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.     

永磁同步电机交流伺服系统的滑模模糊控制

针对模糊控制器在设计过程中参数难以确定以及滑模控制存在抖动等问题,将滑模控制理论与模糊控制理论相结合,提出了一种基于Sugeno型模糊推理的滑模模糊控制器的设计方法。使每一条Sugeno型模糊推理规则的后件部分为一滑模控制策略,保证了模糊控制系统的稳定性和动态性能。将该方法用于永磁同步电机交流伺服系统位置控制器的设计,并进行了仿真。结果表明,该方法综合了传统的滑模控制和模糊控制的优点,具有很好的动态跟随性和较强的鲁棒性,有效地削弱了抖动。     

多馈入HVDC的模糊自适应协调阻尼控制器设计

提出一种基于广域测量系统的多馈入高压直流（HVDC）模糊自适应协调阻尼控制器。该控制器在传统的单输入单输出控制结构基础上增加了一个模糊逻辑单元,自适应地在线调整系统的移相角。同时,增益K与模糊逻辑单元联调,以保证移相环节参数改变后整个控制通道的增益保持不变。利用Prony辨识算法和极点配置法对该阻尼控制器的固定参数进行了整定,并对整定后的参数进行了协调优化。以中国南方电网2007年网络结构为对象,给出了该模糊自适应协调阻尼控制器的设计过程及仿真结果。结果表明：该阻尼控制器能快速、有效地阻尼区域间振荡,提高交流联络线的传输能力,对不同的网络结构具有鲁棒性,可明显改善多馈入HVDc系统的稳定性。     

Increase in the efficiency of the speed controller of an induction machine drive by means of artificial intelligence

This paper describes the application of artificial intelligence to the synthesis of an efficient speed controller for an indirect field-oriented induction machine drive. In this case, two methods of artificial intelligence are proposed to reproduce the characteristics of a fuzzy controller with the maximum number of fuzzy sets. The first method is the adaptive network-based fuzzy inference system (ANFIS) structure by Sugeno and the second is the artificial neural network (ANN) structure. The resulting characteristics of each controller are presented.     

A fuzzy logic-based adaptive damping controller for static VAR compensator

This paper presents an approach for designing a fuzzy logic-based adaptive SVC damping controller for damping low frequency power oscillations. The proposed approach uses linear optimal controllers and, in addition, a fuzzy signal tuner is introduced to achieve adaptiveness. Two linear damping controllers are designed to accommodate two extreme operating conditions and a fuzzy logic mechanism is used to generate one single control signal by properly combining the outputs of the linear controllers. The fuzzy controller is optimized using a least squares error criterion and, as a result, a higher-order adaptive controller is achieved. Simulation studies of the proposed fuzzy adaptive damping controller show that it provides satisfactory damping against low frequency oscillations under different operating conditions. Results are given for a one-machine infinite-bus and a four-machine two-area test system, which show improvement over the use of a fixed parameter controller.     

Robust Frequency Control in an Autonomous Microgrid: A Two-Stage Adaptive Fuzzy Approach

Highly intermittent power from renewable energy sources (RES) along with load and system perturbations in an autonomous microgrid (MG), results in large frequency fluctuations. Conventional controllers like PI controllers to be unable to provide acceptable performance over a wide range of operating conditions. To overcome this problem, present paper introduces a novel two-stage adaptive fuzzy logic based PI controller for frequency control of MG. In this proposed controller, particle swarm optimization (PSO) and grey wolf optimization (GWO) are used to optimize the membership functions (MFs) and rule base of fuzzy logic based PI controller. The proposed controller is examined on an MG test system, the robustness and performance of the proposed controller is tested in presence of different disturbance scenarios and parametric uncertainties. Finally, the superiority of the proposed controller is shown by comparing the results with various controllers available in literature like PSO tuned fuzzy logic based PI controller, fuzzy logic-based PI controller and also with the conventional PI controller.     

基于免疫优化的机炉协调系统模糊增益调度H∞鲁棒控制

针对非线性锅炉-汽轮机系统在负荷点大范围变动情况下的协调控制,提出了一种新的免疫优化模糊增益调度 鲁棒控制方法。该方法首先利用 /混合灵敏度方法设计出多个 局部鲁棒控制器以覆盖机炉系统的负荷变化区间;然后采用模糊推理生成增益调度准则以实现各局部控制器间的软切换;最后利用免疫遗传算法对模糊增益调度准则的结构参数进行优化设计,从而保证闭环系统具有良好的全局控制性能。仿真研究表明：基于该方法设计出的协调控制系统在大工况范围内具有优良的调节品质。     

基于模糊推理的模糊自适应励磁控制器

本文将模糊控制理论和线性最优控制理论结合起来，提出一种模糊自适应励磁控制器的设计方法。     

基于滑模模糊方法的变速风电系统的最大风能捕捉控制器设计

针对具有不确定参数和强干扰的变速风力发电系统,提出一种积分滑模模糊自适应控制器。该控制策略基于带积分补偿的滑模控制器,并利用自适应模糊控制方法,把滑模控制器中的不确定项进行模糊逼近,同时对其中的切换项也进行模糊自适应逼近,从而有效降低变结构控制固有的抖振现象。利用李亚普诺夫函数证明了控制器的稳定性。采用此控制策略,对变速风力发电系统的风轮转速进行跟踪控制,仿真结果显示此控制方法具有较强的鲁棒性和良好的跟踪性能。     

Linear and fuzzy control solutions for tape drives

This paper suggests new linear and fuzzy control solutions for tape drive-based transport systems. The linear control solution involves a cascade control system with inner state-feedback controller and outer PI controllers. The fuzzy control solution employs a Takagi–Sugeno fuzzy controller to schedule 11 separately designed linear controllers depending on measured take-up reel radius. Two simulation scenarios illustrate the control system performance enhancement ensured by the fuzzy control solution.     

Integrated PLC-fuzzy PID Simulink implemented AVR system

Improving the transient response of power generation systems using automation control in a precise manner is the key issue. We design a fuzzy proportional integral derivative (PID) controller using Matlab and programmable logic controllers (PLCs) for a set point voltage control problem in the automatic voltage regulator (AVR) system. The controller objective is to maintain the terminal voltage all the time under any loads and operational conditions by attaining to the desired range via the regulation of the generator exciter voltage. The main voltage control system uses PLCs to implement the AVR action. The proposed fuzzy controller combines the genetic algorithm (GA), radial-basis function network (RBF-NN) identification and fuzzy logic control to determine the optimal PID controller parameters in AVR system. The RBF tuning for various operating conditions is further employed to develop the rule base of the Sugeno fuzzy system. The fuzzy PID controller (GNFPID) is further designed to transfer in PLCs (STEP 75.5) for implementing the AVR system with improved system response. An inherent interaction between two generator terminal voltage control and excitation current is revealed. The GNFPID controller configures the control signal based on interaction and there by reduces the voltage error and the oscillation in the terminal voltage control process. We achieve an excellent voltage control performance by testing the proposed fuzzy PID controller on a practical AVR system in synchronous generator for improve the transient response.     

基于RBF网络的Takagi—Sugeno模糊控制器参数获取

本文分析了在一定条件下RBF网络与简化的Takagi-Sugeno模糊推理规则的函数等效性。并在此基础上提出了使用RBF网络在实时控制过程中获取模糊控制器参数的方法。仿真结果证明了该方法的有效性。     

Robust 2DOF fuzzy gain scheduling control for DC servo speed controller

This paper proposes a new design method called “robust 2DOF fuzzy gain scheduling control” for a DC servo speed control system. The proposed technique utilizes the basic concept of 2DOF robust loop shaping, whose time‐domain specifications are combined during the controller design using a reference model. In addition, the local controllers are fixed‐ structure robust controllers whose structure can be specified as for a simple controller. A fuzzy approach is adopted in both system identification process and global control structure to accomplish an entirely robust system. Although the design of robust control in a fuzzy system is not easy, genetic algorithms (GAs) simplify the control design problem to design the fuzzy controller such that the average stability margin is minimized. Implementation of a DC servo speed control was adopted to investigate the effectiveness of the proposed controller. As seen from the results, the proposed controller has more robust performance and can be adopted in applications with a wide operating range. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

DSP-Based Real-Time Implementation of a Hybrid H ∞ Adaptive Fuzzy Tracking Controller for Servo-Motor Drives

An embedded hybrid Hinfin adaptive fuzzy control structure is implemented for trajectory tracking control of a brushless servo drive system. The control structure employs a fuzzy logic controller incorporating an Hinfin tracking controller via an acceleration feedback signal. The fuzzy logic controller is equipped with an adaptive-law-based Lyapunov synthesis approach to compensate for system uncertainty and random changes in the external load acting on the drive system. The proposed control structure is experimentally verified on a state-of-the-art dSPACE DS1104 digital signal processor (DSP)-based data acquisition and control system in a laboratory 1-hp brushless drive system. The controllers are first designed in Simulink. Then, the Real-Time Workshop is used to automatically generate optimized C code for real-time applications. Afterward, the interface between MATLAB/Simulink and the dSPACE DS 1104 allows the control algorithm to run on the hardware processor of the DSP. The result is a powerful testbed for the rapid design and implementation of the hybrid tracking controllers for a wide variety of operating conditions. Experimental results are provided to verify the effectiveness of the proposed controller. Considerable improvement in the performance generated by the hybrid controller is compared with the traditional Hinfin controller     

规则自适应模糊控制在同步发电机励磁系统中的应用

对于像电力系统这样的典型非线性系统,采用常规PID控制器很难保证系统在不同工作状态下均取得良好的控制效果.采用模糊参数自适应PID励磁控制器对解决小干扰下的励磁控制问题具有较好的控制效果.当系统工作状态变化较大以及遇到较强的干扰时,系统控制性能将趋于恶化.为解决此问题,提出了同步发电机励磁系统的规则自适应模糊控制方案.主要讨论在模糊集Ai、Bi 的比例因子K1、K2、K3给定的条件下,通过调整 di 的取值来实现控制规则的自适应问题, 该规则自适应机构由两组关于控制规则自生成与自校正的元规则组成.仿真结果表明,所提出的方案正确可行并具有良好的性能.     

基于Smith预估的模糊/PID串级主汽温控制系统仿真

针对火电厂主汽温控制对象特点,提出一种带Smith预估的模糊/PID复合串级控制系统。该系统的主控制器采用模糊/PID并联控制器,其中常规模糊控制模块中加入了自适应环节,主环中还采用增益自适应Smith预估控制器,以提高系统适应工况变化能力。Matlab仿真结果表明,新的串级控制系统对模型的不确定性和外扰均有较强的适应性和鲁棒性,其控制品质远优于常规的串级控制系统。     

PSS与HVDC调制器的在线协调控制策略

提出了模糊神经网络电力系统稳定器与高压直流调制器的自适应在线协调控制策略,改善了系统在大扰动后的动态品质。该协调控制策略采用辨识器预测各个发电机在下一个采样点的角速度变化,并以其为变量构造目标函数对模糊控制器进行在线训练,使功角摇摆过程中各发电机间角速度差值的平方和最小。算法用于三区域双馈入直流系统的在线协调控制。仿真结果表明,自适应在线协调策略使系统在不同的运行状态和不同的故障下均具有良好的暂态稳定性,抑制了故障后系统的振荡。     

负荷频率系统的LFC模糊监控器设计

应用在LFC控制中常规的模糊调整增益PI控制器和I控制器,只针对单一区域的负荷频率进行控制,并不考虑实际模型中的互相扰动.其中最突出的问题是由于不限制联络线上的功率流动,电能总是从频率高区域流向频率低区域,加剧了频率高区域的控制负担,再加上各区域控制动作的不协调,使得当扰动在不同的时间和幅值时引起系统调节过程的急剧恶化,导致整个系统不稳定.提出一种基于模糊MAMDANI推理算法的模糊监督控制器,采用各个区域的频率偏差作为控制器的输入,监督控制器作为一种前馈补偿,设计输出为一个ACE的倍数,加快调节过程,到达稳态.经过实验仿真数据对比,说明提出的监督控制器能有效提高系统的稳定性.     

Robust nonlinear control of wind energy conversion systems

This paper presents stability analysis for a class of uncertain nonlinear systems and a method for designing robust fuzzy controllers to stabilize the uncertain nonlinear systems. First, the Takagi–Sugeno (TS) fuzzy model is adopted for fuzzy modeling of the uncertain nonlinear system. Next, new stability conditions for a generalized class of uncertain systems are derived from robust control techniques such Linear Matrix Inequalities (LMIs). The derived stability conditions are used to analyze the stability of Takagi and Sugeno’s fuzzy control systems with uncertainty which can be regarded as a generalized class of uncertain nonlinear systems. The design method employs the so-called Parallel Design Approach (PDA). TS fuzzy systems are classified into three families based on the input matrices and a robust fuzzy controller’s synthesis procedure is given for each family. In each family, sufficient conditions are derived for robust stabilization in the sense of Taylor series stability, for the TS fuzzy system with parametric uncertainties. Important issues for the stability analysis and design are remarked. The effectiveness of the proposed controller design methodology is finally demonstrated through the two different models of Wind Energy Systems (WES) to illustrate the effectiveness of the proposed method.     

Reaching phase free adaptive fuzzy synergetic power system stabilizer

In this paper, an adaptive fuzzy power system stabilizer is developed based on robust synergetic control theory and terminal attractor techniques. The main contribution consists in making the dynamic system insensitive to parameters variation. This aim is achieved using a new synergetic controller design such that power system states start, evolve and remain on a designer chosen attractor toward the equilibrium point therefore avoiding transient mode. Rendering the design more robust, fuzzy logic systems are used to approximate the unknown power system dynamic functions without calling upon usual model linearization and simplifications. Based on an indirect adaptive scheme and Lyapunov theory, adaptation laws are developed to make the controller handle parameters variations due to the different operating conditions occurring on the power system and to guarantee stability. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results show the effectiveness and robustness of the proposed stabilizer in damping power system oscillations under various disturbances and better overall performance than classical PSS and some other types of power stabilizers.