The Distribution Population-based Genetic Algorithm for Parameter Optimization PID Controller

Enlightened by distribution of creatures in natural ecology environment, the distributionpopulation-based genetic algorithm (DPGA) is presented in this paper. The searching capability ofthe algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and thesimulation results show that satisfactory performances are obtained.

Parameters Optimization of the Heating Furnace Control Systems Based on BP Neural Network Improved by Genetic Algorithm

The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the pure time delay and nonlinear time-varying.Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting(Z-N)method.A heating furnace for the object was simulated with MATLAB,simulation results show that the control system has the quicker response characteristic,the better dynamic characteristic and the quite stronger robustness,which has some promotional value for the control of industrial furnace.

Multi-objective optimization based on Genetic Algorithm for PID controller tuning

To get the satisfying performance of a PID controller, this paper presents a novel Pareto-based multi-objective genetic algorithm (MOGA), which can be used to find the appropriate setting of the PID controller by analyzing the pareto optimal surfaces. Rated settings of the controller by two criteria, the error between output and reference signals and control moves, are listed on the pareto surface. Appropriate setting can be chosen under a balance between two criteria for different control purposes. A controller tuning problem for a plant with high order and time delay is chosen as an example. Simulation results show that the method of MOGA is more efficient compared with traditional tuning methods.

Control parameter optimal tuning method based on annealing-genetic algorithm for complex electromechanical system

A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings.The difference from GA is that AGA takes objective function as adaptability function directly,so it cuts down some unnecessary time expense because of float-point calculation of function conversion.The difference from SAA is that AGA need not execute a very long Markov chain iteration at each point of temperature, so it speeds up the convergence of solution and makes no assumption on the search space,so it is simple and easy to be implemented.It can be applied to a wide class of problems.The optimizing principle and the implementing steps of AGA were expounded. The example of the parameter optimization of a typical complex electromechanical system named temper mill shows that AGA is effective and superior to the conventional GA and SAA.The control system of temper mill optimized by AGA has the optimal performance in the adjustable ranges of its parameters.

Development of Genetic Algorithm (GA) Based Optimized PID Controller for Stability Analysis of DC-DC Buck Converter

This paper delineates a conventional buck converter controlled by optimized PID controller where Genetic Algorithm (GA) is employed with a view to enhancing the performance by analyzing the performance parameters. Genetic Algorithm is a probabilistic search algorithm which is substantially used as an optimization technique in power electronics. A bunch of modifications have already been introduced to enhance the performance depending upon the applications. However, in this paper, modified genetic algorithm has been used in order to tune the key parameters in the converter. Hence, an analysis is carried out where the performance of the converter is illustrated in terms of rise time, settling time and percentage of overshoot by deploying GA based PID controller and the overall comparative study is presented. Responses of the overall system are accumulated through rigorous simulation in MATLAB environment.

Design of PID controller with incomplete derivation based on ant system algorithm

A new and intelligent design method for PID controller with incomplete derivation is proposed based on the ant system algorithm ( ASA) . For a given control system with this kind of PID controller, a group of optimal PID controller parameters K p * , T i * , and T d * can be obtained by taking the overshoot, settling time, and steady-state error of the system's unit step response as the performance indexes and by use of our improved ant system algorithm. K p * , T i * , and T d * can be used in real-time control. This kind of controller is called the ASA-PID controller with incomplete derivation. To verify the performance of the ASA-PID controller, three different typical transfer functions were tested, and three existing typical tuning methods of PID controller parameters, including the Ziegler-Nichols method (ZN),the genetic algorithm (GA),and the simulated annealing (SA), were adopted for comparison. The simulation results showed that the ASA-PID controller can be used to control different objects and has better performance compared with the ZN-PID and GA-PID controllers, and comparable performance compared with the SA-PID controller.

基于模糊-遗传算法的粗纱机出料传动系统PID参数优化方法

目前常规的粗纱机出料传动系统PID参数优化方法主要通过结合被控对象的稳态增益情况构建出优化函数,实现PID参数优化整合,忽略了实际运行工况下的干扰成分,导致参数优化效果不佳。对此,提出基于模糊-遗传算法的粗纱机出料传动系统PID参数优化方法。首先结合PID的系统阶跃响应曲线,以被控量的偏差变化率作为主要参数选取性能指标;然后通过引入负荷扰动模块以及频率干扰模块,对PID调节模块进行建模分析;最后结合模糊-遗传算法对最优适应度值进行求解,从而实现PID参数的有效优化,并对提出的方法进行优化效果检验。最终测试结果表明,采用提出的方法对PID参数进行优化整合时,控制输出曲线与理论输出曲线的拟合程度更高,具备较为理想的优化效果。

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 measured 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 supplementary 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(GAPID) and particle swarm optimization(PSO) technique(PSOPID) 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.

Novel approach for active vibration control of a flexible missile

This paper investigates the feasibility of using an active dynamic vibration absorber(ADVA) for active vibration control of a flexible missile system through simulation.Based on the principles of a dynamic vibration absorber(DVA),a ring-type ADVA is first designed to attenuate the elastic vibration of the flexible missile,and the design of the active controller adopts the proportional-integral-derivative(PID)control algorithm.The motion equations of a flexible missile with an ADVA,which is subjected to follower thrust at its aft end,are derived using the Lagrangian approach.Taking the minimum of the root mean square(RMS) of the lateral displacement response of the center of mass as the objective function,a genetic algorithm(GA) is used to optimize the parameter of the DVA and PID controller.The numerical calculations show that the ADVA and DVA are effective in suppressing the vibration and provide approximately 41.2% and 17.6% improvement,respectively,compa red with the case of no DVA,The ADVA has better performance than the DVA,When the missile is subjected to follower thrust,the effect of vibration reduction is more effective than the case without follower thrust.It is feasible to reduce vibration and improve the stability of flexible missiles by means of the ADVA.

充分搜索多策略花授粉算法在PID参数优化中的应用

PID控制器广泛应用于工业领域,结构简单,控制效果良好,参数对控制器起到了绝对作用。花授粉算法是一种应用广泛的元启发算法,但存在易陷入局部最优、迭代后期收敛速度慢、寻优精度差等不足。用加入随机扰动的反双曲正切函数充分搜索策略的转换概率替换原本的固定概率,平衡全局搜索和局部搜索;在异花授粉中引入新型动态因子,改变母系花粉位置的影响;在自花授粉阶段提出权重学习策略,让花粉向优秀花粉方向聚拢;引入翻筋斗探索策略,加大种群多样性。在此基础上提出充分搜索下多策略花授粉算法(MSFPA),对比分析了花授粉算法(FPA)、粒子群算法(PSO)、差分进化算法(DE)和象群算法(EHO)对9个测试函数的仿真实验,结果表明MSFPA算法收敛速度快,性能更优。将MSFPA算法应用于PID参数优化中,经过优化的系统超调量较小,且调整时间较短,有较强的稳定性。

改进麻雀搜索算法的四旋翼PID参数优化设计

针对四旋翼无人机PID控制器参数人工整定复杂、费时且难以确保最优的问题,提出一种基于改进麻雀搜索算法的PID参数优化方法,用于四旋翼无人机控制系统。首先引入Piecewise混沌映射,增强初始种群分布的均匀性;其次在探索者位置更新公式中加入动态惯性权重,提升算法全局优化性能;最后结合自适应混合变异策略和历史最优值,增强变异的多样性、提高算法的搜索效率及精度。仿真结果表明,相比于标准麻雀搜索算法、粒子群算法和遗传算法,用改进麻雀搜索算法优化串级PID控制器参数,能够使控制系统具有更快的响应速度、更高的稳态精度。

Optimal Tuning of Plant-Friendly PID Controllers

A plant-friendly proportional-integral-derivative （PID） controller optimization framework is proposed to make tradeoffs among set-point response,controller output variations and robustness.The objective function is chosen as the weighted sum of the integral of squared time-weighted error and the integral of squared timeweighted derivative of the control variable with respect to set-point response,while the robustness of the system is guaranteed by constraints on gain and phase margins.Due to the complex structure of the constraints,the problem is solved by genetic algorithms.Simulation analysis show the proposed method could efficiently reduce the controller output variations while maintaining a short settling time.Based on the simulation results,iterative tuning rules for the weighting factor in the objective function are obtained,which allows efficient simple proportional-integral（PI） tuning formulae to be derived.

基于GSA的水轮机调速器PID控制参数优化方法

针对现有水轮机调速器控制参数优化研究依赖系统特征参数值、忽视发电机模型的不确定性以及控制策略较落后等问题,以引力搜索算法(GSA)为基础,对水轮机调速器PID控制参数优化模型和优化方法等进行分析,仿真模拟优化中国某水电机组,比较不同状态下水轮机调速器的组合控制效果。结果表明,在孤网运行条件下,基于GSA的水轮机调速器PID控制参数优化方法对水轮机组动态运行品质的提升效果最佳。

洁净空调冷水阀门一阶惯性滞后PID独立控制方法

冷水阀门一阶惯性控制偏差项造成洁净空调温湿度控制效果较差。为此,提出洁净空调冷水阀门一阶惯性滞后PID独立控制方法。该方法在考虑室内环境基本信息的条件下,建立环境与冷水阀的一阶惯性滞后模型;计算室内洁净空调温湿度新风量,将其作为洁净空调冷水阀门的温湿度独立控制的约束;利用自适应免疫遗传算法优化PID控制器参数,以此为依据实现洁净空调冷水阀门温湿度的独立控制。实验结果表明,该方法控制效果较好、节能率较高。

基于PID控制算法的恒温控制器设计与研究

文章提出了一种基于PID控制算法的恒温控制器,满足了用户对舒适泡澡体验的需求,提高了恒温澡盆的智能化和便携性。文章设计了闭环恒温控制器热导信号的采集、放大、滤波以及温度闭环控制等环节;根据所采集的温度数据,设计比例、积分、微分参数,计算目标温度与实际温度之间差值,调节恒温控制器的参数以获得最优的加热方式。实验结果表明,该恒温控制器能够将最终温度误差控制在±0.1℃,提高了水温的加热效率,实现了更精确的温度控制效果。

基于蝙蝠算法的PID控制参数优化技术与仿真研究

PID参数整定是PID控制中的一个重要环节,传统的PID参数整定方法已经不能完全适用。为提高PID参数优化精度,解决传统PID参数整定时产生的误差较大问题,将蝙蝠算法引入控制系统中优化PID控制参数。通过MATLAB仿真,比较蝙蝠算法、粒子群优化算法和增量式PID控制算法对控制参数优化的性能。实验结果表明:在函数寻优测试中,与遗传算法、粒子群优化算法相比,蝙蝠算法能防止陷入局部最优,使种群更加稳定并达到更好的收敛速度和寻优精度;在PID控制参数优化中,与粒子群优化算法、增量式PID控制算法相比,蝙蝠算法优化PID控制参数的实际输出曲线最贴近理论输出曲线,稳定性更好。

Parameter Optimization of Linear Quadratic Controller Based on Genetic Algorithm

The selection of weighting matrix in design of the linear quadratic optimal controller is an important topic in the control theory. In this paper, an approach based on genetic algorithm is presented for selecting the weighting matrix for the optimal controller. Genetic algorithm is adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this algorithm, the fitness function is used to evaluate individuals and reproductive success varies with fitness. In the design of the linear quadratic optimal controller, the fitness function has relation to the anticipated step response of the system. Not only can the controller designed by this approach meet the demand of the performance indexes of linear quadratic controller, but also satisfy the anticipated step response of close-loop system. The method possesses a higher calculating efficiency and provides technical support for the optimal controller in engineering application. The simulation of a three-order single-input single-output (SISO) system has demonstrated the feasibility and validity of the approach.

遗传算法优化的空气悬架模糊PID控制

针对空气悬架系统模糊PID控制方法中参数选取存在局限性和难以选取的问题,利用遗传算法优秀的全局优化搜索能力和并行能力,提出了一种用遗传算法来优化模糊PID控制器参数的策略。采用遗传算法对空气悬架的模糊PID控制器参数进一步优化,比较优化前后模糊PID控制下的空气悬架性能指数值。结果表明,采用遗传算法优化的模糊PID控制空气悬架较传统模糊PID控制以及普通的空气悬架,能够减小车辆振动,提升空气悬架性能,改善车辆的平顺性与舒适性。

灰狼算法优化的PID控制器在实验焦炉温度控制中的应用

针对焦化领域实验焦炉温度控制问题,提出了一种基于灰狼优化算法优化PID参数(GWO-PID)的方法。首先,将焦炉温度控制的数学模型,简化为一个二阶带有纯滞后的模型。然后,利用灰狼优化算法对PID控制器的参数进行优化,提高了控制器的控制精度和鲁棒性。实验结果表明,灰狼算法优化后的PID参数,有效克服传统PID的参数僵化,该方法满足中小型实验焦炉温度控制要求,大大减少响应时间,可以实现无震荡、无超调。

Optimal Robust Control for Unstable Delay System

Proportional-Integral-Derivative control system has been widely used in industrial applications.For uncertain and unstable systems,tuning controller parameters to satisfy the process requirements is very challenging.In general,the whole system’s performance strongly depends on the controller’s efficiency and hence the tuning process plays a key role in the system’s response.This paper presents a robust optimal Proportional-Integral-Derivative controller design methodology for the control of unstable delay system with parametric uncertainty using a combination of Kharitonov theorem and genetic algorithm optimization based approaches.In this study,the Generalized Kharitonov Theorem(GKT)for quasi-polynomials is employed for the purpose of designing a robust controller that can simultaneously stabilize a given unstable second-order interval plant family with time delay.Using a constructive procedure based on the Hermite-Biehler theorem,we obtain all the Proportional-Integral-Derivative gains that stabilize the uncertain and unstable second-order delay system.Genetic Algorithms(GAs)are utilized to optimize the three parameters of the PID controllers and the three parameters of the system which provide the best control that makes the system robust stable under uncertainties.Specifically,the method uses genetic algorithms to determine the optimum parameters by minimizing the integral of time-weighted absolute error ITAE,the Integral-Square-Error ISE,the integral of absolute error IAE and the integral of time-weighted Square-Error ITSE.The validity and relatively effortless application of presented theoretical concepts are demonstrated through a computation and simulation example.