Determination of optimal hierarchical fuzzy controller parameters according to loading condition with ANN

This paper represents an artificial neural network (ANN) backpropagation algorithm is used to choose best coefficients of hierarchical fuzzy power system stabilizer (HFPSS). PSS is used for stability enhancement of a single machine infinite bus (SMIB) power system. ANN algorithm is used to predict load condition of the power system. And according to the predicted load condition ANN determinates choosing optimal parameters of the hierarchical fuzzy controller (HFC) to achieve better performance. Simulation results are compared with conventional PSS (CPSS) to show the effectiveness of the proposed controller. Also quantitative criterias of measuring performance is computed for 16 loading conditions.     

核动力装置蒸汽发生器水位的分层模糊自适应控制

针对压水堆核动力装置蒸汽发生器的水位控制提出一种分层模糊自适应控制方案，该方案中2个模糊控制器分层连接，每个模糊控制均采用典型模糊控制单元，使得模糊规则个数和可调参数个数大大减少，便于在线学习和实时控制，给出了分层模糊控制器的解析表达式及可调参数的在线学习方法，在快速加负荷和突然甩负荷的仿真实验中，该方案的控制效果明显优于已有的变参数PID控制，验证了该方案的有效性。     

Suction Control of a Robotic Gripper: A Neuro-Fuzzy Approach

This paper discusses a fuzzy logic control system designed to determine, regulate and maintain the amount of suction needed by a robotic gripper system to perform reliable limp material manipulation. A neuro-fuzzy approach is followed to determine the amount of desired suction (depending on experimentally derived data and plant characteristics). A knowledge-based valve controller is then designed to generate, regulate and maintain the amount of suction calculated by the neuro-fuzzy suction module. The performance of the overall suction control system is compared with actual experimental results obtained when using a prototype gripper system to handle limp material. Further, performance of the fuzzy logic based valve controller is compared to conventional PD and PID controllers. The proposed control scheme is found to enhance the overall functionality of the prototype robotic gripper system.     

Robust adaptive neuro-fuzzy control of uncertain nonholonomic systems

In this paper, we present an adaptive neuro-fuzzy controller design for a class of uncertain nonholonomic systems in the perturbed chained form with unknown virtual control coefficients and strong drift nonlinearities. The robust adaptive neuro-fuzzy control laws are developed using state scaling and backstepping. Semiglobal uniform ultimate bound-edness of all the signals in the closed-loop are guaranteed, and the system states are proven to converge to a small neigh-borhood of zero. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. By using fuzzy logic approximation, the proposed control is free of control singularity problem. An adaptive control-based switching strategy is proposed to overcome the uncontrollability problem associated with x 0 (t 0 ) = 0.     

A fuzzy logic based supervisory hierarchical control scheme for real time pressure control

This paper describes a supervisory hierarchical fuzzy controller （SHFC） for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances. This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range. The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller. The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation. The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time. To demonstrate the effectiveness, the results of the proposed hierarchical controller, fuzzy controller and conventional proportional-integral （PI） controller are analyzed. The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods.     

Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators

A new robust neuro-fuzzy controller for autonomous and intelligent robot manipulators in dynamic and partially known environments containing moving obstacles is presented. The navigation is based on a fuzzy technique for the idea of artificial potential fields (APFs) using analytic harmonic functions. Unlike the fuzzy technique, the development of APFs is computationally intensive. A computationally efficient processing scheme for fuzzy navigation to reasoning about obstacle avoidance using APF is described, namely, the intelligent dynamic motion planning. An integration of a robust controller and a modified Elman neural networks (MENNs) approximation-based computed-torque controller is proposed to deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The MENN weights are tuned online, with no off-line learning phase required. The stability of the overall closed-loop system, composed by the nonlinear robot dynamics and the robust neuro-fuzzy controller, is guaranteed by the Lyapunov theory. The purpose of the robust neuro-fuzzy controller is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.     

Observer-Based Adaptive Controller Design of Flexible Manipulators Using Time-Delay Neuro-Fuzzy Networks

In this paper, a dynamical time-delay neuro-fuzzy controller is proposed for the adaptive control of a flexible manipulator. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity. For a perfect tracking control of the robot, the output redefinition approach is used in the adaptive controller design using time-delay neuro-fuzzy networks. The time-delay neuro-fuzzy networks with the rule representation of the TSK type fuzzy system have better learning ability for complex dynamics as compared with existing neural networks. The novel control structure and learning algorithm are given, and a simulation for the trajectory tracking of a flexible manipulator illustrates the control performance of the proposed control approach.     

质子交换膜燃料电池动态建模及其双模控制

由于已提出的质子交换膜燃料电池(PEMFC)模型难于控制, 提出利用MATLAB/SIMULINK仿真工具进行PEMFC系统动态建模, 同时为实现对PEMFC系统输出电压的控制, 采用了基于模糊规则切换的模糊逻辑控制器(FLC)和比例积分微分控制器(PID)相结合的双模控制方式. 仿真结果证明该动态模型易于控制, 能够反映出PEMFC系统的动态输出特性, 而且验证了基于模糊规则切换的双模控制能够有效抑制扰动, 改善PEMFC系统的动态输出特性, 保证系统的稳定运行, 有助于对PEMFC系统的输出性能分析以及实时控制系统的设计.     

Modeling and Hierarchical Neuro-Fuzzy Control for Flexure-Based Micropositioning Systems

The micropositioning system using flexural bearing (e.g., for wafer steppers and coarse-fine positioning systems) is a system of infinite degrees of freedom. It is difficult to design a controller for the partial differential equation of the system directly. In this paper, a closed-form dynamics model is first developed using the assumed modes method and the least squares method. Then, a hierarchical neuro-fuzzy controller using backpropagation (BP) training algorithm is proposed for the precision control and active damping of the micropositioning system. Simulation results show that the suggested strategy can actively suppress the flexible vibration and have high positioning performance.     

基于分层模糊系统的直接自适应控制

为解决模糊控制器中规则数目随系统变量呈指数增长的问题，利用分层模糊系统设计了一类非线性系统的直接自适应模糊控制器，并证明了所提出的设计方法不但能保证闭环系统的一致有界性。而且可使跟踪误差收敛到原点的小领域内，通过对倒立摆控制的仿真研究验证了该方法的有效性。     

Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system

This paper presents a novel control approach of hybrid neuro-fuzzy (HNF) for load frequency control (LFC) of four-area power system. The advantage of this controller is that it can handle the non-linearities, and at the same time it is faster than other existing controllers. The effectiveness of proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in four area interconnected power system. Area-1 and area-2 consist of thermal reheat power plant whereas area-3 and area-4 consist of hydro power plant. Performance evaluation is carried out by using fuzzy, ANN, ANFIS and conventional PI and PID control approaches. The performances of the controllers are simulated using MATLAB/Simulink package. The result shows that intelligent HNF controller is having improved dynamic response and at the same time faster than ANN, fuzzy and conventional PI and PID controllers.     

Exponential stability and robust H∞ control of a class of discrete-time switched non-linear systems with time-varying delays via T-S fuzzy model

This paper deals with stability and robust H_∞ control of discrete-time switched non-linear systems with time-varying delays. The T-S fuzzy models are utilised to represent each sub-non-linear system. Thus, with two level functions, namely, crisp switching functions and local fuzzy weighting functions, we introduce a discrete-time switched fuzzy systems, which inherently contain the features of the switched hybrid systems and T-S fuzzy systems. Piecewise fuzzy weighting-dependent Lyapunov–Krasovskii functionals (PFLKFs) and average dwell-time approach are utilised in this paper for the exponentially stability analysis and controller design, and with free fuzzy weighting matrix scheme, switching control laws are obtained such that H_∞ performance is satisfied. The conditions of stability and the control laws are given in the form of linear matrix inequalities (LMIs) that are numerically feasible. The state decay estimate is explicitly given. A numerical example and the control of delayed single link robot arm with uncertain part are given to demonstrate the efficiency of the proposed method.     

利用BP算法的一种自适应模糊预测控制器

提出一种由模糊预测器和模糊预测控制器组成的自适应模糊预测控制方案,采用ＢＰ算法训练模糊预测器和模糊预测控制器,并给出这种模糊预测控制器的训练算法。控制系统对于具有纯时延的非线性被控过程有良好的控制性能。     

Supervisory control using a new control-relevant switching

This paper presents a new supervisory control scheme, which is based on a control-relevant switching logic. Unlike most of the existing switching methods considering only estimator performance, the proposed scheme takes both estimator and controller performance into account. As an index to the controller performance, an iISS (integral-input-to-state stability) Lyapunov function is employed; it is ensured that the Lyapunov function satisfies a certain inequality. This Lyapunov-based switching is then coupled to the state-dependent dwell-time switching developed recently, and the state of the uncertain plant is shown to converge asymptotically. The proposed supervisory control scheme is applied to an input-constrained neurally stable linear plant.     

单路口交通实时模糊控制的一种方法

根据城市交通系统的实际状况，依据单路口４个方向车流的来车信息，提出了一种单路口交通模糊控制方法－多级模糊控制方法，设计了多级模糊控制器，并对该控制器进行了仿真研究，结果令人满意。     

含模型不确定性移动机器人路径跟踪的分层模糊控制

对受非完整约束且含模型不确定性的移动机器人基于分层模糊系统设计了跟踪期望几何路径的鲁棒间接自适应控制方案.此方法除实现路径跟踪外,还可避免控制器的奇异性并保证跟踪方向.由于控制结构中使用了分层模糊系统,大大减少了模糊规则数目;并用鲁棒控制项对模糊系统逼近误差进行补偿,减少了其对跟踪精度的影响.证明了闭环系统跟踪误差收敛到原点的小邻域内,且可通过适当增大鲁棒控制项的设计参数使跟踪误差进一步减小.最后用实验结果验证了方法的有效性.     

用于SVC的一种模糊非线性变结构控制方法

采用直接反馈线性化方法得到有SVC单机无穷大系统线性模型状态方程。以此为被控对象,设计了一个滑膜变结构控制器,将滑膜切换面函数值及其变化率作为模糊控制器输入,以调整控制量输出。通过仿真验证,所设计的SVC模糊滑膜控制器可以有效的改善系统功率环境,受到扰动时可以迅速的平息功率的变化。     

基于复合控制的电力系统稳定器设计

针对传统电力系统稳定器(PSS)的缺点,提出一种基于复合控制的电力系统稳定器,可以根据实际的运行情况在模糊和比例积分微分(Fuzzy-PID)控制中进行选择性的控制,使电力系统稳定器既具有模糊控制简单有效的非线性控制作用,又具有比例和积分控制的快速性和跟踪能力。理论分析及仿真结果均表明所提出的方案正确可行并具有良好的性能。     

基于减法聚类和自适应神经模糊推理系统的递阶模糊系统的设计

提出了一种设计递阶模糊系统的简易而有效的方法.在得到一个单级模糊系统的基础上,用灵敏度分析法对每一个输入变量的重要性进行排序,从而确定每一级子系统的输入变量.利用减法聚类和自适应神经 模糊推理系统逐级对子系统进行训练.所得到的递阶模糊系统可进一步得到简化.仿真实例证实了设计方法的有效性.     

应用进化策略的城域交叉路口分级模糊控制

提出一种面向城域单交叉路口的自适应分级模糊控制系统，采用进化策略对分级模糊控制器的模糊隶属度函数进行离线优化调整．该控制系统不仅具有分级模糊控制的优点，同时能使模糊隶属度函数根据不同的交通情况自适应地变化，从而改善控制效果．对一个具有直行和左转车流运动的四向交叉路口进行的仿真表明了该方法能比定时控制和隶属度函数固定的分级模糊控制取得更好的控制效果．