MIMO非仿射非线性系统的自适应模糊控制

针对一类多输入多输出非仿射非线性系统,设计了一种自适应模糊H∞控制方案,该方案把自适应模糊控制和高增益观测器结合起来.利用多变量的隐函数定理,证明了非仿射系统控制器的存在性.通过设计高增益观测器,解决了系统的状态不可测量问题,实现系统的输出反馈控制,模糊自适应控制增强了系统在线逼近干扰及处理系统不确定的能力.仿真结果表明了控制方案的有效性及优越性.     

一类输入饱和非仿射非线性系统的非线性动态逆控制

输入饱和是实际系统中经常遇到的问题,很多已有的控制方法要求被控系统具有仿射结构.本文针对一类具有输入饱和的非仿射纯反馈非线性系统提出了一种基于奇异值摄动理论的非线性动态逆控制方法.首先构建一个快变子系统,在慢时间尺度下将非仿射非线性系统转换为具有仿射结构的线性系统,从而应用已有的控制算法实现控制目的.为了消除输入饱和带来的影响,建立一个中间子系统对理想控制量与输入饱和限制下控制量的差进行估计和补偿.所提出的控制方法不依赖于被控系统固有的时标分离特性,并能证明闭环系统指数跟踪参考轨迹.该方法具有良好的扩展性,可以根据实际需求与多种控制算法相结合.与动态面控制器和传统近似动态逆控制器的对比仿真结果验证了本文控制方法的有效性.     

多变量线性模型不确定系统终端滑模分解控制方法

针对线性多变量模型不确定系统系统,提出了一种终端滑模分解控制方法.通过状态变换和去耦合处理将系统转换为块能控标准型,它由值域空间子系统和稳定的零空间子系统组成.提出了特殊的终端滑模超曲面,采用滑模控制策略,使值域空间子系统的状态在有限时间内收敛至平衡点,随后稳定的零空间子系统渐近收敛至平衡点.所提出的方法对于维数较高系统的控制具有较大意义,可简化设计,实现递阶控制.仿真验证了该方法的有效性.     

神经网络解耦预测函数控制

结合解耦思想研究多变量系统预测函数控制。通过引入神经网络补偿环节，对多变量系统解耦，在此基础上，对解耦后各子系统进行单变量预测函数控制，以确定各个控制量。同时基于系统脉冲传递函数，得到单变量PFC控制器的显式表达式。仿真表明，该算法有较好的跟踪特性，对解决多变量系统的优化和控制具有一定的适用性。     

基于状态反馈精确线性化的三相四桥臂逆变器的控制

根据三相四桥臂逆变器的工作原理,应用开关函数建立了控制系统数学模型,引入开关周期平均算子将离散的系统转化为连续系统.根据系统的主要控制目标选取状态变量、输入变量和输出变量,得到适合于微分几何方法的3输入3输出的仿射非线性系统模型.根据非线性微分几何理论,从理论上证明了该模型满足多输入、多输出系统精确线性化的条件,推导出非线性状态反馈控制律.对非线性坐标变换后得到的线性系统,利用二次型最优控制策略时,根据无源性控制方法的思想,提出一种闭环系统能量函数,并推导出权矩阵的参数形式.将最优化得到的控制律进行逆变换来实现原系统的优化控制设计.仿真结果验证了该方法的有效性和正确性.     

多变量系统灰色预测函数控制方法研究

针对多变量时滞系统,提出一种以灰色模型为基础的多变量灰色预测函数控制策略,并给出多变量灰色预测函数控制算法.分析了灰色系统建模,灰色模型预测输出,和控制量计算方程的求解.仿真实验表明,该方法有较强的鲁棒性,快速性和强抗干扰能力强的特点.     

基于干扰观测器的解耦快速终端滑模控制

针对一类欠驱动系统跟踪控制问题,提出了一种基于非线性干扰观测器的全局解耦快速终端滑模控制(NDODGFTSMC)策略.将欠驱动系统分解成两个子系统分别设计全局快速终端滑模面,利用其中一个子系统滑模面的符号函数来构造中间变量,并将该变量引入到另一个子系统的滑模面中,构造出整个系统的滑模面,采用等效控制法和模糊双幂次趋近律...     

基于稳定性分析的一类欠驱动系统的滑模控制器设计

从稳定分析的角度提出了一种欠驱动系统的新型滑模控制方法．该方法将各个子系统的一个变量进行组合定义成一个中间变量，然后从这个中间变量出发构造滑模函数，通过求取总的控制量保证中间变量在有限时间内收敛到平衡点；进一步利用LaSalle不变性原理证明该收敛域内只有一个平衡点且是渐近稳定的．仿真实验进一步验证了该结论．     

关联不确定大系统的分散变结构控制

分散控制方法和变结构控制方法两者优点的结合使得分散变结构控制在大系统研 究中得到了广泛的重视.针对各个子系统均为多输入情况.研究了不确定性关联大系统的分 散变结构控制方法.基于开关平面的等价性,提出了关联大系统的分散滑动模态全局可达条 件.并针对子系统不确定性的界已知及未知两种情况,提出了分散变结构控制算法.该方法克 服了以往控制方法中需已知线性关联函数或不确定性关联的界的缺陷.     

非仿射系统的自学习滑模抗扰控制

针对一类单输入单输出(single-input single-output,SISO)非仿射非线性系统的控制问题,提出了一种自学习滑模抗扰控制方法.该方法用非线性光滑函数设计扩张状态观测器,实现SISO非仿射非线性系统内部不确定性和外部扰动的扩张状态估计,并将扩张状态观测器(extended state observer,ESO)与自学习滑模控制技术融为一体,实现SISO非仿射非线性系统的自学习滑模抗扰控制.该方法不依赖受控对象的数学模型,可以快速跟踪任意给定的参考信号.数值仿真试验表明了该方法响应速度快、控制精度高,具有很强的抗扰动能力,因而是一种鲁棒稳定性很强的控制方法,在SISO非仿射非线性系统控制领域具有重要作用.     

Adaptive neural network tracking control for a class of non-linear systems

This article extends the application of the adaptive neural network control to a new class of uncertain MIMO non-linear systems, which are composed of interconnected subsystems where each interconnected subsystem is in the non-affine pure-feedback form. Because both the variables which are used as virtual controllers and the actual controllers appear non-linearly in unknown functions of the MIMO systems, thus, this class of systems is difficult to control. The radial basis function neural networks are utilised to approximate the desired virtual controllers and the desired actual controllers which are obtained by using implicit function theorem. The salient property of the proposed approach is that the number of the adjustable parameters is less than the numerous alternative approaches existing in the literature. It is proven that, under appropriate assumptions, all the signals in the closed-loop system are uniformly bounded and the tracking errors converge to a small neighbourhood of the origin by appropriately choosing design parameters. The feasibility of the developed approach is verified by two simulation examples.     

Adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems

An adaptive fuzzy control approach is proposed for a class of multiple-input-multiple-output (MIMO) nonlinear systems with completely unknown nonaffine functions. The MIMO systems are composed of n subsystems and each of subsystems is in the nested lower triangular form. It is difficult and complicated to control this class of systems due to the existence of unknown nonaffine functions and the couplings among the nested subsystems. This difficulty is overcome by introducing some special type Lyapunov functions and taking advantage of the mean-value theorem, the backstepping design method and the approximation property of the fuzzy systems. The proposed control approach can guarantee that all the signals in the closed-loop system are bounded. A simulation experiment is utilized to verify the feasibility of the proposed approach.     

Direct adaptive fuzzy control of a class of MIMO non-affine nonlinear systems

An adaptive fuzzy control approach is proposed for a class of multiple-input–multiple-output (MIMO) nonlinear systems with completely unknown non-affine functions. The global implicit function theorem is first used to prove the existence of an unknown ideal implicit controller that can achieve the control objectives. Within this scheme, fuzzy systems are employed the approximate the unknown ideal implicit controller, and robustifying control terms are used to compensate the approximation errors and external disturbances. The adjustable parameters of the used fuzzy systems are deduced from the stability analysis of the closed-loop system in the sense of Lyapunov. To show the efficiency of the proposed controllers, two simulation examples are presented.     

Adaptive fuzzy semi‐decentralized control for a class of large‐scale nonlinear systems with unknown interconnections

In this paper, a general method is developed to generate a stable adaptive fuzzy semi‐decentralized control for a class of large‐scale interconnected nonlinear systems with unknown nonlinear subsystems and unknown nonlinear interconnections. In the developed control algorithms, fuzzy logic systems, using fuzzy basis functions (FBF), are employed to approximate the unknown subsystems and interconnection functions without imposing any constraints or assumptions about the interconnections. The proposed controller consists of primary and auxiliary parts, where both direct and indirect adaptive approaches for the primary control part are aiming to maintain the closed‐loop stability, whereas the auxiliary control part is designed to attenuate the fuzzy approximation errors. By using Lyapunov stability method, the proposed semi‐decentralized adaptive fuzzy control system is proved to be globally stable, with converging tracking errors to a desired performance. Simulation examples are presented to illustrate the effectiveness of the proposed controller. Copyright © 2006 John Wiley & Sons, Ltd.     

Decentralised output-feedback control design for switched fuzzy large-scale systems

This paper investigates the output-feedback control design problem for a class of switched Takagi–Sugeno fuzzy large-scale systems with non-measurable premise variables. The considered fuzzy large-scale systems consist of several interconnected subsystems with different switching modes and there exists the asynchronous switching between the system switching modes and the controller switching modes. A decentralised state observer is proposed to estimate the unmeasured states, and a new output-feedback decentralised control scheme is developed by using the state observers and the switching functions. Based on the theory of Lyapunov stability and average dwell-time methods, the sufficient conditions of ensuring the switched control system stability are proposed and proved, which are formulated in the form of linear matrix inequalities. An applicable example is provided to show the effectiveness of the obtained theoretical results.     

Adaptive tracking control for non-affine nonlinear systems with non-affine function possibly being discontinuous

A novel adaptive control scheme is presented for a class of non-affine nonlinear systems with non-affine nonlinear function possibly being discontinuous. A discontinuous condition for non-affine nonlinear systems is present to guarantee the controllability of system. The non-affine nonlinear function is modelled appropriately by using piecewise functions. Based on Lyapunov analysis method, the basic idea of invariant set theory is constructively introduced to prove the boundedness of all the signals in the closed-loop system. Finally, simulation example is provided to demonstrate the effectiveness of the proposed approach.     

Delay-dependent T–S fuzzy control for nonlinear interconnected systems: Using dithers as auxiliaries

In this study, a novel approach via the composite of fuzzy controllers and dithers is presented. According to this approach, we can synthesize a set of fuzzy controllers and find appropriate dithers to stabilize nonlinear multiple time-delay (NMTD) interconnected systems. A robustness design of model-based fuzzy control is first proposed to overcome the effect of modeling errors between the NMTD interconnected subsystems and Takagi–Sugeno (T–S) fuzzy models. In terms of Lyapunov's direct method, a delay-dependent stability criterion is then derived to guarantee the asymptotic stability of NMTD interconnected systems. Based on this criterion and the decentralized control scheme, a set of model-based fuzzy controllers is synthesized via the technique of parallel distributed compensation (PDC) to stabilize the NMTD interconnected system. When the designed fuzzy controllers cannot stabilize the NMTD interconnected systems, a batch of high-frequency signals (commonly referred to as dithers) is simultaneously introduced to stabilize it. If the frequencies of dithers are high enough, the outputs of the dithered interconnected system and those of its corresponding mathematical model–the relaxed interconnected system can be made as close as desired. This makes it possible to obtain a rigorous prediction of the stability of the dithered interconnected system based on the one of the relaxed interconnected system. Finally, a numerical example is given to illustrate the feasibility of our approach.     

Adaptive fuzzy controller design with observer for a class of uncertain nonlinear MIMO systems

In this paper, an adaptive fuzzy control approach is proposed to stabilize a class of uncertain nonlinear MIMO systems with the unmeasured states and the external disturbances. The fuzzy logic systems are used to approximate the unknown functions. Because it does not required to assume that the system states are measurable, it needs to design an observer to estimate the system unmeasured states. The considered MIMO systems are more general, i.e. they consist of N subsystems and each subsystem is in the non‐affine form. The stability of the closed‐loop system is verified by using Lyapunov analysis method. Two simulation examples are utilized to verify the effectiveness of the proposed approach. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

一类纯反馈非线性系统的简化自适应神经网络动态面控制

针对一类完全非仿射纯反馈非线性系统,提出一种简化的自适应神经网络动态面控制方法.基于隐函数定理和中值定理将未知非仿射输入函数进行分解,使其含有显式的控制输入;利用简化的神经网络逼近未知非线性函数,对于阶SISO纯反馈系统,仅一个参数需要更新;动态面控制可消除反推设计中由于对虚拟控制反复求导而导致的复杂性问题.通过Lyapunov稳定性定理证明了闭环系统的半全局稳定性,数值仿真验证了方法的有效性.     

一类非线性系统的自适应滑模模糊控制

针对一类具有多个子系统的欠驱动非线性系统提出了一种自适应滑模模糊控制方法. 首先通过分析模糊控制与边界层滑模控制的相似性,提出了滑模模糊控制方法;然后根据滑模 面斜率和各子系统控制对于系统动态性能的影响,分别采用模糊推理根据系统状态自动地实时 调节滑模面斜率和各子系统在系统控制中的作用;最后通过简单的滑模模糊控制器实现对具有 多个子系统的欠驱动非线性系统的控制.将该方法应用于吊车的运输控制中,仿真结果证明了 其有效性和鲁棒性.