基于微粒群算法的PID控制器优化研究

研究PID控制器性能优化中,由于被控对象具有高阶、非线性等特点,而在工业生产过程中,传统的PID参数整定方法易出现超调和震荡问题,使系统响应特性差。为改善系统性能,提出一种改进粒子群算法的智能优化策略,将PID控制器参数作为粒子群中的粒子,以控制误差时间积分函数作为优化目标,进行PID控制参数的并行优化。利用MATLAB仿真软件进行仿真,并通过与传统整定方法(Z-N法)进行比较。结果表明,粒子群的PID参数整定法可提高控制器性能,并能够实现目标的最优整定,为PID控制器性能优化提供依据。     

一种高效的函数可微的全局优化模拟退火算法

针对函数可微的全局优化问题,将最速下降法,Newton法和罚函数法引入模拟退火算法中,提出了一种高效的模拟退火算法.该算法可以求得可微函数优化问题的全局最优解,且具有计算量小,效率高的特点.利用罚函数将约束优化问题转化为无约束优化问题后,可以利用提出的算法进行求解.数值算例表明,提出的算法能够高效地求解无约束及带约束的函数可微的全局优化问题.     

基于群智能求解带约束问题的时域鲁棒优化算法

针对现有的时域鲁棒优化算法无法解决带约束的优化问题,基于群智能优化方法,提出一种求解带约束优化问题的时域鲁棒优化算法.首先,用约束条件构造罚函数,将带约束优化问题处理成为无约束优化问题;然后,采用一个分段函数作为粒子的适应度评价函数,通过竞争规则筛选粒子,设计带约束问题的时域鲁棒优化算法.以优化碳纤维原丝的性能为背景,将算法在多组参数下进行测试和对比分析,结果表明了所提出算法的有效性.进一步分析AR模型对算法性能的影响,指出预测模型的改进是提升算法性能的一个重要手段.     

基于单纯形法的PID控制器参数优化设计

对于热工自动调节系统中PID控制器参数优化问题，该文提出了一种先进方法，即采用MATLAB优化工具箱来优化PID控制器参数。文中先介绍了工具箱的主要特点，然后给出了在约束条件下的优化算法。若考虑采用时间和误差的绝对值乘积的积分(即IATE准则)作为目标函数，采用单纯形法来进行PID参数寻优，则使目标函数为最小就可以达到控制系统优化的目的。文中给出了优化设计的过程。最后，仿真结果和分析表明了单纯形法在PID控制器参数优化算法中是适用的，改善了控制系统的动态性能。     

改进的无约束优化3D-DV-Hop定位算法

针对传统DV-Hop三维定位算法定位误差较大,且机器学习及仿生算法计算任务繁重的缺点,提出一种改进的无约束优化3D-DV-Hop定位算法,采用二通信半径策略计算最小跳数值,提出平方代价函数对锚节点跳距值进行优化处理,并将其加权跳距值作为未知节点跳距值,最后根据约束问题的无约束求解思想,将加权误差最小化进而求解。通过与传统算法和各类改进算法在3种条件下进行仿真对比,验证了该优化算法在较低计算量的情况下定位误差显著降低。     

混合型蛙跳算法及其应用研究*

为了提高蛙跳算法求解无约束连续优化问题的能力,提出了一种改进型混合蛙跳算法。为验证该算法求解函数优化问题的高效性,将其与基本蛙跳算法进行比较实验,结果表明该算法的解精度及收敛速度均优于基本蛙跳算法,更适用于求解复杂的无约束连续优化问题。     

地铁站台空调系统非线性预测控制策略

地铁站台空调系统回路众多且具有强耦合和非线性特性,PID控制方法参数整定困难,无法兼顾乘客舒适性和能效最优,由于系统建模困难,非线性优化算法计算量大,智能控制方法难以实现工程应用.对此,提出一种地铁站台空调系统预测控制策略.首先,根据热湿负荷平衡和能量守恒定律建立地铁站台热动态特性预测模型;然后,将满足乘客舒适性并节省能耗作为系统优化目标,使用神经网络作为优化反馈控制器,将系统优化目标函数作为控制器优化性能指标,结合变分法和随机梯度下降法,对神经网络控制器的权值和阈值进行在线滚动优化,算法计算量小,占用存储空间适中.仿真实验结果表明,所提出的预测控制策略与传统PID控制方法相比,在满足乘客舒适性要求的前提下,系统响应时间可缩短约39.6%,末端风机能耗降低约73.39%.     

一种关于目标罚参数的精确罚函数法

罚函数法是一种将约束优化问题转化为无约束问题的重要方法.对于一般的约束优化问题,通过加入新参数,给出了一种改进的精确罚函数和这种罚函数的精确罚定理证明,提出了求解这种罚函数的算法.实验表明该算法是有效的.     

一种改进的协同优化算法及其应用

针对协同优化方法收敛困难、优化效率低的问题,提出了一种改进的协同优化算法—ICO算法。通过引入自适应松弛因子将一致性等式约束转化为不等式约束,同时建立混合惩罚函数,将系统级约束优化问题转化为无约束优化问题,ICO算法较好地克服了传统协同优化算法难于收敛的缺点。标准算例实验结果表明,ICO算法能够有效提高优化的稳定性、可靠性和计算效率。优化结果显示了协同优化算法解决海洋供应船的设计优化问题的有效性,为解决更为复杂工程系统的设计优化问题奠定了基础。     

基于改进粒子群算法的模糊神经网络PID控制器设计

针对模糊神经网络PID控制器中参数初始值的设置对控制器性能影响大的问题,提出一种改进的PSO算法优化模糊神经网络PID控制器参数的设计方法.该方法采用实数编码的方式对控制器参数进行优化,并以ITAT指标作为改进的PSO优化算法的适应度函数.实验仿真表明:经过改进的PSO算法优化的模糊神经网络PID控制器具有良好的动静态性能,响应速度更快,超调量更小,控制精度更高.     

一种PID参数整定的粒子群优化算法

提出了一种PID控制器参数整定的粒子群优化算法。该方法首先通过定义一个包含系统超调量、上升时间和稳态误差指标项的适应度函数,并根据系统的实际控制要求对各指标项适当加权。之后由带收缩因子的粒子群算法对PID进行多目标寻优,从而实现PID控制器的自动参数整定。仿真结果表明,该方法优化得到PID控制器的综合性能优于常规方法得到的PID控制器。     

基于稳定参数空间的PID 调节器遗传优化设计

提出一种满足平方误差积分（ＩＳＥ）最小的闭环反馈稳定ＰＩＤ调节器遗传优设计方法,运用增广Ｈｅｒｍｉｔｅ－Ｂｉｅｎｌｅｒ定理,解析地给出了使任意给定（稳定或不稳定）被控对象闭环稳定的调节器参数取值范围。在该取值范围内动用遗传优化自救得到了满足指定性能指标（ＩＳＥ）最小撮佳调节器参数值。仿真例子验证了该方法的有效性。     

基于量子遗传算法的PID控制器参数自整定

提出了一种基于量子遗传算法（QGA）的PID控制器参数整定方法。首先定义一个包含表示系统超调量、上升时间和稳态误差指标项的适应度函数,并根据实际系统的性能要求对指标项进行适当加权。之后采用具有量子比特个体表示形式和量子旋转门实现种群进化的量子遗传算法,对PID进行多目标寻优,从而实现PID参数的自动整定。仿真结果表明,该方法优化得到PID控制器的综合性能优于常规方法和一般遗传算法得到的PID控制器。     

OPTIMIZATION OF DECENTRALIZED PI/PID CONTROLLERS BASED ON GENETIC ALGORITHM

In this paper, an optimization method of tuning decentralized PI/PID controllers based on genetic algorithms is presented. First, the existence of decentralized PI controllers with integrity is examined. Then, stable regions of each PI/PID controller parameters are calculated as the feasible area to be exploited, and the optimal PI/PID controllers are obtained by using a real‐coded genetic algorithm with elitist strategy, to meet the design specifications for the whole control system. The proposed method is applied to six examples from literature. Simulation results demonstrate that the proposed decentralized PI control is compatible to the referenced method while the decentralized PID control is better than the referenced method, and the proposed method is feasible for more complicated control systems optimizations.     

A hybrid genetic algorithm and bacterial foraging approach for global optimization

The social foraging behavior of Escherichia coli bacteria has been used to solve optimization problems. This paper proposes a hybrid approach involving genetic algorithms (GA) and bacterial foraging (BF) algorithms for function optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the lifetime of the bacteria. The proposed algorithm is then used to tune a PID controller of an automatic voltage regulator (AVR). Simulation results clearly illustrate that the proposed approach is very efficient and could easily be extended for other global optimization problems.     

Comparable investigation of backtracking search algorithm in automatic generation control for two area reheat interconnected thermal power system

This paper concentrates on the validation of metaheuristic algorithms like backtracking search optimization algorithm (BSA) and fruit fly optimization algorithm (FFA) for tuning a optimal PID controller for automatic generation control. For this purpose, a two area reheat interconnected thermal system with nonlinearities like generator rate constant (GRC), deadband and time delay are considered. The proposed work is implemented using MATLAB Simulink for various load conditions with objective functions for metaheuristic algorithms capturing signals from various positions of proposed model. The results obtained using two algorithms are compared and explored.     

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.      

Output feedback distributed optimization algorithms of second-order Lipschitz nonlinear multi-agent systems

This paper introduces output feedback distributed optimization algorithms designed specifically for second-order nonlinear multi-agent systems. The agents are allowed to have heterogeneous dynamics, characterized by distinct nonlinearities, as long as they satisfy the Lipschitz continuity condition. For the case with unknown states, nonlinear state observers are designed first for each agent to reconstruct agents' unknown states. It is proven that the agents' unknown states are estimated accurately by the developed state observers. Then, based on the agents' state estimates and the gradient of each agent local cost function, a kind of output feedback distributed optimization algorithms are proposed for the considered multi-agent systems. Under the proposed distributed optimization algorithms, all the agents' outputs asymptotically approach the minimizer of the global cost function which is the sum of all the local cost functions. By using Lyapunov stability theory, convex analysis, and input-to-state stability theory, the asymptotical convergence of the output feedback distributed optimization closed-loop system is proven. Simulations are conducted to validate the efficacy of the proposed algorithms.     

Echo state networks with Tikhonov regularization: optimization using integral gain

This study proposes feed-forward echo state networks (ESN) as an estimator, and couples it with second-order proportional-integral-derivative (PID) feedback extension to compensate for dead time in feedback systems. The system is tested for two-dimensional space motion patterns recognition and prediction using simulations, which allows control of noise input. Tikhonov regularization is employed for training readouts and second-order PID feedback minimizes prediction bias. Evaluation is done using mean squared error and the coupled system performs well compared to any of its standalone versions. The results suggest it is feasible to (1) ‘compress’ the memory capacity of the system, and (2) reduce the number optimization parameters, while maintaining the estimation performance and following the excitation property of the estimator. It is feasible to optimize the ESN using feedback gain although it plays a significant role in the proposed system because the improvement by bias correction is far greater than that of optimization; thus, simplifying the estimation to a feedback problem which is easily tuned using the Ziegler–Nichols method.     

制冷站双目标权重自适应非线性预测控制

针对传统制冷站控制系统易产生振荡, 且无法实现系统性能整体优化的问题, 本文提出一种制冷站非线性 预测控制策略, 优化目标函数设计为满足建筑冷量需求的同时, 尽可能提高系统整体能效. 为解决上述两个优化目 标之间的矛盾关系, 本文采用模糊逻辑设计了优化目标权重自适应模块, 实时求取权重因子最优解; 针对非线性系 统在线优化求解困难问题, 本文提出了基于神经网络的非线性滚动优化算法, 采用神经网络作为反馈优化控制器, 并将系统优化目标函数作为在线寻优性能指标, 结合Euler-Lagrange方法和随机梯度下降法对控制器权值和阈值进 行在线寻优, 算法计算量小, 占用存储空间适中, 便于采用低成本的现场控制器实现制冷站预测控制. 仿真实验结果 表明, 本文所提出的预测控制策略与PID控制相比, 在未加入优化目标函数权重自适应模块情况下, 系统平均能效 比提高约32.5%; 进行优化目标函数权重自适应寻优后, 系统平均能效提高约39.43%.