基于有界扰动分区的多模型自适应控制

对于含有界扰动的离散时间系统,将有界扰动变化范围分割成若干小区间,针对这些区间建立多个自适应模型,并针对每一个自适应模型建立相应的控制器.给定一个指标切换函数,基于多个自适应模型控制器和给定的指标切换函数构成多模型自适应控制器.可以证明多模型自适应控制器能够保证闭环系统是输入输出有界稳定的.仿真实例表明,当被控对象的有界扰动随时间变化的时候,采用单一模型自适应控制,系统输出很难跟踪设定值,而多模型自适应控制器却极大地改善了控制品质.     

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.     

采用遗传优化自适应模糊PID控制球杆系统

为解决球杆系统动态、静态性能不高的问题,提出了遗传算法优化自适应模糊PID控制器的控制方法.该模型在拉格朗日方程建立球杆系统数学模型的基础上,采用遗传算法优化模糊控制规则、隶属函数和自适应PID参数.在GBB1004系统中建立了遗传算法优化后的自适应模糊PID控制器以及控制模型,并对该控制器进行实验验证.实验结果证明了遗传算法优化后的模糊控制器有效地减小了系统的超调量,缩短了系统的调节时间,能够较好地控制球杆系统.     

一类非线性多变量系统的多模型自适应控制

针对一类不确定的非线性多变量离散时间动态系统,提出了一种基于切换的多模型自适应控制方法.该控制方法的特点在于以下两个方面:首先,引入一个高阶差分算子使得非线性系统的非线性项的限制条件不再要求全局有界;其次,提出的控制方法由线性自适应控制器、神经网络非线性自适应控制器以及切换机构组成:线性控制器用来保证闭环系统的输入输出信号有界,神经网络非线性控制器用来改善闭环系统的性能,基于性能指标的切换机构在每一时刻选择性能指标较好的控制器对系统进行控制.理论分析和仿真实验说明了提出的多模型自适应控制方法的有效性.     

基于PD增益的模型参考自适应同步控制

在保证自适应控制精度的前提下,找出快速收敛的参数,从而使被控系统和参考系统达到同步.针对传统的PI控制器无法获得参数波动的系统的较高的控制性能.基于模型参考自适应控制,利用PD控制器可以预料到系统误差的方向的优点,设计了一种自适应同步控制器.仿真结果表明,该自适应同步控制器能够使被控系统和参考系统达到同步,并能够较精确地控制被控系统的输出,使被控系统满足系统所要求的动态性能.这对研究组合自适应控制策略和开展多模型自适应控制器的研究提供了基础.     

基于动态模型库的多模型切换控制

针对含有有界扰动和模型参数跳变的离散时间系统,提出基于动态模型库的多模型切换控制方法.在模型参数范围未知情况下,利用在线学习的多模型自适应控制算法自动建立多模型,并对模型库中的子模型进行优化.采用具有积分特性的指标函数作为切换准则.在每一采样时刻根据其最小值来选择与实际系统最接近的模型,并将基于此模型的控制器切换为当前控制器.文中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.     

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.     

一类离散时间非线性系统的多模型自适应控制

针对一类离散时间非线性被控对象,根据模型参数的变化范围,对被控对象建立多个模型,并针对每一模型设计控制器.基于模型的估计误差建立指标切换函数,每一采样时刻,利用指标切换函数选择最优模型,并将基于此模型的控制器切换为当前控制器.采用局部化技术,保证在不损失控制品质的同时,减少多模型自适应控制器的计算量.可以证明,多控制器相互切换时闭环系统是稳定的,同时由于多个模型的存在,控制品质得到了极大的改善.     

非线性系统的自组织多模型自适应逆控制

针对离散时间非线性系统,在分析基于自组织映射神经网络的多模型控制方法的基础上,提出了一种自组织多模型直接逆控制方法.并分析了控制误差的有界性.进一步,借鉴参数空间多模型方法的切换一自适应策略,在固定模型的基础上增加一个参数可调节的自适应逆模型.提高了稳态控制性能.仿真实例表明,对于变化较快的信号,自组织多模型直接逆控制器和自组织多模型自适应逆控制器都能进行有界跟踪,对于稳态信号,自组织多模型自适应逆控制器还能进行渐近跟踪.     

多变量周期系统的多模型二阶段自适应控制

对于一类参数未知的多变量周期系统,传统自适应控制方法存在参数收敛慢的问题,导致系统暂态响应差、控制效果不理想.因此,本文针对多变量周期系统设计了多模型二阶段自适应控制器.首先根据先验知识,确定不确定区域范围,并在不确定区域内建立多个自适应模型.然后根据李雅普诺夫理论得到第一阶段辨识方程;在第二阶段中,充分考虑辨识误差并确定了权值自适应律,以此获取虚拟模型以提高参数的收敛速度.接着,利用得到的虚拟模型参数设计了二阶段自适应控制器,在保证了系统稳定性的基础上,提高了系统的暂态性能.最后,给出的仿真结果表明多模型二阶段自适应控制器提高了参数的收敛速度,改善了系统的暂态性能.     

Stable indirect adaptive switching control for fuzzy dynamical systems based on T–S multiple models

A new indirect adaptive switching fuzzy control method for fuzzy dynamical systems, based on Takagi–Sugeno (T–S) multiple models is proposed in this article. Motivated by the fact that indirect adaptive control techniques suffer from poor transient response, especially when the initialisation of the estimation model is highly inaccurate and the region of uncertainty for the plant parameters is very large, we present a fuzzy control method that utilises the advantages of multiple models strategy. The dynamical system is expressed using the T–S method in order to cope with the nonlinearities. T–S adaptive multiple models of the system to be controlled are constructed using different initial estimations for the parameters while one feedback linearisation controller corresponds to each model according to a specified reference model. The controller to be applied is determined at every time instant by the model which best approximates the plant using a switching rule with a suitable performance index. Lyapunov stability theory is used in order to obtain the adaptive law for the multiple models parameters, ensuring the asymptotic stability of the system while a modification in this law keeps the control input away from singularities. Also, by introducing the next best controller logic, we avoid possible infeasibilities in the control signal. Simulation results are presented, indicating the effectiveness and the advantages of the proposed method.     

Dynamic adaptive trajectory tracking control of nonholonomic mobile robots using multiple models approach

This paper presents an approach for the trajectory tracking control of nonholonomic wheeled mobile robots (WMR) by combining one of the existing adaptive control methods and multiple identification models. The overall system includes two types of controllers in the control scheme. A kinematic controller developed by using kinematic model produces the required linear and angular velocities of the robot for tracking a reference trajectory. These required velocities are used to calculate the torques using an adaptive dynamic controller with multiple models. The proposed method uses the multiple models of the WMR for the identification of the dynamic parameters and performs switching between the given models. The models used in the identification are identical, except for the initial estimates of the parameters. By using an adaptive dynamic controller with multiple models of the WMR, enhancement in transient response is obtained. Stability analysis of the overall system is given, and simulation results are presented to demonstrate the effective performance of the adaptive control by using multiple models approach.     

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)     

Adaptive Control of A Servo System Based on Multiple Models

This paper presents an alternative technology for adaptive control of a DC motor servo system based on multiple models. A dynamic mechanical model of the controlled plant is built, where the unmeasurable variables can be estimated by a filter observer. According to the mechanical model, an adaptive controller is designed. Specific attention is given to the jumping parameters in the control process, which motivate the proposition of multiple models, including fixed models, identified model, and adaptive model, to approximate the global dynamic characteristics of the plant model. A model switching rule is proposed to select the optimal model matching the plant, and the identified and adaptive models are reset when switching occurs, minimizing the effect caused by jumping parameters. Simulation results demonstrate that the introduced scheme is superior to the conventional adaptive control in that it yields a significant improvement of transient stability and response speed as well as steady accuracy, guaranteeing better low‐speed performance.     

Multiple model adaptive control for switched linear systems: A two‐layer switching strategy

In this paper, the multiple model adaptive control scheme is first introduced into a class of switched systems. A switched multiple model adaptive control scheme is proposed to improve the transient behavior by resetting the controller parameters. Firstly, a finite‐time parameter identification model is presented, which greatly reduces the number of identification models. Secondly, a two‐layer switching strategy is constructed. The outer layer switching mechanism is to ensure the stability of the switched systems. The inner layer switching mechanism is to improve the transient behavior. Then, by using the constructed jumping multiple Lyapunov functions, the proposed adaptive control scheme guarantees that all the closed‐loop system signals remain bounded and the state tracking error converges to a small ball whose radius can be made arbitrarily small by appropriately choosing the design parameter. Finally, a practical example about model reference adaptive control of an electrohydraulic system using multiple models is given to demonstrate the validity of the main results.     

基于多模型的非线性系统自适应最小方差控制

对于一类典型的离散时间非线性系统, 提出了一种基于多模型的自适应最小方差控制方法. 通过在平衡点附近建立线性模型, 用径向基函数神经元网络来补偿建模误差和未建模动态, 形成了非线性系统的多模型表示. 采用了具有积分性质的切换指标函数作为切换法则和最小方差的控制方法构成了多模型自适应控制器. 仿真实验的结果表明了这种方法的有效性.     

Adaptive control using multiple models

Intelligent control may be viewed as the ability of a controller to operate in multiple environments by recognizing which environment is currently in existence and servicing it appropriately. An important prerequisite for an intelligent controller is the ability to adapt rapidly to any unknown but constant operating environment. This paper presents a general methodology for such adaptive control using multiple models, switching, and tuning. The approach was first introduced by Narendra et al. (1992) for improving the transient response of adaptive systems in a stable fashion. This paper proposes different switching and tuning schemes for adaptive control which combine fixed and adaptive models in novel ways. The principal mathematical results are the proofs of stability when these different schemes are used in the context of model reference control of an unknown linear time-invariant system. A variety of simulation results are presented to demonstrate the efficacy of the proposed methods     

基于局部化技术的多模型自适应控制

对一类含跳变参数的单输入单输出离散时间被控对象,建立由多固定模型和自适应 模型组成的多模型,并通过引入指标切换函数构成多模型自适应极点配置控制器,在保证闭 环系统稳定的前提下,改善系统瞬态响应.同时采用局部化(Localization)技术优化多模型模 型集,在不损失计算精度的前提下,大大减少了计算量,提高了计算速度.     

Adaptive receding horizon control for constrained MIMO systems

An adaptive control algorithm for open-loop stable, constrained, linear, multiple input multiple output systems is presented. The proposed approach can deal with both input and output constraints, as well as measurement noise and output disturbances. The adaptive controller consists of an iterative set membership identification algorithm, that provides a set of candidate plant models at each time step, and a model predictive controller, that enforces input and output constraints for all the plants inside the model set. The algorithm relies only on the solution of standard convex optimization problems that are guaranteed to be recursively feasible. The experimental results obtained by applying the proposed controller to a quad-tank testbed are presented.