一类非仿射非线性系统混合自适应模糊控制

针对一类单输入单输出非仿射非线性系统控制方向未知情况下的控制器设计问题,提出一种混合自适应模糊控制器设计方案。该方案利用泰勒级数将非仿射系统转换为仿射系统,通过模糊逻辑系统逼近该仿射系统中的未知函数,基于此构造模糊控制器,同时利用跟踪误差和模型预测误差设计参数自适应律。利用Lyapunov稳定性定理证明运用所设计的控制器可以保证系统跟踪误差收敛,同时证明了该方案相比已有方案系统跟踪误差具有更快的收敛速度。仿真结果验证了所提控制器设计方案的有效性。     

一类不确定非线性系统的自适应模糊滑模控制

针对一类不确定非线性系统自适应模糊控制中,为了保证系统稳定性而附加监督控制问题,根据滑模控制原理并利用模糊系统的逼近能力,提出了一种Ⅰ型间接自适应模糊滑模控制方法。该方法取消了监督控制,用滑模控制器增加了逼近误差的自适应补偿,李雅普诺夫稳定性理论分析证明,控制系统全局稳定且跟踪误差收敛到零。将这种控制器应用到过程控制的典型对象液位控制中,仿真结果表明了该控制器的有效性和可行性。     

一类模型不确定非线性系统的反演预测控制

针对一类模型不确定的单输入单输出仿射非线性系统,设计一种使得闭环系统稳定且滚动时域性能指标在线最小化的预测控制器。运用反演(Backstepping)设计思想获得具有待定参数的控制器表达式,其误差导数中的未知函数采用模糊逻辑系统来逼近,通过直接估计模糊系统最优参数向量的范数上界来设计控制器和自适应律,大大降低了在线计算量。理论证明该方法设计的控制器保证了闭环系统所有信号是半全局有界的,并且跟踪误差收敛于零的某一邻域。仿真算例验证了所提出算法的有效性。     

基于自适应模糊控制的X-Y平台滑模控制

首先描述了X-Y数控平台系统及模型建立,然后提出了一种自适应模糊滑模控制的力/位置控制方法.在位置控制部分采用自适应模糊控制去逼近不确定项,用滑模控制器对逼近误差及外部的干扰进行补偿控制;力控制回路采用小脑模型神经网络(CMAC)确定相应模糊调整因子的模糊控制器,把力控制器输出作为位置控制的修正值,通过提高位置的控制精度达到控制力的目的.仿真结果表明,该方法使平台系统的控制精度得到了改善,并对外界环境接触刚度的变化具有一定的自适应性和鲁棒性.     

基于自适应模糊滑模控制的机器人轨迹跟踪算法

针对控制参数的不确定性以及存在未知外部扰动情况下移动机器人的轨迹跟踪问题,提出一种基于光滑非线性饱和函数的自适应模糊滑模轨迹跟踪控制算法。通过建立不确定非线性移动机器人运动控制模型,利用自适应模糊逻辑系统构建自适应模糊滑模控制器。为了增强轨迹跟踪控制算法对随机不确定外部扰动适应能力的同时削弱滑模控制算法中的输入抖振现象,利用有界输入有界输出(BIBO)稳定的方法,通过带有自适应调节算法的模糊系统对滑模控制律中非线性函数项进行自适应逼近,并设计了模糊系统中可调参数的自适应控制律,保证了控制系统的稳定与收敛。实验结果表明,所设计的控制器对系统参数不确定性和外界扰动均具有较强的轨迹跟踪性能和鲁棒性。与传统的滑模控制算法相比,该算法不仅能有效减小输入抖振而且轨迹跟踪控制精度提高了18.89%。     

非线性模糊自适应直接广义预测控制

针对广义预测控制(GPC)计算量大的缺陷,提出一种模糊自适应直接广义预测控制(FDGPC)方法.该方法利用中值定理将一类非线性系统等价表示为时变线性系统,通过模糊逻辑系统直接设计预测控制器,利用三次样条基函数多项式逼近广义误差时变系数,基于广义误差估计值对控制器参数θu和广义误差估计值中的未知向量θe'进行自适应调整.理论证明了该方法可使广义误差收敛到原点的一个小邻域内.仿真结果验证了此方法的有效性.     

可控串补自适应模糊阻尼控制策略研究

早期的模糊控制器设计,主要依赖于专家经验,而且只是采用近似推理的方法。针对可控串补非线性系统,设计了一种直接自适应模糊控制器。建立模糊控制器逼近误差和控制器参数之间的线性关系,用Lyapunov稳定性理论设计参数的自适应律,不仅调节模糊规则结论参数,同时调节隶属函数的参数。将该控制策略应用于含可控串补电力系统中,提出可控串补自适应模糊阻尼控制器的设计方法。仿真表明该控制器对系统发电机摇摆具有良好的阻尼作用,提高了系统的稳定性。     

一种带有干扰观测器的自适应模糊控制方案

以LT50m模型为研究载体,针对一类多输入多输出非线性系统研究了一种带有干扰观测器的自适应模糊控制方案。考虑到实际系统中存在外界扰动不确定和数学模型不精确的情况,首先设计干扰观测器来估计系统的不可测状态;然后利用模糊系统的万能逼近性,设计一种具有监督控制能力的自适应模糊控制器;最后对其进行仿真分析。结果表明,该控制方案不仅能满足控制准确度的要求,而且对系统参数及非参数等方面的不确定性有很好的鲁棒性。     

一类非线性系统模糊自适应鲁棒控制

针对一类不确定性非线性系统的传统模糊自适应控制方案存在的缺陷,利用第一类模糊逻辑系统直接作为非线性系统控制器,提出了一种模糊自适应鲁棒控制策略.在传统方案的基础上加入鲁棒补偿项,取消监督控制项,进一步放宽稳定条件到最小近似误差有界,利用Lyapunov稳定性理论证明了系统的全局稳定性.通过对二阶混沌对象仿真结果表明,鲁棒控制有效地补偿了稳态误差,解决了单一模糊自适应控制寻优空间有限问题.     

基于摩擦补偿的永磁球形电机自适应模糊控制

针对摩擦可能引起永磁球形电机控制系统品质恶化的问题,提出樽糊逼近的摩擦补偿自适应控制方案.首先,通过卡尔丹角(Carden)旋转,建立考虑摩擦项的转子动力学模型,分别基于已知模型和名义模型设计控制方案.基于名义模型的控制方案采用改进的模糊补偿自适应控制器分别补偿摩擦项以及系统不确定性因素,以减少模糊规则的数目,控制律参数基于李亚普诺夫稳定性理论自适应调节,控制方案保证了闭环系统的稳定性.仿真试验结果表明,该控制方案能有效实现对摩擦项等不确定信号的补偿,从而提高球形电机的跟踪控制性能.     

Adaptive fuzzy logic load frequency control of multi-area power system

Based on indirect adaptive fuzzy control technique, a new load frequency control (LFC) scheme for multi-area power system is proposed. The power systems under study have the characterization of unknown parameters. Local load frequency controller is designed using the frequency and tie-line power deviations of each area. In the controller design, the approximation capabilities of fuzzy systems are employed to identify the unknown functions, formulate suitable adaptive control law and updating algorithms for the controller parameters. It is proved that the proposed controller ensures the boundedness of all variables of the closed-loop system and the tracking error. Moreover, in the proposed controller an auxiliary control signal is introduced to attenuate the effect of fuzzy approximation error and to mitigate the effect of external disturbance on the tracking performance. Simulation results of a three-area power system are presented to validate the effectiveness of the proposed LFC and show its superiority over a classical PID controller.     

Adaptive neural finite-time output-feedback tracking control for nonlinear stochastic nonstrict-feedback systems with input saturation

In this paper, an observer-based adaptive neural output-feedback control scheme is developed for a class of nonlinear stochastic nonstrict-feedback systems with input saturation in finite-time interval. The mean value theorem and the property of the smooth function are applied to cope with the difficulties caused by the existence of input saturation. According to the universal approximation capability of the radial basis function neural network, it will be utilized to compensate the unknown nonlinear functions. Based on the state observer, the finite-time Lyapunov stability theorem, we propose an adaptive neural output-feedback control scheme for nonlinear stochastic systems in nonstrict-feedback form. The developed controller guarantees that the system output signal can track the given reference signal trajectory, and all closed-loop signals are semi-globally finite-time stability in probability. The observer errors and the tracking error can converge to a small neighborhood of the origin. Finally, simulation results demonstrate the effectiveness of the developed control scheme.     

Novel fuzzy adaptive finite-time nonsmooth control for uncertain nonlinear systems

In this article, a novel fuzzy adaptive finite-time nonsmooth controller is developed to handle the finite-time tracking problem for a class of uncertain nonlinear systems. Different from traditional fuzzy adaptive approximation methods, proposed method contains only one adaptive parameter, no matter how many states there are in the system. By constructing a new Lyapunov function with prescribed performance bound, the transient and steady performances of control system can be ensured. Further, based on a criterion of finite-time semiglobal practical stability and backstepping technology, a novel fuzzy adaptive finite-time nonsmooth control method is designed. It can be demonstrated that proposed control can effectively ensure tracking error tends to small neighborhood in a finite time. Finally, two examples have been simulated by the proposed control method, and it shows effective tracking performance.     

自适应模糊滑模控制在伺服电动机系统中的应用

设计了一种带积分滑模面的自适应模糊滑模控制系统,并将其应用于伺服电动机的位置和速度控制系统中.自适应模糊滑模控制系统是由模糊控制和hitting控制组成的,在模糊控制设计中,用模糊控制器来模拟反馈线性化控制律;在hitting控制设计中,用hitting控制器来补偿反馈线性化控制律和模糊控制器之间的误差.调节算法是从Lyapunov稳定性理论得到的,从而可以保证系统的稳定性.而且为缓解对近似误差界的需要,提出了一种误差估计机制来实时观测近似误差界.仿真结果证明了所设计的系统可以得到令人满意的跟踪性能,而且对参数变化和外部负载扰动具有鲁棒性.     

Finite-time adaptive fuzzy command filtering control for stochastic nonlinear systems with input quantization

This article focuses on the finite-time adaptive fuzzy control problem based on command filtering for stochastic nonlinear systems subject to input quantization. Fuzzy logic systems are employed to estimate unknown nonlinearities. In the control design, the hysteretic quantized input is decomposed into two bounded nonlinear functions, which solves the chattering problem. Meanwhile, an adaptive fuzzy controller is presented by the combination of command filter technique and backstepping control, which eliminates the computational complexity existing in traditional backstepping design. Under the proposed adaptive mechanism, all the closed-loop signals remain bounded while the desired system performance can be realized within finite time. The main significance of this work is that (1) the filtering error can be solved on the basis of the designed compensating signals; (2) the requirement of adaptive parameters is decreased to only one, which simplifies the controller design process and may improve the control performance. Two simulation examples are used to validity of the developed scheme.     

A combined backstepping and dynamic surface control to adaptive fuzzy state‐feedback control

This paper studies an adaptive fuzzy dynamic surface control for a class of nonlinear systems with fuzzy dead zone, unmodeled dynamics, dynamical disturbances, and unknown control gain functions. The unknown system functions are approximated by the Takagi‐Sugeno–type fuzzy logic systems. There are 3 main features for the presented systematic design scheme. First, by adopting an integrated method, a novel adaptive fuzzy controller is constructed for the nonlinear system with fuzzy dead zone. Second, only 3 online learning parameters need to be tuned, which significantly reduces the computation burden. Third, the possible controller singularity problem in some of the existing adaptive control methods with feedback linearization techniques can be avoided. On the basis of the backstepping technique and dynamic surface control, all the signals of the closed‐loop system are guaranteed to be semiglobally uniformly ultimately bounded. Finally, 2 simulation examples are provided to illustrate the effectiveness of the proposed scheme.     

一种补偿动态摩擦的自适应模糊控制方法

机械系统的摩擦力精确数学模型很难建立，一直是人们所关注的问题。文中针对小型DC电机采用自适应模糊系统在线逼近摩擦模型并将辨识结果作为PD算法的补偿项。在控制方法上，文中采用了基于自适应模糊补偿的PD控制器．在系统证明上，从李雅普诺夫函数中导出了自适应参数并且分析了闭环系统跟踪误差的有界性。在算法实现上，利用Mauab对文中的方法及证明的有效性进行了验证。文中的方法也可应用到其它机械系统的摩擦补偿建模与控制上，如机器人系统。     

一类非仿射非线性系统的自适应模糊控制

为了讨论一类非仿射非线性系统自适应模糊控制问题,利用有关隐函数定理和泰勒公式,将系统由非仿射型转变为仿射型,基于滑模控制原理,并运用模糊逻辑系统对未知函数进行在线逼近,提出了一种具有监督器的自适应模糊控制方法。该方法在考虑到外界干扰的情况下,通过监督控制器保证闭环系统所有信号有界,通过引入最优逼近误差的自适应补偿项来消除建模误差的影响。通过Lyapunov方法,证明了跟踪误差收敛到零,仿真结果表明了该方法的有效性。