A new hybrid algorithm based on optimal fuzzy controller in multimachine power system

In this article, a new methodology based on fuzzy proportional‐integral‐derivative (PID) controller is proposed to damp low frequency oscillation in multimachine power system where the parameters of proposed controller are optimized offline automatically by hybrid genetic algorithm (GA) and particle swarm optimization (PSO) techniques. This newly proposed method is more efficient because it cope with oscillations and different operating points. In this strategy, the controller is tuned online from the knowledge base and fuzzy interference. In the proposed method, for achieving the desired level of robust performance exact tuning of rule base and membership functions (MF) are very important. The motivation for using the GA and PSO as a hybrid method are to reduce fuzzy effort and take large parametric uncertainties in to account. This newly developed control strategy mixed the advantage of GA and PSO techniques to optimally tune the rule base and MF parameters of fuzzy controller that leads to a flexible controller with simple structure while is easy to implement. The proposed method is tested on three machine nine buses and 16 machine power systems with different operating conditions in present of disturbance and nonlinearity. The effectiveness of proposed controller is compared with robust PSS that tune using PSO and the fuzzy controller which is optimized rule base by GA through figure of demerit and integral of the time multiplied absolute value of the error performance indices. The results evaluation shows that the proposed method achieves good robust performance for a wide range of load change in the presents of disturbance and system nonlinearities and is superior to the other controllers. © 2014 Wiley Periodicals, Inc. Complexity 21: 78–93, 2015     

Adaptive tuning of the fuzzy controller for robots

An adaptive tuning algorithm of the fuzzy controller is developed for a class of serial-link robot arms. The algorithm can on-line tune parameters of premise and consequence parts of fuzzy rules of the fuzzy basis function (FBF) controller. The main part of the fuzzy controller is a fuzzy basis function network to approximate unknown rigid serial-link robot dynamics. Under some mild assumptions, a stability analysis guarantees that both tracking errors and parameter estimate errors are bounded. Moreover, a robust technique is adopted to deal with uncertainties including approximation errors and external disturbances. Simulations of the proposed controller on the PUMA-560 robot arm demonstrate the effectiveness.     

基于GA-BP的模糊神经网络控制器与Elman辨识器的系统设计

提出了一种基于神经网络的模糊控制系统 ,该系统由模糊神经网络控制器和模型辨识网络组成 .文中介绍了模糊神经网络控制器采用遗传算法离线优化与 BP算法在线调整 ,给出了具体控制算法 ,推导了变形 Elmam网络的系统辨识算法 .仿真结果表明了此法的可行性和有效性 .     

A two-stage approach to self-learning direct fuzzy controllers

In this paper, a novel approach is presented to fine tune a direct fuzzy controller based on very limited information on the nonlinear plant to be controlled. Without any off-line pretraining, the algorithm achieves very high control performance through a two-stage algorithm. In the first stage, coarse tuning of the fuzzy rules (both rule consequents and membership functions of the premises) is accomplished using the sign of the dependency of the plant output with respect to the control signal and an overall analysis of the main operating regions. In stage two, fine tuning of the fuzzy rules is achieved based on the controller output error using a gradient-based method. The enhanced features of the proposed algorithm are demonstrated by various simulation examples.     

ETSK模糊模型辨识及模糊控制算法

本文提出一种基于扩张原理的ＥＴＳＫ（ＥｘｔｅｎｄｅｄＴＳＫ）模型,导出了该模型的输入输出解析式,给出了辨识这种模型的方法。本文还导出了ＥＴＳＫ模型的一种等价形式——变权ＴＳＫ模型,从而将ＥＴＳＫ模型规则后件中的模糊数及其扩展运算转化为普通数的运算,使基于ＥＴＳＫ模型的模糊控制算法ＭＢＦＣ（Ｍｏｄｅｌ－ＢａｓｅｄＦｕｚｚｙＣｏｎｔｒｏｌ）易于实现。仿真辨识结果表明,ＥＴＳＫ模型的辨识效果和预报精度优于ＴＳＫ和ＬＭ模型;ＭＢＦＣ算法的控制效果优于通常模型ＰＩ控制算法     

自组织结构的变论域模糊控制

针对变论域模糊控制,提出一种新的自组织结构的变论域模糊控制方法。自组织结构算法可以调整变论域模糊系统结构以及动态获得模糊规则,进一步减小变论域模糊控制项的稳态逼近误差。通过进一步理论分析可知,自组织结构算法仅仅保证了系统瞬时的切换是平稳的,但不能保证系统的闭环稳定性。给出了所提出控制方法的适用条件。通过与固定模糊系统结构的变论域模糊控制比较,仿真结果表明,所提出控制方法不仅使得系统的稳态跟踪误差更平稳,而且使得输入控制信号更加平滑。     

Tracking control of chaotic spinning disks via nonlinear dynamic output feedback with input constraints

In many control engineering applications, it is impossible or expensive to measure all the states of the dynamical system and only the system output is available for controller design. In this study, a new dynamic output feedback control algorithm is proposed to stabilize the unstable periodic orbit of chaotic spinning disks with incomplete state information. The proposed control structure is based on the T‐S fuzzy systems. This investigation also introduces a new design procedure to satisfy a constraint on the T‐S fuzzy dynamic output feedback control signal. This procedure is independent of the exact value of initial states. Finally, computer simulations are accomplished to illustrate the performance of the proposed control algorithm. © 2015 Wiley Periodicals, Inc. Complexity 21: 148–159, 2016     

Robust control of a spatial robot using fuzzy sliding modes

In order to improve the performance of the sliding mode controller, fuzzy logic sliding mode controller is proposed in this study. The control gain of the conventional sliding mode controller is tuned by a fuzzy logic rule base and, also dynamic sliding surfaces are obtained by changing their slopes using the error states of the system in another fuzzy logic algorithm. These controllers are then combined in order to enhance the performance. Afterwards, proposed controllers were used in trajectory control of a three degrees of freedom spatial robot, which is subjected to noise and parameter variations. Finally, the controllers introduced are compared with a PID controller which is commonly used for control of robotic manipulators in industry. The results indicate the superior performance of the proposed controller.     

遗传算法在自动泊车辅助系统模糊控制中的应用

随着汽车工业的发展,自动泊车辅助系统已逐渐成为汽车的必备装置.对自动泊车控制过程进行了分析,设计了自动泊车辅助系统模糊控制器,并将遗传算法应用于模糊控制器参数寻优过程,较为有效的确定了模糊控制器的参数,使用遗传算法工具箱对模糊控制器的隶属度函数进行了优化.并在Matlab环境下,对自动泊车模糊控制进行了仿真研究,论述了遗传算法在改善模糊控制效果中的应用.     

Wall-following of mobile robot based on fuzzy genetic algorithm to linear interpolating

Traditional fuzzy controller has some disadvantages, such as inferiorly adaptability due to the invariable membership function parameters and too many subjective factors. So in this paper, we firstly put forward a new method to fuzzy inference based on the idea of linear interpolating. This method overcomes the shortcoming of conventional fuzzy controller such as the character of multi-relay and the conflict of rule numbers and real-time. Then we use genetic algorithm to off-line optimize the membership function parameters of fuzzy controller, which is used in the controlling course of mobile robot following straight wall. The result shows the optimizing control strategy is more effective in the aspect of following precision than the traditional fuzzy controller.     

Fuzzy fractional order sliding mode controller for nonlinear systems

In this paper, an intelligent robust fractional surface sliding mode control for a nonlinear system is studied. At first a sliding PD surface is designed and then, a fractional form of these networks PD^α, is proposed. Fast reaching velocity into the switching hyperplane in the hitting phase and little chattering phenomena in the sliding phase is desired. To reduce the chattering phenomenon in sliding mode control (SMC), a fuzzy logic controller is used to replace the discontinuity in the signum function at the reaching phase in the sliding mode control. For the problem of determining and optimizing the parameters of fuzzy sliding mode controller (FSMC), genetic algorithm (GA) is used. Finally, the performance and the significance of the controlled system two case studies (robot manipulator and coupled tanks) are investigated under variation in system parameters and also in presence of an external disturbance. The simulation results signify performance of genetic-based fuzzy fractional sliding mode controller.     

Modelling and control of flexible joint robot based on Takagi–Sugeno fuzzy approach and its stability analysis via sum of squares

This article proposes a Takagi–Sugeno (T-S) fuzzy model of single-link rotary flexible joint robot. The proposed control method is based on parallel distributed control. The parameters of T-S controller are improved by distributed population genetic algorithm (GA) with chaos GA. Using Hermite–Biehler theorem in distributed population, GA is made to have a fast convergence. Dividing search space into several sub-spaces causes a better response, and chaos disturbance helps the whole algorithm to reach a best answer. The stability of the controller is analysed via the sum of squares programming, and finally, it is implemented on the plant.     

New fuzzy wavelet network for modeling and control: The modeling approach

In this paper, a fuzzy wavelet network is proposed to approximate arbitrary nonlinear functions based on the theory of multiresolution analysis (MRA) of wavelet transform and fuzzy concepts. The presented network combines TSK fuzzy models with wavelet transform and ROLS learning algorithm while still preserve the property of linearity in parameters. In order to reduce the number of fuzzy rules, fuzzy clustering is invoked. In the clustering algorithm, those wavelets that are closer to each other in the sense of the Euclidean norm are placed in a group and are used in the consequent part of a fuzzy rule. Antecedent parts of the rules are Gaussian membership functions. Determination of the deviation parameter is performed with the help of gold partition method. Here, mean of each function is derived by averaging center of all wavelets that are related to that particular rule. The overall developed fuzzy wavelet network is called fuzzy wave-net and simulation results show superior performance over previous networks.The present work is complemented by a second part which focuses on the control aspects and to be published in this journal([17]). This paper proposes an observer based self-structuring robust adaptive fuzzy wave-net (FWN) controller for a class of nonlinear uncertain multi-input multi-output systems.     

T–S fuzzy-model-based robust stabilization for a class of nonlinear discrete-time networked control systems

In this paper, the robust stabilization problem is investigated for a class of nonlinear discrete-time networked control systems (NCSs). To study the system stability and facilitate the design of fuzzy controller, Takagi–Sugeno (T–S) fuzzy models are employed to represent the system dynamics of the nonlinear discrete-time NCSs with effects of the approximation errors taken into account, and a unified model of NCSs in the T–S fuzzy model is proposed by modeling the approximation errors as norm-bounded uncertainties in system metrics, where non-ideal network Quality of Services (QoS), such as data dropout and network-induced delay, are coupled in a unified framework. Then, based on the Lyapunov–Krasovskii functional, sufficient conditions are derived for the existence of a fuzzy controller. By these criteria, two approaches to design a fuzzy controller are developed in terms of linear matrix inequalities (LMIs). Finally, illustrative examples are provided to show the effectiveness of the proposed methods.     

On-Line Constrained Predictive Control Algorithm using Multi-Objective Fuzzy-Optimization and a Case Study

Model predictive control (MPC) has been used in process control systems with constraints, however, the constrained optimization problem involved in control systems has generally been solved in practice in a piece-meal fashion. To solve the problem systemically, in this paper, the Multi-Objective Fuzzy-Optimization (MOFO) is used in the constrained predictive control for online applications as a means of dealing with fuzzy goals and fuzzy constraints in control systems. The conventional model predictive control is integrated with the techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the fuzzy goals and the fuzzy constraints of the control problem is combined by using a decision function from the fuzzy theory, so it is possible to aggregate the fuzzy goals and the fuzzy constraints using fuzzy operators, e.g. t-norms, s-norms or the convex sum. It is shown that the model predictive controller based on MOFO allows the designers a more flexible aggregation of the control objectives than the usual weighting sum of squared errors in MPC. The efficiency of the presented algorithm is validated by the visual robot path planning.     

多机电力系统直接自适应输出反馈模糊控制

针对多机电力系统励磁控制模型,考虑电力系统的状态不完全可测及多变量、非线性等特点,以T-S模糊逻辑系统直接逼近控制器,设计出基于状态观测器的直接自适应输出反馈模糊控制器,并通过李亚普诺夫函数进行了稳定性证明.算法具有很好的鲁棒性和动态性能,仿真结果表明所设计的控制器能够快速有效地改善系统在大干扰下的暂态稳定性.     

Modeling and control of nonlinear systems using novel fuzzy wavelet networks: The output adaptive control approach

This paper proposes an observer based self-structuring robust adaptive fuzzy wave-net (FWN) controller for a class of nonlinear uncertain multi-input multi-output systems. The control signal is comprised of two parts. The first part arises from an adaptive fuzzy wave-net based controller that approximates the system structural uncertainties. The second part comes from a robust H_∞ based controller that is used to attenuate the effect of function approximation error and disturbance. Moreover, a new self structuring algorithm is proposed to determine the location of basis functions. Simulation results are provided for a two DOF robot to show the effectiveness of the proposed method.     

一类MIMO非线性系统的稳定自适应模糊控制

针对一类 MIMO不确定非线性系统 ,基于一种修改的李亚普诺夫函数并利用 I型模糊系统的逼近能力 ,提出一种分散自适应模糊控制器设计的新方案。该方案不但能够避免现有的一些自适应模糊 /神经网络控制器设计中对控制增益一阶导数上界的要求 ,而且能够避免控制器的奇异问题。通过理论分析 ,证明闭环控制系统是全局稳定的 ,跟踪误差收敛到零。仿真结果表明了该方法的有效性。     

自适应模糊变结构控制的研究

本文主要研究一类具有未知常数控制增益的非线性系统的自适应模糊控制问题,提出了一种能够利用专家的语言信息和数字信息的自适应模糊变结构控制器的设计方案。通过理论分析,证明了模糊变结构控制系统是全局稳定的,跟踪误差可收敛到零的一个邻域内     

AQM scheme design for TCP network via Takagi‐Sugeno fuzzy method

This paper proposes a novel T‐S fuzzy control method instead of the traditional linear system control method to improve the TCP network performance. Thus a TCP network can be modeled as a T‐S fuzzy system, and by use of linear matrix inequality method and cone complementarity linearization algorithm, a fuzzy state feedback controller is provided while considering the problem of the asynchronous membership grades between the controller and the plant. Simulation results are presented to show that the proposed control approach can guarantee the asymptotical stability of the studied system and the desired queue size. © 2016 Wiley Periodicals, Inc. Complexity 21: 606–612, 2016