A recurrent neural fuzzy controller based on self‐organizing improved particle swarm optimization for a magnetic levitation system

This paper proposes a recurrent neural fuzzy controller (RNFC) approach based on a self‐organizing improved particle swarm optimization (SOIPSO) algorithm used for solving control problems. The proposed SOIPSO algorithm can adaptively determine the number of fuzzy rules and automatically adjust the parameters in an RNFC. The proposed learning algorithm consisted of phases of structure and parameter learning. Structure learning adopts several subswarms to constitute the adjustable variables in fuzzy systems, and an elite‐based structure strategy determines the suitable number of fuzzy rules. This paper proposes an improved particle swarm optimization technique, which consists of the modified evolutionary direction operator (MEDO) and traditional PSO techniques. The proposed MEDO method used the EDO and migration operation to improve the search ability of a global solution. Finally, the proposed RNFC approach based on the SOIPSO learning algorithm (RNFC–SOIPSO) was adopted to control a magnetic levitation system. Experimental results demonstrated that the proposed RNFC–SOIPSO model outperforms other models. Copyright © 2014 John Wiley & Sons, Ltd.     

DC/DC变换器自适应模糊逻辑控制器设计

为DC/DC变换器设计了一种自适应模糊逻辑控制器（AFLC）。所提出的AFLC不需要专家系统提供决策参数和控制规则,而是使用模型数据文件来产生参数和规则,该模型数据文件包含输入输出对的整体概况。所提出的控制器使用8位微控制器来实现降压、升压和降压-升压变换器。     

Damping of low‐frequency oscillations using an SVC‐based supplementary controller

In this paper, the optimal tunning problem of parameters of a conventional lead–lag controller (LLC) and a fuzzy logic controller (FLC) based on the static Var compensator (SVC) is considered. The solution is obtained using an improved particle swarm optimization (IPSO) algorithm. The membership functions and scaling factors of the FLC and LLC parameters are optimized using the above‐mentioned technique. The proposed controllers are settled down to an SVC that is installed at the middle of a transmission line connecting a single machine to an infinite bus system. Simulation results show the superiority of the optimized FLC compared to the optimized LLC and also when the SVC is without the supplementary controller under different disturbances and loading conditions. The simulations and analyses are implemented in MATLAB environment. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

粒子群优化模糊PID的电动负载模拟器研究

为了解决多余力矩对电动负载模拟器强干扰，影响加载指令跟踪精度的问题，将基于粒子群优化的模糊PID控制方法用于加载电机控制器的设计。首先在分析加载电机结构以及工作原理的基础上建立了电动加载系统的数学模型，并利用结构不变性原理进行前馈补偿推导；其次针对常规PID控制器无法通过变参数来应对复杂的非线性环境，以及模糊PID量化因子、比例因子难以依靠经验调整问题，提出了一种基于模糊PID和粒子群优化算法的复合控制策略；最后通过仿真验证了该控制策略在对多余力矩消除上要优于常规的模糊PID控制。     

基于专家系统的自校正模糊控制器

针对传统模糊控制器的不足 ,构造了一种基于专家系统的自校正模糊控制器 ,丰富了模糊控制器的结构及内容 ,改善了其性能。仿真研究证明该控制器的有效性。     

磁悬浮系统的哈密顿建模和无源控制

针对磁悬浮系统非线性特点,采用端口受控哈密顿系统理论与无源性控制原理研究磁悬浮系统的建模和控制。将磁悬浮看作能量变换装置,从能量平衡角度推导出单自由度磁悬浮系统的端口受控哈密顿模型。在哈密顿结构基础上,引入结构互联和阻尼配置,给出闭环系统期望的哈密顿函数,设计了磁悬浮系统的无源控制器。设计中直接采用哈密顿函数作为存贮函数,保证了数学严密性,且使系统在满足无源性的条件下达到要求的性能,具有明确的物理意义。仿真结果表明,系统能够快速响应,并且对负载的变化具有很强的抗干扰性能。     

Experimental study of a PM-HTSC hybrid magnetic levitation system

A principle of hybrid magnetic levitation system using permanent magnets (PMs) and high-temperature superconductors (HTSCs) for a linear-motor-type carrier system in a high-quality clean room and a magnetic bearing for the flywheel energy storage system are discussed. A substantial levitation force was generated by the repulsive force between like poles of coupled magnets. However, the repulsion system with magnets is essentially unstable without control. Thus, HTSCs are applied for control elements of the PM-HTSC hybrid magnetic levitation system. In this system, the levitation force becomes stronger than in the system without PM, and we can expect sandwich-type magnetic bearing capable of sustaining greater force.     

Parameter design of event‐driven logic controller for two‐wheeled vehicle

Programmable Logic Controllers (PLCs) are widely used in industry. In PLC‐based control systems, low‐resolution (especially ON‐/OFF) sensors are inexpensive, and actuators are commonly used because they are compatible with programming languages used in PLCs. PLC switches the actuators ON/OFF as the state of the sensor changes between ON/OFF. In designing PLC‐based systems, the design of the parameters of these sensors and actuators (e.g., position of limit switches and torque of motors) is an important problem because they affect the overall performance of the system. This problem, however, has not yet been fully discussed. In the present paper, a systematic design method for this problem is developed. The main concept is to express the model of the system as a Mixed Logical Dynamical System (MLDS) and to formulate the problem as a mathematical programming problem. The developed idea is applied to the line‐following control of a two‐wheeled vehicle. The usefulness of the proposed method is demonstrated through simulation and experiments. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(2): 51–60, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20584     

磁悬浮系统模型的研究

为了设计磁悬浮系统的单磁体控制器,必须建立系统的状态空间模型。分析了磁悬浮系统的工作原理,结合经典动力学和电磁学基本理论,利用Lagrange方程的一般形式导出系统的非线性运动方程,并通过在工作点附近的线性化处理,得到系统数学模型的状态空间描述。仿真结果表明,所提出的系统模型是可行且有效的。     

Maximum‐power operation of a stand‐alone PV system using fuzzy logic control

The PV array has a unique operating point that can supply maximum power to the load. This point is called the maximum‐power point (MPP), and is located on the output characteristics of the array. The locus of this point varies, non‐linearly, with the unpredictable variations in the solar insolation and/or the cell temperature. Thus, to operate the PV array at its MPPs for every instant, the PV system must contain a maximum‐power point tracking (MPPT) controller. This article proposes the application of the fuzzy logic controller (FLC) to operate a suggested PV control system at the MPP of the array for every instant. The simulation of the system indicates that the use of the FLC gives good results for the MPPT. Copyright © 2002 John Wiley & Sons, Ltd.     

Comparative seeker and bio-inspired fuzzy logic controllers for power system stabilizers

Seeker optimization algorithm (SOA) is a new heuristic population-based search algorithm. In this paper, SOA is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). In SOA, the act of human searching capability and understandings are exploited for the purpose of optimization. In SOA-based optimization, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (namely PSS2B, PSS3B and PSS4B) are optimally tuned to obtain the optimal transient performances. From simulation study it is revealed that the transient performance of the dual-input PSS is better than the single-input PSS. It is further explored that among the dual-input PSSs, PSS3B offers the best optimal transient performance. While comparing the SOA with recently reported optimization algorithms like bacteria foraging optimization (BFO) and genetic algorithm (GA), it is revealed that the SOA is more effective than either BFO or GA in finding the optimal transient performance. Sugeno fuzzy logic (SFL)-based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer parameters.     

Improvement of levitation characteristics in a magnetic bearing system using HTSC‐permanent magnet hybrid structure

Magnetic bearing using pinning force of a permanent magnet and a high‐temperature superconductor has been developed. Additional permanent magnet is introduced to increase the levitation force of the magnetic bearing. In this hybrid magnetic bearing system, levitation force is mainly given by the repulsive force of the permanent magnets, and stability for the lateral direction is given by pinning force of the superconductor. The experimental device is developed. A ring‐type superconductor and a bulk one are examined. Levitation characteristics of the hybrid magnetic bearing are measured. The bulk superconductor shows better characteristics of both levitation and lateral stability than the ring one. Levitation force of the hybrid system becomes about twice as large as that of the nonhybrid one. Although, the repulsive force of the permanent magnet decreases the lateral stability of the system, its influence becomes small by choosing an adequate position of the permanent magnets and the superconductor. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 150(1): 71–77, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10350     

视轴稳定系统的模糊PID控制器设计

为了实现无人机视轴稳定系统的准确测量与跟踪,设计了经典PID和模糊控制为基础的视轴稳定控制器。在实际工程中PID参数整定过程存在大量不确定性,为了实现PID参数的在线整定,将模糊控制算法与经典PID控制相结合,构造了参数自整定模糊PID控制器,实现了对PID控制器的修正。在MATLAB中的Fuzzy Toolbox和Simulink中,将PID和参数自整定模糊PID进行对比,参数自整定模糊PID控制器在无扰动和10 Hz的正弦扰动的阶跃响应曲线表明,模糊PID相对于模糊控制和PID控制有更短的响应时间和更小的隔离度;在输入为1~10 Hz的系统正弦响应曲线,模糊PID误差最小,控制效果最好。由此可得参数自整定模糊PID在视轴稳定系统中有良好的鲁棒性和控制性能。     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

基于NARX神经网络预测及模糊控制的互联电网CPS鲁棒控制策略研究

对于传统CPS控制策略难以满足互联电力系统对鲁棒性和适应性的要求,提出了一种将NARX神经网络预测算法和模糊逻辑控制器相结合的控制方法。配合CPS下的传统PI控制器,根据CPS控制参数的预测值与当前值之间的偏差值,实现对AGC机组的预控制。利用Matlab的Simulink仿真软件建立了一个双区域电力系统的控制模型。仿真结果表明,新的控制方法不仅达到了改善CPS控制效果的目的,并且提高了CPS1,CPS2指标的考核率,减少了机组的调节次数,降低了运行费用,取得了一定的经济效益。     

Magnetic damping of the electrodynamic suspension‐type superconducting levitation system

The damping characteristic of the bogie motion is one of the most important issues to be investigated in the superconducting magnetically levitated system based on electrodynamic suspension (EDS). We have therefore studied the damping characteristics and the mechanism of magnetic damping. Electric circuits consisting of coil resistances and inductances can describe the electromagnetic behavior of the levitation system. By numerical calculation based on the electric‐circuit equations combined with the equations of motion, damping characteristics of the bogie are examined for the system consisting only of levitation coils and superconducting coils, and also for the configuration with magnetic shielding structures incorporated. Furthermore, paying attention to the energy balance in the levitation and propulsion system when the bogie is forced to oscillate sinusoidally, we have investigated the relation between the input energy and the running speed, and compared the waveforms of levitation force and bogie oscillation to consider the characteristics and the mechanism of the damping. © 1999 Scripta Technica, Electr Eng Jpn, 127(2): 49–60, 1999     

Optimal tunning of type-2 fuzzy logic power system stabilizer based on differential evolution algorithm

In this paper, a type-2 fuzzy logic power system stabilizer with differential evolution algorithm is proposed. As an extension of type-1 fuzzy logic theory, type-2 fuzzy logic theory can effectively improve the control performance by uncertainty of membership function especially when we have to confront with less expert knowledge or unpredicted external disturbances. The corresponding parameters and rule base of type-2 fuzzy logic power system stabilizer are optimally tuned by using differential evolution algorithm for multi-machine power system. Through simulation under different operational conditions, the results demonstrate the effectiveness of the proposed approach for damping the power system electromechanical oscillations.     

改善系统暂态稳定性的HVDC模糊神经控制器

提出了一种HVDC在线模糊神经控制器以提高交直流系统的暂态稳定性。该控制器的特点是结合了模糊系统处理复杂和不确定性问题及神经网络具有自学习能力的优点,选取整流侧交流母线电压相位误差及其变化率作为模糊逻辑控制部分的输入,其输出结果作为神经网络的一个输入,采用改进BP算法进行在线训练神经网络,神经网络的输出用来修正整流器的触发角,并利用NETOMAC软件对控制器主要参数进行了离线优化。仿真结果表明该控制器能有效地抑制有功功率振荡,改善发电机的功角特性,提高系统的暂态稳定性。     

基于QPSO-RBFNN和模糊理论的电力系统短期负荷预测方法

在基于径向基函数神经网络(RBFNN)的电力系统短期负荷预测的基础上,采用量子粒子群优化算法(QPSO)优化神经网络权值,并运用模糊理论进行修正预测模型,提出基于QPSO-RBFNN和模糊理论的电力系统短期负荷预测方法.仿真实例计算结果表明该方法收敛速度快、预报精度高,具有工程应用前景.