Adaptive sliding mode-like fuzzy logic control for nonlinear systems

Sliding mode-like fuzzy logic control （SMFC） algorithm for nonlinear systems is presented in this paper. Firstly dead zone parameters of sliding mode control （SMC） are selftuned by proper adaptive laws and then combined into fuzzy logic system （FLS） to compose the opportune fuzzy logic control （FLC）, which is equivalent to the predesigned SMC controller with self-tuning parameters. Robustness and invariance to the uncertainties of the closed-loop systems are improved and chattering of the SMC is eliminated. Finally simulation results of numerical examples show that the proposed control algorithm is efficient and feasible.     

非线性离散时间系统的自适应模糊补偿控制

针对一类非线性离散时间系统,提出一种自适应模糊逻辑补偿控制方案.控制律由跟踪控制律和逼近误差补偿控制律两部分组成,利用模糊逻辑系统对系统参数扰动和外界干扰进行自适应补偿,由模糊滑模控制律实现对模糊逻辑系统逼近误差的进一步补偿.所设计的控制器可保证闭环系统一致最终有界.将该控制器用于月球探测车动态转向系统中,仿真结果表明了该方法的有效性.     

一类基于模糊逻辑系统的非线性系统自适应跟踪控制

利用非线性不确定系统的动态数学模型和模糊逻辑系统，对不确定性的输出信息，设计出被控系统的自适应鲁棒跟踪控制器和模糊逻辑系统参数估计的自适应律．在较弱的假设条件下，证明了这种控制器能使被控系统的状态及参数估计误差一致终极有界．仿真实例表明，所提出的方法是有效的．     

Dynamic non-Singleton fuzzy logic systems for nonlinear modeling

We investigate dynamic versions of fuzzy logic systems (FLSs) and, specifically, their non-Singleton generalizations (NSFLSs), and derive a dynamic learning algorithm to train the system parameters. The history-sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. This is illustrated through an example in nonlinear dynamic system identification. Since dynamic NSFLS's can be considered to belong to the family of general nonlinear autoregressive moving average (NARMA) models, they are capable of parsimoniously modeling NARMA processes. We study the performance of both dynamic and static FLSs in the predictive modeling of a NARMA process     

间歇精馏过程的模糊逻辑与增益自调整PID混合控制

针对间歇精馏过程的强非线性和非平稳时变特性,结合模糊逻辑控制和增益自调整PID控制的优点,提出了一种模糊逻辑和增益自调整PID混合控制的先进控制策略,详细推导了其控制算法,设计了相应的控制器,并在EuroBEEB工控机上用实时BASIC语言编程实现,对一套甲醇/水二元间歇精馏塔的塔顶浓度进行了推断控制实验,获得了比单独采用模糊逻辑控制时更好的控制结果。这说明,模糊逻辑和增益自调整PID混合控制是强非线性和非平稳时变过程的一种有效控制策略。     

Adaptive control of a class of nonlinear systems with fuzzy logic

An adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems, for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs fuzzy systems, which are expressed as a series expansion of basis functions, to adaptively compensate for the plant nonlinearities. Global asymptotic stability of the algorithm is established in the Lyapunov sense, with tracking errors converging to a neighborhood of zero. Simulation results for an unstable nonlinear plant are included to demonstrate that incorporating the linguistic fuzzy information from human experts results in superior tracking performance     

Adaptive sliding mode control for MIMO nonlinear systems based on fuzzy logic scheme

In this study an indirect adaptive sliding mode control (SMC) based on a fuzzy logic scheme is proposed to strengthen the tracking control performance of a general class of multi-input multi-output (MIMO) nonlinear uncertain systems. Combining reaching law approach and fuzzy universal approximation theorem, the proposed design procedure combines the advantages of fuzzy logic control, adaptive control and sliding mode control. The stability of the control systems is proved in the sense of the Lyapunov second stability theorem. Two simulation studies are presented to demonstrate the effectiveness of our new hybrid control algorithm.     

Robust load-frequency control for uncertain nonlinear power systems: A fuzzy logic approach

A new robust load-frequency control (LFC) methodology is proposed for controlling uncertain nonlinear power systems. Critical nonlinearity in the power system—the valve position limit on the governor, and the parametric uncertainty are concerned. The Takagi-Sugeno fuzzy model of the power system under consideration is first constructed to design the robust fuzzy-model-based LFC. Sufficient conditions for the robust asymptotic convergence of the frequency deviation are then provided in terms of linear matrix inequalities. Boundedness of the other system variables is also studied to ensure justifiable grounds for use of the proposed LFC method. Simulation results convincingly validate the effectiveness of the novel LFC design scheme and the theoretical discussions, which give a positive answer to the quality control of the electric energy.     

仿手工雕刻的非线性模糊控制器的设计

本文介绍了一种自学习非线性模糊控制器的设计，以及它在雕刻加工中的具体应用。     

A multiregion fuzzy logic controller for nonlinear process control

Although a fuzzy logic controller is generally nonlinear, a PI-type fuzzy controller that uses only control error and change in control error is not able to detect the process nonlinearity and make a control move accordingly. In this paper, a multiregion fuzzy logic controller is proposed for nonlinear process control. Based on prior knowledge, the process to be controlled is divided into fuzzy regions such as high-gain, low-gain, large-time-constant, and small-time-constant. Then a fuzzy controller is designed based on the regional information. Using an auxiliary process variable to detect the process operating regions, the resulting multiregion fuzzy logic controller can give satisfactory performance in all regions. Rule combination and controller tuning are discussed. Application of the controller to pH control is demonstrated     

不需持续激励条件的非线性离散时间系统的自适应模糊逻辑控制

针对一类非线性离散时间系统，根据模糊逻辑系统的逼近性质，给出了一种自适应模糊逻辑控制器的设计方法。利用李亚普诺夫稳定性理论，证明了控制算法是全局稳定的，跟踪误差收敛于零的某一领域中。该设计方法克服了要求模糊基函数向量满足持续激励（PE）条件这一难以验证和满足的假设条件。     

Adaptive control design for a class of nonlinear systems based on fuzzy logic systems with scalers and saturators

Many practical engineering applications require various types of fuzzy logic systems (FLSs) to design adaptive controllers for nonlinear systems with uncertainties. In this article, we will consider a fundamental theoretical question: is it possible to find a unified adaptive control design method suited to various types of FLSs? In order to solve this problem, we will introduce scalers and saturators at the input and output terminals of FLSs to form the extended FLSs (EFLS). The scalers and saturators have adjustable parameters. By designing the updated laws of these parameters and the estimate values of the fuzzy approximate accuracies, stable adaptive fuzzy controllers can be realised for a class of nonlinear systems with unknown homogeneous drift functions and gains. The proposed design method is only dependent on the outputs of EFLS and the above updated laws, thus increasing its adaptability. The fuzzy control scheme introduced in this article is suitable for all fuzzy systems with or without fuzzy rules. Simulations will also be used to show the validity of the method proposed in this article.     

An adaptive fuzzy design for fault-tolerant control of MIMO nonlinear uncertain systems

This paper presents a novel control method for accommodating actuator faults in a class of multiple-input multiple-output (MIMO) nonlinear uncertain systems.The designed control scheme can tolerate both the time-varying lock-in-place and loss of effectiveness actuator faults.In each subsystem of the considered MIMO system,the controller is obtained from a backstepping procedure;an adaptive fuzzy approximator with minimal learning parameterization is employed to approximate the package of unknown nonlinear functions in each design step.Additional control effort is taken to deal with the approximation error and external disturbance together.It is proven that the closed-loop stability and desired tracking performance can be guaranteed by the proposed control scheme.An example is used to show the effectiveness of the designed controller.     

Robust adaptive fuzzy VSS control for a class of uncertain nonlinear systems using small gain design

In this paper, a novel robust adaptive fuzzy variable structure control (RAFVSC) scheme is proposed for a class of uncertain nonlinear systems. The uncertain nonlinear system and gain functions originating from modeling errors and external disturbances are all unstructured (or non-repeatable), state-dependent and completely unknown. The Takagi–Sugeno type fuzzy logic systems are used to approximate uncertain functions in the systems and the RAFVSC is designed by use of the input-to-state stability (ISS) approach and small gain theorem. In the algorithm, there are three advantages which are that the asymptotic stability of adaptive control in the presence of unstructured uncertainties can be guaranteed, the possible controller singularity problem in some of existing adaptive control schemes using feedback linearization techniques can be removed and the adaptive mechanism with minimal learning parameterizations can be achieved. The performance and effectiveness of the proposed methods are discussed and illustrated with two simulation examples.     

Stable adaptive fuzzy control for MIMO nonlinear systems

In this paper, an indirect adaptive fuzzy control scheme is presented for a class of multi-input and multi-output (MIMO) nonlinear systems whose dynamics are poorly understood. Within this scheme, fuzzy systems are employed to approximate the plant’s unknown dynamics. In order to overcome the controller singularity problem, the estimated gain matrix is decomposed into the product of one diagonal matrix and two orthogonal matrices, a robustifying control term is used to compensate for the lumped errors, and all parameter adaptive laws and robustifying control term are derived based on Lyapunov stability analysis. The proposed scheme guarantees that all the signals in the resulting closed-loop system are uniformly ultimately bounded (UUB). Moreover, the tracking errors can be made small enough if the designed parameter is chosen to be sufficiently large. A simulation example is used to demonstrate the effectiveness of the proposed control scheme.     

非线性系统的输入多采样率模糊优化控制

基于多采样率数字控制理论,讨论了非线性连续被控对象和输入多采样率模糊控制器的设计问题.提出用线性矩阵不等式凸优化技术构建非线性系统的输入多采样率T-S模糊模型,并相应地研究了基于优化区域极点配置的PDC状态反馈控制.通过解代数Riccati方程得到控制器的参数,给出了优化数字控制器的设计算法和闭环系统的稳定性条件.计算机仿真表明了所提出方法的有效性.     

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.     

非线性模糊时滞系统鲁棒自适应控制

研究一类基于模糊T-S模型的非线性时滞系统鲁棒镇定问题．基于记忆型状态反馈策略，首先给出由T-S模糊模型描述的非线性时滞系统在时滞精确已知情况下的鲁棒镇定准则；然后给出非线性时滞系统在时滞未知情况下的鲁棒自适应控制策略．所设计的控制器可确保闭环系统渐近稳定，且具有良好的可操作性．最后通过仿真实例证明了该方法的正确性和有效性．     

Constructing nonlinear variable gain controllers via theTakagi-Sugeno fuzzy control

We investigated the analytical structure of the Takagi-Sugeno (TS) type of fuzzy controllers, which was unavailable in the literature. The TS fuzzy controllers we studied employ a new and simplified TS control rule scheme in which all the rule consequent use a common function and are proportional to one another, greatly reducing the number of parameters needed in the rules. Other components of the fuzzy controllers are general: arbitrary input fuzzy sets, any type of fuzzy logic, and the generalized defuzzifier, which contains the popular centroid defuzzifier as a special case. We proved that all these TS fuzzy controllers were nonlinear variable gain controllers and characteristics of the gain variation were parametrized and governed by the rule proportionality. We conducted an in-depth analysis on a class of nonlinear variable gain proportional-derivative (PD) controllers. We present the results to show: (1) how to analyze the characteristics of the variable gains in the context of control; (2) why the nonlinear variable gain PD controllers can outperform their linear counterpart; and (3) how to generate various gain variation characteristics through the manipulation of the rule proportionality     

一类非线性系统的误差轨迹跟踪鲁棒学习控制算法

针对一类含非参数不确定性的非线性系统,提出一种鲁棒迭代学习控制算法,该算法放宽了常规迭代学习控制方法的初始定位条件,迭代初值可任意取值.基于类Lyapunov方法设计误差轨迹跟踪控制器,通过鲁棒限幅学习机制对不确定性进行估计和补偿,能够在整个作业区间上实现误差对给定期望误差轨迹的精确跟踪,期望误差轨迹根据迭代起始时刻的误差值设置.利用期望误差轨迹的衰减性状,可使系统误差在预设的时间点后收敛于原点的邻域内,邻域半径的大小可根据需要任意设置.理论分析和仿真结果表明了控制方法的有效性.