基于量子粒子群优化的主动队列管理新算法

本文推导了基于流体流理论的网络简化模型, 基于该模型将量子空间中的粒子群优化算法(QDPSO)应用于PID控制器参数优化, 定义了一个综合调节时间、上升时间、超调量、系统静态误差、正弦跟踪误差等动静态性能指标函数, 在给定的参数空间进行组合优化搜索, 迅速求得获取使性能指标优化函数极小化的一组PID控制器参数,将PID控制器应用于网络主动队列管理系统中. 仿真结果表明, 在大时滞和突发业务流的冲击两种情况下,该方法设计的控制器的动静态性能优于PI算法, 也优于GA、SPSO算法的优化结果, 超调量均小于4%, 调节时间均小于4s, 稳态误差均小于两个数据包.     

基于蚁群算法分数阶PID控制器在温度控制系统的应用

针对温度控制系统的控制,提出了一种基于蚁群算法的分数阶PID控制方法。该控制器以系统误差、控制器输出、上升时间和超调量构成的函数为性能指标,利用蚁群算法全局搜索能力获取一组最优的PID参数Kp、Ki、Kd,并且能根据实时获取的误差在线调整PID参数。通过对比分析传统PID和分数阶PID,结果表明该温度控制器具有良好的动、静态特性,自适应性和鲁棒性好。     

基于Fuzzy-PID的PZT微纳扫描控制算法

针对压电陶瓷的特性,提出了基于Fuzzy-PID的复合控制算法.该算法在整个过程中的不同阶段采用不同的控制方式,即根据误差信号e的大小有选择性的使用模糊控制或PID控制,因此不需要建立被控对象的精确数学模型,而且还继承了常规PID控制静差小、静态稳定性好的特点,同时又兼有模糊控制适应能力强、动态性能好的优势. 实验中采用此算法获得闭环控制电压最大误差为18 mV,置位重复精度为6 nm,系统相对误差0.15%;在位移量为10 μm的时间-位移阶跃响应曲线中阶越响应时间约为20 ms,且在电压为80 V的蠕动曲线中,3 min内的蠕变为3.6%,3 min以后变化开始缓慢.由此可知该控制方法能够有效地降低压电陶瓷非线性所引起的位移误差,提高定位精度,而且还能改善压电陶瓷的蠕动特性.     

基于单片机和模糊控制的水温自动控制系统

介绍了一种以单片机89C52为控制核心,以开关控制和PID算法控制以及PID参数模糊整定相结合的复合控制方法实现了水温的自动控制。着重介绍了系统的硬件设计及软件设计。该系统的特点是电路结构简单、程序简短、系统可靠性高。理论分析和实验结果表明:采用模糊控制与PID控制相结合的控制算法,有效地减少了超调量和静态误差,缩短了调节时间。     

模糊PID控制在除氧系统中的应用

采用模糊控制与PID控制方式相结合的控制策略,设计了一种高精度温度控制算法,并以PIC18F258单片机为核心,实现了该控制方案。使用Matlab软件对PID控制、模糊PID控制分别进行了仿真研究,仿真结果表明参数模糊PID控制能满足调节时间短、超调量小且稳态误差在104±3℃内的控制要求。     

快速刀具伺服分数阶PID控制仿真的研究

利用分数阶PID控制,提出了一种新的快速刀具伺服(FTS)跟踪控制方法,以改善FTS的控制性能。根据差分进化算法,讨论了分数阶PID控制器的参数整定;通过数值仿真,考察了该方法的可行性和有效性。针对FTS的轨迹跟踪,根据响应时间、跟踪精度等指标,详细比较了分数阶PID控制与传统PID控制的性能。仿真结果表明,分数阶PID控制器的阶跃响应时间约为5×10-7s,是PID控制响应时间的42%,对频率为1 kHz,幅值为1μm的正弦信号的跟踪误差约为6 nm,是PID跟踪误差的50%,验证了基于分数阶PID控制器实现FTS轨迹跟踪控制的可行性和优越性。     

模糊分数阶滑模控制在PMSM伺服系统中研究与应用

针对整数阶滑模控制器容易引起永磁同步电机抖振的问题,利用分数阶系统具有能量传递缓慢及逐渐收敛的原理,设计一种分数阶滑模控制器来减弱永磁同步电机抖振及提高电机性能指标.再利用Lyapunov稳定性理论和分数阶微积分理论证明这种分数阶滑模控制器的稳定性.利用模糊算法实现了滑模切换项和分数阶阶次的在线自适应调节.仿真及试验结论证明,所提方法可以有效减弱永磁同步电机系统的抖振,缩短调节时间,并具有较强的鲁棒性.     

基于预测函数控制的电力系统励磁控制

励磁控制系统是同步发电机中重要的组成部分,其性能好坏直接影响电力系统运行的可靠性和稳定性。预测函数控制(PFC)是在预测控制原理的基础上发展起来的一种控制方法,相对而言,PFC拥有算法简便、在线计算量小等特点,且跟踪速度快。将预测函数控制算法用于同步发电机励磁系统中,通过MATLAB仿真,并与PID控制器的仿真进行比较,可以看出预测函数控制有更好的控制效果。,     

改进ABC算法的双容水箱液位控制研究

从提高双容水箱液位控制系统的稳定性和控制精度出发,设计了一种基于改进人工蜂群算法的PID控制调整策略。该策略针对人工蜂群算法全局探测能力不足而易陷入局部最优解的问题,提出将遗传算法的交叉操作引入到人工蜂群算法中,使其可以得到更加精确的PID控制参数。通过仿真分析并与传统的PID控制相比较,结果表明,经过改进的人工蜂群算法优化的PID控制具有静态误差小、调整时间短、超调量小等优点,能很好的满足控制过程的动态性能,具有重要的实用价值。     

催青室温度控制器的设计与仿真

针对传统的催青室温度PID控制系统存在稳定性差,控制精度效果不佳,无法在线调整PID参数等问题,提出了一种采用达林算法方式来设计催青室温度控制器方案,并介绍了此系统的控制器算法设计步骤,实验结果仿真,并在进行实地实验.仿真、实践结果表结果表明该控制方法超调量小,调节时间大幅缩短,并可以有效提高系统响应速度及控制精度.     

System identification with measurement noise compensation based on polynomial modulating function for fractional-order systems with a known time-delay

This study presents a system identification method based on polynomial modulating function for fractional-order systems with a known time-delay involving input and output noises in the time domain. Based on the polynomial modulating function and fractional-order integration by parts, the identified fractional-order differential equation is transformed into an algebraic equation. By using the numerical integral formula, the least squares form for the system identification is obtained. In order to reduce the effect of noises existing in the input and output measurements, the compensation method for the input and output noises is also studied by introducing an auxiliary high-order fractional-order system in the revised identification algorithm. Finally, the effectiveness of the proposed algorithm is verified by the simulation result of an illustrative example and the experimental result of temperature identification for a thermal system.     

Improved model reduction and tuning of fractional-order PI(λ)D(μ) controllers for analytical rule extraction with genetic programming

Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) PI(λ)D(μ) controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H(2)-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and PI(λ)D(μ) controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples.     

基于PID算法的控制量按任意函数变化的一种控制方法

在PID(proportional-integral-differential)控制算法的基础上,通过对控制量按某一函数变化的控制系统进行分析,提出了一种简单的控制量按任意函数变化的控制方法,即小区段设定值恒定PID控制算法,给出了该算法的数学模型,简要介绍了此PID控制算法的各个参数的整定方法,并分析了此算法在控制量超前控制、滞后控制、理想控制情况下的参数选取原则;用MATLAB对此算法进行了仿真和验证,仿真结果比较理想;同时还在不同的设备、不同的试验条件下进行了试验并获得成功,此外还将这种方法应用到实际工程当中,经过试验和实际工程检测获得了良好的效果.     

Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms

The most studied controller for pitch control of wind turbines is proportional-integral-derivative (PID) controller. However, due to uncertainties in wind turbine modeling and wind speed profiles, the need for more effective controllers is inevitable. On the other hand, the parameters of PID controller usually are unknown and should be selected by the designer which is neither a straightforward task nor optimal. To cope with these drawbacks, in this paper, two advanced controllers called fuzzy PID (FPID) and fractional-order fuzzy PID (FOFPID) are proposed to improve the pitch control performance. Meanwhile, to find the parameters of the controllers the chaotic evolutionary optimization methods are used. Using evolutionary optimization methods not only gives us the unknown parameters of the controllers but also guarantees the optimality based on the chosen objective function. To improve the performance of the evolutionary algorithms chaotic maps are used. All the optimization procedures are applied to the 2-mass model of 5-MW wind turbine model. The proposed optimal controllers are validated using simulator FAST developed by NREL. Simulation results demonstrate that the FOFPID controller can reach to better performance and robustness while guaranteeing fewer fatigue damages in different wind speeds in comparison to FPID, fractional-order PID (FOPID) and gain-scheduling PID (GSPID) controllers.     

灰色预测模糊PID控制在调节阀智能定位系统中的应用

针对气动调节阀系统大惯性、纯滞后和时变性的特点,建立了调节阀执行机构的二阶加纯滞后模型,在此基础上设计了基于灰色预测模糊PID算法的复合控制器。PID模块作为该复合算法的基础,用来减小系统偏差,提升动态性能;模糊控制模块对系统偏差及其变化率进行模糊处理,输出值作为PID参数的调整量,实现在线调整控制参数,克服时变性对系统的影响;灰色预测模块对系统输出数据序列进行提取并预测未来发展趋势以进行系统的“超前控制”,进一步解决气动系统滞后和稳态性能差的问题。基于灰色预测模糊PID算法的复合控制器将灰色预测模型、模糊控制和比例微积分算法相结合,可实现阀门位置的高精度调节与智能控制。仿真结果表明,灰色预测模糊PID控制算法相比常规PID以及模糊PID具有更快的响应速度、更高的调节精度以及更好的稳态性能。     

直驱泵控电液位置伺服系统模糊PID控制仿真与实验研究

直驱泵控系统由于控制精度高、响应快、功率损失小、结构紧凑等优点,逐渐被应用到工业控制领域。但直驱泵控系统存在严重饱和与死区、非线性、时变性、时滞性的特点。我们采用模糊PID控制方法进行直驱泵控系统的液压缸位置伺服控制。在AMESim/Simulink联合仿真平台上建立了系统模型与模糊PID控制算法,采用PID和模糊PID控制算法对系统阶跃响应和正弦跟踪性能进行了仿真研究,同时在自行开发的电液比例伺服控制实验台上对PID和模糊PID控制性能进行了实验研究。结果表明：自适应模糊PID控制能大大改善了PID控制的性能,具有响应速度快、上升时间短、无滞与超调小的特点。     

基于负载观测器的PMSM分数阶滑模位置控制

针对永磁同步电机位置控制系统,设计分数阶滑模变结构控制器,以代替传统PID三闭环串级控制结构的转速环和位置环控制器。针对电机运行时负载转矩会受到外界干扰的问题,设计负载转矩观测器。仿真结果表明分数阶滑模控制器有良好的动态和稳态性能以及良好的鲁棒性。     

Variable-order fuzzy fractional PID controller

In this paper, a new tuning method of variable-order fractional fuzzy PID controller (VOFFLC) is proposed for a class of fractional-order and integer-order control plants. Fuzzy logic control (FLC) could easily deal with parameter variations of control system, but the fractional-order parameters are unable to change through this way and it has confined the effectiveness of FLC. Therefore, an attempt is made in this paper to allow all the five parameters of fractional-order PID controller vary along with the transformation of system structure as the outputs of FLC, and the influence of fractional orders λ and μ on control systems has been investigated to make the fuzzy rules for VOFFLC. Four simulation results of different plants are shown to verify the availability of the proposed control strategy.     

基于改进型灰色预测大时滞过程的自适应PID控制研究

针对传统PID控制对大时滞控制效果不佳的情况,提出一种基于改进灰色预测模型的自适应PID控制系统。采用等维新信息、改变初始条件和精指数拟合法对G（1,1）模型进行改进,将改进后的G（1,1）模型与传统PID结合,组成灰色预测控制系统。用预测结果代替被控对象测量值,克服了大时滞系统控制效果不能及时反馈的不足,并在控制运算中,沿二次型性能指标的负梯度方向对加权系数进行在线修改,实现了自适应PID的优化控制。仿真结果表明了该预测控制算法的响应速度快、鲁棒性强,有较好的适应性。     

An approach to design controllers for MIMO fractional-order plants based on parameter optimization algorithm

The parameter optimization method for multivariable systems is extended to the controller design problems for multiple input multiple output (MIMO) square fractional-order plants. The algorithm can be applied to search for the optimal parameters of integer-order controllers for fractional-order plants with or without time delays. Two examples are given to present the controller design procedures for MIMO fractional-order systems. Simulation studies show that the integer-order controllers designed are robust to plant gain variations.