters of the system jump abruptly. The controller is composed of multiple fixed controller models, a free-running adaptive controller model and a re-initialized adaptive controller model. The fixed controller models are derived from the corresponding fixed system models directly. The adaptive controller models adopt the direct adaptive algorithm to reduce the design calculation. At every instant, the optimal controller is chosen out according to the switching index. The interaction of the system is viewed as the measured disturbance which is eliminated by the choice of the weighing polynomial matrix. The global convergence is obtained. Finally, several simulation examples in a wind tunnel experiment are given to show both effectiveness and practicality of the proposed method. The significance of the proposed method is that it is applicable to a non-minimum phase system, adopting direct adaptive algorithm to overcome the singularity problem during the matrix calculation and realizing decoupling control for a multivariable system. Supported by the National Natural Science Foundation of China (Grant Nos. 60504010, 60864004), the National High-Tech Research and Development Program of China (Grant No. 2008AA04Z129), the Key Project of Chinese Ministry of Education (Grant No. 208071), and the Natural Science Foundation of Jiangxi Province (Grant No. 0611006)     

Improved transient performance of nonlinear adaptive backsteppingusing estimator resetting based on multiple models

It is proved that the the transient performance of nonlinear adaptive backstepping can be improved by re-setting the parameter estimator, without loss of stability. The estimator re-setting algorithm is based on multiple-model adaptive control, where a number of models with fixed parameter vectors are monitored online in order to detect a parameter vector that gives a negative jump in the control Lyapunov function when replacing the estimate provided by the standard adaptation law. An application to wheel slip control is studied     

Sensitivity in the decoupling of non-linear control systems†

The problem of decoupling the inputs and outputs of non-linear control systems is considered for the situation where the system parameters are subject to variations or disturbances. The exact decoupling is obtained by an adaptive state feedback controller which requires full knowledge of the instantaneous values of the system parameters. By utilizing the parameter sensitivity concept an approximate adaptive decoupling controller is synthesized in the present paper which is shown to decouple the system to first (or higher) order in the variations of the system parameters from their known nominal values. The sensitivity equations which generate the state sensitivity function required for the synthesis are derived. The results of the paper actually provide the tools for reducing the system sensitivity to parameter variations when the final goal is the improvement of decoupling.