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1.
PID调节是自动化领域中应用最广的控制策略,其参数选取的优劣决定了系统的动态响应性能。针对参数的优化问题,应用归一化遗传算法进行PID参数的在线整定,并基于M.SrinivasL.M.Patnaik理论,采用了自适应算子来提高整定的效率和精度,并与一般遗传算法进行对比、分析PID整定后的动态响应性能。仿真结果表明:归一化遗传算法整定的最优指标函数的收敛速度比一般遗传算法提高了30代;稳态时间仅为52ms,比一般遗传算法提高了10ms;归一化整定后信号跟踪误差比一般遗传算法低了0.001。因此,采用自适应算子的归一化遗传算法在PID整定效果优于一般GA算法,大幅提高了PID整定效率和系统动态响应性能。  相似文献   

2.
粒子群优化算法(PSO)是一种基于种群搜索策略的自适应随机优化算法,本文在分析基本PSO算法进化原理的基础上,提出了一种PID参数进行自整定的计算框架,这种改进的PSO算法应用于PID参数整定,对整个控制器参数空间进行高效并行搜索,提高其优化性能。以某超临界电厂主汽温度为研究对象,通过MATLAB仿真证明了所提出算法的有效性和所设计控制器的优越性。  相似文献   

3.
遗传算法作为一种基于生物进化机制的自适应算法,适用于各类复杂系统的优化计算。然而标准遗传算法所具有的易早熟、易陷入局部最优等问题,在一定程度上限制了遗传算法的推广和使用。在对遗传算子做出改进的基础上,提出了一种基于小种群策略的并行遗传算法,从而有效地提高了遗传算法的执行效率和性能。  相似文献   

4.
PID调节器是最早发展起来的控制策略之一,遗传算法是一种借鉴生物界自然选择和自然遗传学机理上的迭代自适应概率性搜索算法。本文探讨了基于遗传算法的PID控制器参数优化设计。通过适应度函数来确定寻优方向,与其他一些常规整定方法相比,遗传算法比较简便,整定精度较高。  相似文献   

5.
针对标准遗传算法在对城市空间增长分析时还存在精度不高、误差较大等问题,提出了一种基于种群优化遗传算法的城市空间增长分析模型,该模型在标准遗传算法的基础上,首先采用动态自适应调整策略对原算法遗传算子中的交叉算子和变异算子进行优化,然后引入蚁群算法,利用小生境方法限制种群个体的繁衍,以达到种族多样化的优化。仿真试验结果表明,本文提出的基于种群优化遗传算法的城市空间增长分析模型相对于标准遗传算法,其精度得到了很大的提升,降低了城市空间增长预测的误差。  相似文献   

6.
主要介绍了遗传算法以及一种基于遗传算法的反应炉温度PID控制方法,并运用遗传PID对反应炉温度控制器参数进行优化。通过MATIAB仿真结果表明,与常规PID相比,遗传PID控制系统具有更高的控制精度和更快的反应速度,鲁棒性也有一定的提升。当被控对象为反应炉这类存在较大纯滞后特性的被控对象时,采用本文所述的遗传PID控制器可获得比一般的PID控制器更好的调节效果。  相似文献   

7.
双容水箱的液位具有非线性、时滞等特性,传统的控制方法很难保证系统的控制品质,采用单神经元自适应PID控制器,对控制器参数实行实时在线优化调整,其控制效果优于传统的PID控制器。  相似文献   

8.
针对PID算法在PLC控制中还存在稳定性不高、精确性较差的问题,本文根据膨胀烟丝加香控制的需求,提出了一种基于参数整定PID算法的膨胀烟丝加香实时控制模型。首先构建基于PLC的膨胀烟丝加香闭环控制系统,然后建立加香泵的近似模型,并引入遗传算子对PID算法进行参数整定优化,在目标函数中加入控制输入的平方项以防止控制能量过大,并采用惩罚机制避免超调,接着对交叉算子和变异算子进行自适应调整,最后采用线性函数对加香比例设定值进行实时修正。通过实例仿真表明,相比较标准PID算法,本文提出的参数整定PID算法具有较为平稳的波形,鲁棒性较高,并且本文对加香比例进行实时修正,大大减小了加香的误差。  相似文献   

9.
安宁  邱玮炜 《内江科技》2010,31(8):88-88
根据矿井并下掘进工作面的工作环境要求,对传统的PID控制方法进行改进,提出了一种利用遗传算法对PID控制器参数进行整定的矿井局部通风机控制方法,基于遗传算法的PID控制器根据掘进工作面的工作环境对局部通风机的转速进行实时调整一控制系统能够达到较高的控制精度和较好的稳定性。  相似文献   

10.
利用遗传算法的全局优化能力和径向基网络相结合,得到了一种PID控制策略。通过遗传算法,实现对PID参数的优化整定,并给出了具体的遗传优化算法。同时,用径向基网络进行系统辨识,从而实现了PID参数自整定的控制器。最后给出汽车CIMCAR-1运动过程的仿真。  相似文献   

11.
经典PID控制应用广泛,但由于其参数的工程整定方法一般为试探法,这样对于设计人员的调试经验要求较高。随着工程技术的发展,被控对象也越来越复杂,经典PID参数整定也变得复杂,本文提出了一种基于遗传算法优化的PID控制器,PID的参数不仅可以自动整定而且支持在线整定。  相似文献   

12.
The interconnected large-scale power systems are liable to performance degradation under the presence of sudden small load demands, parameter ambiguity and structural changes. Due to this, to supply reliable electric power with good quality, robust and intelligent control strategies are extremely requisite in automatic generation control (AGC) of power systems. Hence, this paper presents an output scaling factor (SF) based fuzzy classical controller to enrich AGC conduct of two-area electrical power systems. An implementation of imperialist competitive algorithm (ICA) is made to optimize the output SF of fuzzy proportional integral (FPI) controller employing integral of squared error criterion. Initially the study is conducted on a well accepted two-area non-reheat thermal system with and without considering the appropriate generation rate constraint (GRC). The advantage of the proposed controller is illustrated by comparing the results with fuzzy controller and bacterial foraging optimization algorithm (BFOA)/genetic algorithm (GA)/particle swarm optimization (PSO)/hybrid BFOA-PSO algorithm/firefly algorithm (FA)/hybrid FA-pattern search (hFA-PS) optimized PI/PID controller prevalent in the literature. The proposed approach is further extended to a newly emerged two-area reheat thermal-PV system. The superiority of the method is depicted by contrasting the results of GA/FA tuned PI controller. The proposed control approach is also implemented on a multi-unit multi-source hydrothermal power system and its advantage is established by Correlating its results with GA/hFA-PS tuned PI, hFA-PS/grey wolf optimization (GWO) tuned PID and BFOA tuned FPI controllers. Finally, a sensitivity analysis is performed to demonstrate the robustness of the proposed method to broad changes in the system parameters and size and/or location of step load perturbation.  相似文献   

13.
This paper presents the design and performance analysis of Proportional Integral Derivate (PID) controller for an Automatic Voltage Regulator (AVR) system using recently proposed simplified Particle Swarm Optimization (PSO) also called Many Optimizing Liaisons (MOL) algorithm. MOL simplifies the original PSO by randomly choosing the particle to update, instead of iterating over the entire swarm thus eliminating the particles best known position and making it easier to tune the behavioral parameters. The design problem of the proposed PID controller is formulated as an optimization problem and MOL algorithm is employed to search for the optimal controller parameters. For the performance analysis, different analysis methods such as transient response analysis, root locus analysis and bode analysis are performed. The superiority of the proposed approach is shown by comparing the results with some recently published modern heuristic optimization algorithms such as Artificial Bee Colony (ABC) algorithm, Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) algorithm. Further, robustness analysis of the AVR system tuned by MOL algorithm is performed by varying the time constants of amplifier, exciter, generator and sensor in the range of ?50% to +50% in steps of 25%. The analysis results reveal that the proposed MOL based PID controller for the AVR system performs better than the other similar recently reported population based optimization algorithms.  相似文献   

14.
数字PID控制器的硬件优化设计   总被引:1,自引:0,他引:1  
研究了PID控制器的数字电路实现方法. 通过对数字PID算法进行流水线设计,提高了算法运行效率;通过对加法器和乘法器采用有符号二进制小数操作,减小了电路面积. 该算法在Actel AFS600芯片上实现,仿真结果表明了该方案的可行性和有效性.  相似文献   

15.
This paper presents a tuning approach based on a tabu search algorithm (TSA) to obtain the optimal proportional-integral-derivative (PID) controller parameters in order to achieve a desired transient response. TSA is used to determine the main parameters of the PID controller. The performance of the PID controlled system is examined by considering the characteristics of the step response of the plant. Simulation results demonstrate that the tabu algorithm based approach is one of the useful methods for PID controller tuning, and using by the presented method, performance of the controlled system can be significantly improved according to the given control specifications.  相似文献   

16.
给出了多连接查询优化问题的计算模型,分析了免疫遗传算法的基本原理,提出将免疫遗传算法应用于多连接查询优化问题。针对多连接查询优化问题的具体特点,给出了免疫遗传算法的设计,包括亲和度、适应度函数的设计,基于抗体浓度的选择算子、交叉算子、变异算子的设计,免疫算子的设计。  相似文献   

17.
智能控制已经从二元论发展到四元论,目前智能推理算法存在缺陷,寻求最优模糊控制系统的有效方法还没有真正给出。文中介绍了智能蕴含算子的概念及4种基本的T-Norms,给出了分配的完备格上的T-Norms关系式的求解算法以及与T(r)相伴的智能蕴含算子相关的计算公式,其作用是提升PID的建模思想,实现调节r,改良推理模型,完成最优控制。  相似文献   

18.
Model reference adaptive control algorithms with minimal controller synthesis have proven to be an effective solution to tame the behaviour of linear systems subject to unknown or time-varying parameters, unmodelled dynamics and disturbances. However, a major drawback of the technique is that the adaptive control gains might exhibit an unbounded behaviour when facing bounded disturbances. Recently, a minimal controller synthesis algorithm with an integral part and either parameter projection or σ-modification strategies was proposed to guarantee boundedness of the adaptive gains. In this article, these controllers are experimentally validated for the first time by using an electro-mechanical system subject to significant rapidly varying disturbances and parametric uncertainty. Experimental results confirm the effectiveness of the modified minimal controller synthesis methods to keep the adaptive control gains bounded while providing, at the same time, tracking performances similar to that of the original algorithm.  相似文献   

19.
张慧  邢培振 《科技通报》2012,28(4):156-158
针对数据库多连接查询优化问题,提出一种基于遗传禁忌算法的数据库多连接查询优化策略。把遗传算法作为查询优化的主框架,禁忌搜索作为遗传算法的变异算子,增加种群多样性,克服遗传算法收敛慢、局部搜索能力差等缺陷。仿真结果表明,遗传禁忌算法加快了求解数据库多连接查询优化问题的速度,而且提高了查询优化效率,得到较满意的查询优化结果。  相似文献   

20.
This paper proposes to use a hybrid Stochastic Fractal Search (SFS) and Local Unimodal Sampling (LUS) based multistage Proportional Integral Derivative (PID) controller consisting of Proportional Derivative controller with derivative Filter (PDF) plus (1 + Proportional Integral) for Automatic Generation Control (AGC) of power systems. Initially, a single area multi-source power system consisting of thermal hydro and gas power plants is considered and parameters of Integral (I) controller is optimized by Stochastic Fractal Search (SFS) algorithm. The superiority of SFS algorithm over some recently proposed approaches such as optimal control, Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO) is demonstrated. To improve the system performance further, LUS is subsequently employed. The study is further extended for different controllers like PID, and proposed multistage PID controller and the superiority of multistage PID controller over conventional PID controller structure is demonstrated. The study is further extended to a two-area six unit multi-source interconnected power system and the superiority of proposed approach over, TLBO and optimal control is demonstrated. Finally the study is extended to a three unequal area system power system with appropriate nonlinearities such as Generation Rate Constraint (GRC), Governor Dead Band (GDB) and time delay. From the analysis, it is found that hybrid SFS–LUS algorithm is superior to the original SFS algorithm and substantial improvement in system performance are realized with proposed multistage PID controller over conventional PID controller structure.  相似文献   

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