电液数字伺服双缸系统PSO-PID同步控制

将粒子群算法与传统的PID控制器相结合,并采用平方误差矩积分函数作为适应度判据,构成了PSO-PID控制器。以电液数字伺服双缸系统的PID参数优化为例,使用粒子群优化算法对PID参数进行优化。仿真结果显示:PSO算法使得PID控制参数调整速度较传统方法快,产生超调量较小,该方法的调节效果较好。     

基于粒子群优化的滑模控制参数整定

针对滑模控制参数难整定的问题,结合粒子群优化算法设计滑模控制器。选取有关控制器输出和系统跟随误差的函数作为滑模参数优化目标,并以该目标函数为粒子群的适应度值,寻优处理后获得等效控制、切换项及指数趋近率系数,从而缩短到达滑模面时间,减小控制器输出抖振,有效改善滑动阶段动态特性。在MATLAB中仿真表明:该方法对被控对象参数波动具有较强鲁棒性,系统响应快,跟随性能好。     

自适应变异粒子群算法实现的回转支承运动控制系统优化

以提高回转支承运动控制系统响应性能和速度跟随性能为目的,提出了一种自适应变异粒子群算法来优化系统内部速度环PI控制器参数,从而优化系统运动控制性能的方法。将遗传算法中的变异操作引入粒子群算法中,加入粒子变异环节,形成自适应变异粒子群算法,该算法能够克服普通粒子群算法易陷入局部最优的缺陷,同时消除控制器初始参数选择不当对系统性能的影响。将仿真所得PI控制器参数输入所搭建的系统中,实验结果显示该控制策略取得了满意的控制效果。     

PID控制器的量子粒子群多目标优化设计

智能算法如粒子群算法已被应用于PID控制器的参数优化,以弥补传统优化方法容易产生振荡和较大超调量的不足,但是粒子群算法存在易于早熟的缺点,在分析量子粒子群算法的基础上,提出了使用量子粒子群算法优化PID控制器的参数.为了兼顾控制系统的各项性能指标,根据控制器的实际要求对各项指标进行加权作为算法的目标函数,对PID控制器进行多目标寻优.通过2个传递函数实例,分别使用Z-N、粒子群算法和量子粒子群算法进行了PID控制器参数优化设计,并对结果进行了分析.     

基于粒子群算法的参数自整定伺服控制器的设计

提出了一种可以不考虑系统的数学模型以及外部的工作状况,直接通过在可行域内搜索最优解来找到合适的控制器参数的方法。以伺服系统的速度环控制器为研究对象,将粒子群优化算法应用于控制器的参数自整定中,设计了参数自整定PI控制器。在Matlab／Simulink环境下建立永磁同步电机伺服系统的仿真模型,对比了通过人工参数整定与基于粒子群优化算法的参数整定两种方法。仿真实验结果表明,该方法整定出的控制器参数可以使伺服系统的性能得到较大提高,具有较大的应用价值。     

改进粒子群算法应用于挖掘机铲斗位置控制

挖掘机工作装置精确的位置控制是实现其轨迹自动控制的基础。提出一种改进粒子群优化算法,应用于液压系统PID参数的优化整定中,把遗传算法中的选择和交叉两种操作方式添加到标准的粒子群算法中形成的混合优化算法,提高了算法的搜索能力。建立具有整定PID控制器功能的仿真平台,使用改进粒子群算法、标准粒子群算法和相位裕度方法对PID控制器进行整定仿真,根据仿真结果,进行了模拟铲斗平地运动试验。仿真和试验结果表明改进粒子群算法整定的PID控制器参数,在电液伺服系统的动态响应和精确的轨迹控制方面有良好效果。     

探究使用粒子群算法优化电机PI控制效果及耗费时间

在以往采用粒子群算法优化控制系统参数过程中，评价函数是考虑粒子群算法整定的控制效果是否符合系统要求的重要依据，并没有考虑算法整定参数所耗费的时间。文中分别对推进电机静态负荷和动态负荷仿真模型采用粒子群算法整定控制参数，通过两种情况下迭代次数中调节时间的变化以及在静态负荷下优化的控制参数代入动态负荷的推进电机仿真模型获得的转速曲线对比分析可知，采用静态负荷的推进电机仿真模型相比推进电机动态负荷模型使用粒子群算法优化控制参数，其仿真效果基本一致，算法运行的仿真时间提升了50%;为后续采用粒子群优化控制模型提供了一种新的思路，即根据使用者的实际需求可以将仿真模型中的部分次要模型简化为静态参数，在不影响仿真结果的基础上降低仿真响应时间，提高仿真效率。     

基于小生境粒子群优化的挖掘机器人自抗扰视觉伺服控制

为提高挖掘机器人的自主挖掘能力,设计一种基于图像的自抗扰视觉伺服控制器,对挖掘机器人的动臂、斗杆、铲斗组成的3节机械臂末端位置和姿态在x-z平面进行控制,实现自主挖掘目标任务。针对自抗扰控制器需要整定的参数较多,参数间相互影响,整定困难的特点,引入粒子群算法对控制器参数进行优化。由于原始粒子群算法存在后期易陷入局部最优的缺欠,采用小生境粒子群算法对自抗扰控制器参数进行整定优化。对粒子群及小生境粒子群算法的优化性能进行比较研究的基础上,设计了适合挖掘机器人的自抗扰视觉伺服控制器,采用小生境粒子群算法得到自抗扰控制器整定参数。搭建xPCTarget主机—目标机环境进行试验及仿真,表明小生境粒子群优化的自抗扰视觉伺服控制器控制精度高、鲁棒性强。     

果蝇优化算法和粒子群优化算法的应用对比

参数优化一直是控制系统重要的一个环节,现在常用的优化算法有蚁群算法、遗传算法、粒子群优化算法。然而现在这些算法普遍存在鲁棒性差、陷入局部最优等缺点。果蝇优化算法作为一种较新的群体优化算法与粒子群有颇多的相似之处。本文以水轮机组调速系统PI控制参数为优化对象,分别以粒子群优化算法和果蝇优化算法进行参数优化,通过分析二者的优化过程与结果,对比了两种优化算法的优缺点,证明了果蝇优化算法的可行性。     

基于CAPSO算法的修正炮弹分数阶控制器设计

为了提高修正炮弹系统模型的控制品质,采用分数阶控制器以取得更优的控制效果。针对分数阶控制器参数整定时大都需要公式推导、计算量大等问题,提出一种基于混沌自适应粒子群优化算法(CAPSO)并用于修正炮弹分数阶控制器的设计。将混沌算法与惯性权重调整的粒子群算法融合,对粒子群进行混沌初始化并对陷入局部最优的粒子进行混沌搜索,同时引入惯性权重非线性调整策略提高了算法的收敛精度,得到全局最优解。利用CAPSO算法对分数阶PIλDμ控制器的参数进行整定,并用于修正炮弹俯仰角稳定回路的控制中。通过仿真实验,验证了该优化算法的可行性。仿真结果表明,CAPSO算法在修正炮弹分数阶控制器的参数整定方面优于主导极点法、粒子群优化算法(PSO)等算法,与PSO算法相比调节时间减少了1.139 s、超调量减小了11.84%,具有收敛速度快、超调量小、稳定性好、抗干扰性强等特点;经CAPSO算法优化的分数阶PIλDμ控制器动态响应特性要优于整数阶PID控制器。     

RTDS implementation of an improved sliding mode based inverter controller for PV system

This paper proposes a novel approach for testing dynamics and control aspects of a large scale photovoltaic (PV) system in real time along with resolving design hindrances of controller parameters using Real Time Digital Simulator (RTDS). In general, the harmonic profile of a fast controller has wide distribution due to the large bandwidth of the controller. The major contribution of this paper is that the proposed control strategy gives an improved voltage harmonic profile and distribute it more around the switching frequency along with fast transient response; filter design, thus, becomes easier. The implementation of a control strategy with high bandwidth in small time steps of Real Time Digital Simulator (RTDS) is not straight forward. This paper shows a good methodology for the practitioners to implement such control scheme in RTDS. As a part of the industrial process, the controller parameters are optimized using particle swarm optimization (PSO) technique to improve the low voltage ride through (LVRT) performance under network disturbance. The response surface methodology (RSM) is well adapted to build analytical models for recovery time (R_t), maximum percentage overshoot (MPOS), settling time (T_s), and steady state error (E_ss) of the voltage profile immediate after inverter under disturbance. A systematic approach of controller parameter optimization is detailed. The transient performance of the PSO based optimization method applied to the proposed sliding mode controlled PV inverter is compared with the results from genetic algorithm (GA) based optimization technique. The reported real time implementation challenges and controller optimization procedure are applicable to other control applications in the field of renewable and distributed generation systems.     

Fuzzy logic speed controller optimization approach for induction motor drive using backtracking search algorithm

This paper presents an adaptive fuzzy logic controller (FLC) design technique for controlling an induction motor speed drive using backtracking search algorithm (BSA). This technique avoids the exhaustive traditional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive MFs are implemented in speed controller design for input and output based on the evaluation results of the fitness function formulated by the BSA. In this paper, the mean absolute error (MAE) of the rotor speed response for three phase induction motor (TIM) is used as a fitness function. An optimal BSA-based FLC (BSAF) fitness function is also employed to tune and minimize the MAE to improve the performance of the TIM in terms of changes in speed and torque. Moreover, the measurement of the real TIM parameters via three practical tests is used for simulation the TIM. Results obtained from the BSAF are compared with those obtained through gravitational search algorithm (GSA) and particle swarm optimization (PSO) to validate the developed controller. Design procedure and accuracy of the develop FLC are illustrated and investigated via simulation tests for TIM in a MATLAB/Simulink environment. Results show that the BSAF controller is better than the GSA and PSO controllers in all tested cases in terms of damping capability, and transient response under different mechanical loads and speeds.     

Fuzzy controller training using particle swarm optimization for nonlinear system control

This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi–Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.     

Design and analysis of tilt integral derivative controller with filter for load frequency control of multi-area interconnected power systems

In this paper, a novel Tilt Integral Derivative controller with Filter (TIDF) is proposed for Load Frequency Control (LFC) of multi-area power systems. Initially, a two-area power system is considered and the parameters of the TIDF controller are optimized using Differential Evolution (DE) algorithm employing an Integral of Time multiplied Absolute Error (ITAE) criterion. The superiority of the proposed approach is demonstrated by comparing the results with some recently published heuristic approaches such as Firefly Algorithm (FA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) optimized PID controllers for the same interconnected power system. Investigations reveal that proposed TIDF controllers provide better dynamic response compared to PID controller in terms of minimum undershoots and settling times of frequency as well as tie-line power deviations following a disturbance. The proposed approach is also extended to two widely used three area test systems considering nonlinearities such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB). To improve the performance of the system, a Thyristor Controlled Series Compensator (TCSC) is also considered and the performance of TIDF controller in presence of TCSC is investigated. It is observed that system performance improves with the inclusion of TCSC. Finally, sensitivity analysis is carried out to test the robustness of the proposed controller by varying the system parameters, operating condition and load pattern. It is observed that the proposed controllers are robust and perform satisfactorily with variations in operating condition, system parameters and load pattern.     

基于粒子群优化的锅炉温度模糊PID控制器设计及其仿真

首先讨论了模糊理论和粒子群(PSO)优化算法,然后针对典型的工业过程控制对象,设计了一种基于PSO优化的二维自适应锅炉温度模糊PID控制器,可以对比例因子和量化因子进行优化。对锅炉温度控制对象系统模型进行辨识,得到锅炉温度的传递函数,在此基础上利用MATLAB进行了仿真。仿真结果表明,采用基于PSO优化的锅炉温度控制与PID控制、模糊控制相比,具有超调量小、上升时间短、响应速度快、调节时间短、稳定性能好、抗干扰能力强等优势。     

基于改进PSO算法的电液位置伺服系统MRAC跟踪控制

针对电液位置伺服系统控制性能不佳的问题,提出一种基于改进PSO算法优化的模型参考自适应(Model Reference Adaptive Control,MRAC)跟踪控制方法。首先,建立电液位置伺服系统数学模型,设计出模型参考自适应控制器;其次,分析PSO算法、APSO算法在参数寻优过程中的不足,提出一种改进的PSO算法;最后,将改进的PSO算法用于模型参考自适应控制器以改善其控制性能。结果表明,改进PSO算法优化的模型参考自适应控制具有响应速度快、跟踪精度高的优点。     

齿轮箱故障诊断的粒子群优化模糊聚类技术

用粒子群算法取代传统的梯度下降法,优化模糊C—均值算法的各个参数,并依据聚类有效性指标确定最优聚类数及聚类中心,有效地利用了粒子群算法全局寻优的优点,克服了模糊C—均值算法极易陷入局部最优的缺点,将经过PSO优化的模糊C-均值算法应用于齿轮箱故障诊断.试验结果表明,粒子群算法是有效的模糊聚类分析优化算法,提高了齿轮箱故障诊断的准确率.     

交流伺服系统分数阶PID改进型自抗扰控制

针对采用永磁同步电机驱动的火箭炮交流伺服系统存在摩擦、惯性力矩、变负载及不同工况下内外扰动等复杂非线性问题,考虑到自抗扰控制(ADRC)抗内外干扰能力强和分数阶PID控制动态性能好,设计了一种分数阶PID改进型自抗扰控制器(FOPID-IADRC)。为了取得良好的动态性能和减少参数计算量,采用分数阶PID控制器取代非线性状态误差反馈器;引入粒子群优化算法,对FOPID控制器的5个控制参数进行实时在线自整定。仿真和半实物台架实验结果表明：该控制策略能够有效抑制位置扰动,具有良好的动态性能和较强的抗干扰能力。     

粒子群优化人工神经网络在高速铣削力建模中的应用

将粒子群优化人工神经网络理论应用于高速铣削力的建模研究中.采用粒子群算法与反向传播算法相结合的方法,对反向传播神经网络模型进行优化.用粒子群算法训练网络参数,直到误差趋于一稳定值,然后用优化的权值进行反向传播算法运算,以实现高速铣削力的预测.充分发挥了粒子群算法的全局寻优能力和反向传播算法的局部搜索优势.仿真结果表明,与其他几种反向传播算法相比较,粒子群算法与反向传播算法的学习算法训练的神经网络,不仅训练时间明显缩短,而且其预报精度也得到了较大的提高,能够有效地建立铣削力模型,并对铣削力进行准确的预测.     

基于信息熵的模拟退火算法优化铣削加工模糊控制器参数

提出以信息熵为性能指标、用模拟退火算法优化模糊控制器参数的新方法应用于铣削加工恒力约束控制。仿真表明:将优化后的模糊控制器应用于切深不变的铣削加工控制中可以取得良好的控制效果;变切深时,可在小范围进行参数优化。