采用遗传优化自适应模糊PID控制球杆系统

为解决球杆系统动态、静态性能不高的问题,提出了遗传算法优化自适应模糊PID控制器的控制方法.该模型在拉格朗日方程建立球杆系统数学模型的基础上,采用遗传算法优化模糊控制规则、隶属函数和自适应PID参数.在GBB1004系统中建立了遗传算法优化后的自适应模糊PID控制器以及控制模型,并对该控制器进行实验验证.实验结果证明了遗传算法优化后的模糊控制器有效地减小了系统的超调量,缩短了系统的调节时间,能够较好地控制球杆系统.     

Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is mea sured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control, automatic generation control (AGC) plays a crucial role. In this paper, multi-area (Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative (PID) controller as a supplemen tary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm (FFA). The experimental results demonstrated the comparison of the proposed system performance (FFA-PID) with optimized PID controller based genetic algorithm (GA PID) and particle swarm optimization (PSO) technique (PSO PID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error (ITAE) cost function with one percent step load perturbation (1% SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.      

Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

This paper deals with the design of a novel fuzzy proportional–integral–derivative (PID) controller for automatic generation control (AGC) of a two unequal area interconnected thermal system. For the first time teaching–learning based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed fuzzy-PID controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the fuzzy-PID controller. The superiority of proposed approach is demonstrated by comparing the results with some of the recently published approaches such as Lozi map based chaotic optimization algorithm (LCOA), genetic algorithm (GA), pattern search (PS) and simulated algorithm (SA) based PID controller for the same system under study employing the same objective function. It is observed that TLBO optimized fuzzy-PID controller gives better dynamic performance in terms of settling time, overshoot and undershoot in frequency and tie-line power deviation as compared to LCOA, GA, PS and SA based PID controllers. Further, robustness of the system is studied by varying all the system parameters from −50% to +50% in step of 25%. Analysis also reveals that TLBO optimized fuzzy-PID controller gains are quite robust and need not be reset for wide variation in system parameters.     

Predictive control of a building hybrid heating system for energy cost reduction

Today, the buildings’ energy consumption is considerable amount of whole. Therefore, optimizing energy in buildings leads to a noticeable decrease in total energy consumption of the world. Energy-efficient buildings have developed by carrying out great research effort. The control procedures serve as a privileged method to help new buildings to comply with the most optimal system as an energy consumer and thus meet ‘nearly zero-energy’.The purpose of this paper is to present a method of controlling the building temperature and simultaneously reducing the cost of providing the hybrid heating systems with sufficient energy. Investigating a room in Tehran city on a day as an example, methods of (a) Model Predictive Control (MPC) with economic optimization (MPC consecutively with On-Off), (b) MPC without economic optimization, (c) Proportional-Integral-Derivative (PID) controller optimized by Genetic Algorithm (GA) in presence of gas thermal source, (d) PID controller optimized with GA in presence of electric thermal source and (e) PID controller optimized with multi-objective GA in the presence of two gas and electric thermal sources have been designed and implemented in this research. Furthermore, the effect of each of these methods on cost reduction and temperature regulation of inside of the room has been studied. Eventually it has been specified that using MPC method with economical optimization has the highest influence on cost reduction and keeps the temperature of inside of the room in the predefined range. This method achieved cost saving of 50% compared to the MPC and GA. But the main targets of this study are both of regulating inside temperature and cost optimization. According to the main targets of this study, using MPC methods without economical optimization and multi-objective genetic algorithm would be more effective.     

Synchronization of general chaotic systems using neural controllers with application to secure communication

The main contribution of this paper is to propose a nonlinear robust controller to synchronize general chaotic systems, such that the controller does not need the information of the chaotic system’s model. Following this purpose, in this paper, two methods are proposed to synchronize general forms of chaotic systems with application in secure communication. The first method uses radial basis function neural network (RBFNN) as a controller. All the parameters of the RBFNN are derived and optimized via particle swarm optimization (PSO) algorithm and genetic algorithm (GA). In order to increase the robustness of the controller, in the second method, an integral term is added to the RBF neural network gives an integral RBFNN (IRBFNN). The coefficients of the integral term and the parameters of IRBFNN are also derived and optimized via PSO and GA. The proposed methods are applied to the famous Lorenz chaotic system for secure communication. The performance and control effort of the proposed methods are compared with the recently proposed PID controller optimized via GA. Simulation results show the superiority of the proposed methods in comparison to the recent one in improving synchronization while using smaller control effort.     

改进遗传算法PID参数整定及在漂白温度控制中的应用

遗传算法（GA）是一种基于群智能的全局随机优化算法。针对简单遗传算法（SGA）收敛速度慢、易于早熟等缺点,采用改进的自适应交叉算子和自适应变异算子。结合兼顾性能指标和响应过程平衡的适配函数,以多种改进方式相结合的遗传算法对PID参数进行寻优整定。并将该控制器应用于纸浆漂白温度控制中,仿真结果表明：改进遗传算法能够明显改善收敛速度和寻优效果,当被控对象存在较大纯滞后、时间常数特性较大时,采用本方法优化PID控制器参数可获得比较满意的控制效果。     

基于模糊自适应PID的无轴传动控制系统研究

研究模糊自适应PID控制算法对无轴传动控制系统的影响,并对控制电机转速同步精度应用不同PID算法进行比较。采用主从电机同步控制策略,并利用Matlab开发了完整的以永磁伺服电机为执行机构的仿真系统。调节控制环参数,得出控制系统的仿真曲线。通过对仿真曲线的分析,比较传统PID和模糊自适应PID算法对转速同步精度的影响,最后指出模糊自适应PID控制算法的控制效果及优越性。     

Supersonic, variable-throat, blow-down wind tunnel control using genetic algorithms, neural networks, and gain scheduled PID

In this paper the design and application of a control algorithm is discussed to control the test conditions within plenum chamber and the test section of a supersonic blow-down, variable throat wind tunnel at the University of Alabama. The artificially intelligent controller algorithm was designed using a gain scheduled Proportional-Integral-Differential (PID) control approach. The PID controller was augmented to work with time variant properties of the control problem by determining a functional form of the integral term of the controller from the governing equations of the tunnel. The controller was optimized using genetic algorithms (GA) on a neural network (NN) model of the tunnel and was compared to a conventional PID controller using the same NN model. The process was repeated for different throat settings to find the control gains for each setting. The controller algorithm was next applied to the actual wind tunnel at different throat settings and the results were compared. The optimized controller is proven to work very well at every throat setting.     

基于GA的模糊-PID控制器设计

提出一种基于GA优化的模糊-PID复合控制算法，该算法可实现在偏离工作点较远的区域采用模糊控制，在工作点附近实施PID控制，两种控制算法优势互补。采用遗传算法分步优化模糊-PID控制器参数，解决了控制器参数整定和优化等难点问题。该算法在SCON-2000先进控制软件平台进行了工程化实现，对实际工业对象的控制结果表明，该方法比常规PID控制、模糊控制具有更强的鲁棒性和更好的稳态性能，能使系统的响应满足既快速又不振荡的要求。     

基于改进遗传算法寻优的神经网络PID控制及应用

根据神经网络PID控制器初值的选取影响系统控制性能的特点,提出了一种基于改进遗传算法寻优的神经网络PID控制方法.即先利用遗传算法对PID控制器参数离线寻优,将求出的参数值作为控制器的比例、积分、微分系数的初值,再进行神经网络PID控制.对一类液位过程的实时控制结果表明采用本方法的控制系统具有较好的稳定性和鲁棒性.     

Modeling, simulation, and control of an electromechanical aerofin control system with a PWM-controlled DC motor

A nonlinear model of an electromechanical actuator (EMA) system for aerofin control is presented. The EMA is realized with a permanent magnet brush DC motor controlled by a constant current driver. In this paper we introduced a simulation model that includes nonlinearities of the motor driver. A PID position controller was designed using the simulation model, although it was impossible to develop a linear model. The simulation results have been of the great importance for understanding the system behavior and successful control system implementation. The proposed control system has experimentally been validated on a test bench. The EMA-AFC test bench is designed to provide real operating conditions. The text was submitted by the authors in English.     

在线知识获得智能模糊控制器研究

对一些复杂的系统。传统PID或模糊控制很难得到满意控制效果,本文提出采用基于RBF神经网络和遗传算法的自适应模糊控制器来进行控制。由遗传算法在线优化模糊控制器的比例因子、模糊推理规则和隶属函数。并由RBF网络辨识被控对象的动态特性,以评价模糊控制器控制性能。仿真实验表明。优化后的Fuzzy控制器具有较强的学习和自适应控制能力,控制效果优于没有寻优的Fuzzy控制。     

Optimized Model Based Controller with Model Plant Mismatch for NMP Mitigation in Boost Converter

In this paper, an optimized Genetic Algorithm (GA) based internal model controller-proportional integral derivative (IMC-PID) controller has been designed for the control variable to output variable transfer function of dc-dc boost converter to mitigate the effect of non-minimum phase (NMP) behavior due to the presence of a right-half plane zero (RHPZ). This RHPZ limits the dynamic performance of the converter and leads to internal instability. The IMC PID is a streamlined counterpart of the standard feedback controller and easily achieves optimal set point and load change performance with a single filter tuning parameter λ. Also, this paper addresses the influences of the model-based controller with model plant mismatch on the closed-loop control. The conventional IMC PID design is realized as an optimization problem with a resilient controller being determined through a genetic algorithm. Computed results suggested that GA–IMC PID coheres to the optimum designs with a fast convergence rate and outperforms conventional IMC PID controllers.     

Sugeno fuzzy PID tuning,by genetic-neutral for AVR in electrical power generation

We report a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system, using a combined genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic approaches. GA and a RBF-NN with a Sugeno fuzzy logic are proposed to design a PID controller for an AVR system (GNFPID). The problem for obtaining the optimal AVR and PID controller parameters is formulated as an optimization problem and RBF-NN tuned by GA is applied to solve the optimization problem. Whereas, optimal PID gains obtained by the proposed RBF tuning by genetic algorithm for various operating conditions are used to develop the rule base of the Sugeno fuzzy system and design fuzzy PID controller of the AVR system to improve the system's response (∼0.005 s). The proposed approach has superior features, including easy implementation, stable convergence characteristic, good computational efficiency and this algorithm effectively searches for a high-quality solution and improve the transient response of the AVR system (7E−06). Numerical simulation results demonstrate that this is faster and has much less computational cost as compared with the real-code genetic algorithm (RGA) and Sugeno fuzzy logic. The proposed method is indeed more efficient and robust in improving the step response of an AVR system.     

基于人工免疫多目标寻优算法的PID自整定

提出了一种基于人工免疫多目标寻优算法(AIMOA)的PID参数自适应整定的设计方法.利用生物免疫系统的免疫机理设计系统响应的目标函数,再通过AIMOA算法搜索PID控制器的优化参数组,最后将基于AIMOA算法同基于遗传算法(GA) 和齐格勒-尼柯尔斯(Zi-Ni)方法的PID自整定进行了仿真比较.结果表明:AIMOA算法具有快速收敛性,能够较快地搜索到PID参数自适应整定的最优或者次最优解,体现了算法的优越性、实用性和有效性.     

无线传感器和执行器网络控制系统设计与仿真

无线传感器和执行器网络通过无线网络(ZigBee)将传感器和执行器与控制器相连,完成分布式传感和执行任务。针对无线网络传输和被控对象延时的不良影响,首先详细介绍了基于TrueTime 2.0的无线网络控制系统在Windows 7和Matlab R2010b环境下的搭建,给出了系统的仿真模型。对控制器节点分别采用常规PID控制和模糊PID控制算法进行仿真研究。仿真结果表明,对于存在延时环节的无线网络控制系统,模糊PID控制可以取得较好的控制效果,系统鲁棒性较强。该研究对物联网智能家居系统的设计具有一定参考价值。     

压电驱动器的免疫PID控制技术研究

针对压电驱动器的伺服控制问题,提出了一种基于生物免疫反馈调节规律的PID控制算法;对于具有非线性滞环特性的压电驱动器,采用线性PID控制器难以达到理想的控制效果;如果借助于生物免疫反馈调节规律来自动整定PID控制器的参数,就有可能使压电驱动器的阶跃响应无振荡现象发生并具有较好的鲁棒性;试验结果表明,与传统的压电驱动器PID控制系统比较,应用免疫PID控制算法可以使伺服系统获得更好的动态特性。     

Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band

An attempt has been made to the effective application of a recently introduced, powerful optimization technique called differential search algorithm (DSA), for the first time to solve load frequency control (LFC) problem in power system. In this paper, initially, DSA optimized classical PI/PIDF controller is implemented to an identical two-area thermal-thermal power system and then the study is extended to two more realistic power systems which are widely used in the literature. To assess the usefulness of DSA, three enhanced competitive algorithms namely comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE), and success history based DE (SHADE) are studied in this paper. Moreover, the superiority of proposed DSA optimized PI/PID/PIDF controller is validated by an extensive comparative analysis with some recently published meta-heuristic algorithms such as firefly algorithm (FA), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), craziness based particle swarm optimization (CRPSO), differential evolution (DE), teaching-learning based optimization (TLBO), particle swarm optimization (PSO), and quasi-oppositional harmony search algorithm (QOHSA). A case of robustness and sensitivity analysis has been performed for the concerned test system under parametric uncertainty and random load perturbation. Furthermore, to demonstrate the efficacy of proposed DSA, the system nonlinearities like reheater of the steam turbine and governor dead band are included in the system modeling. The extensive results presented in this article demonstrate that proposed DSA can effectively improve system dynamics and may be applied to real-time LFC problem.     

Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity

This work presents an application of bio-inspired flower pollination algorithm (FPA) for tuning proportional–integral–derivative (PID) controller in load frequency control (LFC) of multi-area interconnected power system. The investigated power system comprises of three equal thermal power systems with appropriate PID controller. The controller gain [proportional gain (K _p), integral gain (K _i) and derivative gain (K _d)] values are tuned by using the FPA algorithm with one percent step load perturbation in area 1 (1 % SLP). The integral square error (ISE) is considered the objective function for the FPA. The supremacy performance of proposed algorithm for optimized PID controller is proved by comparing the results with genetic algorithm (GA) and particle swarm optimization (PSO)-based PID controller under the same investigated power system. In addition, the controller robustness is studied by considering appropriate generate rate constraint with nonlinearity in all areas. The result cumulative performance comparisons established that FPA-PID controller exhibit better performance compared to performances of GA-PID and PSO-PID controller-based power system with and without nonlinearity effect.

     

基于PSO的钻机快速自适应PID控制

针对传统液压盘刹钻机PID控制系统响应速度慢、稳态误差大、参数整定周期长,以及无法满足随钻遇地层变化实时参数优选的不足,以恒钻压自动送钻为研究对象,构建了液压盘刹钻机控制模型,设计了粒子群算法(PSO)优选钻机PID控制参数,并实现在Simulink环境下自动调用优选出的PID参数,提高了钻机控制参数的快速、自适应整定...