基于粒子群优化的分数阶PID预测函数参数整定

为避免分数阶PID预测函数手动调节参数的不确定性以及繁琐性,应用粒子群算法优化其参数,完善分数阶PID预测函数控制器,并提高其控制精度,充分体现了算法的智能性。将粒子群整定分数阶PID预测函数算法应用于单变量的励磁控制系统中,通过Matlab仿真并与模糊分数阶PID预测函数以及经验调节方法相比较,结果表明,粒子群算法整定参数收敛速度快,找到最优点时间短,整定后的算法具有静态误差小、无超调、上升速度快、调节时间短、抗干扰能力强等优点,能很好地满足励磁系统的动态特性,并具有较强的鲁棒性及适应性。     

基于RandW-SecPSO的四旋翼飞行器分数阶PID控制器参数优化

为了提高四旋翼飞行器姿态控制的控制性能,将分数阶PID控制器运用到四旋翼飞行器的控制系统中.提出了一种带随机权重平均值的二阶粒子群算法(RandW-SecPSO)去优化分数阶PID控制器的参数.将随机权重平均值与二阶粒子群算法相结合,对粒子群进行二阶初始化,同时加入随机权重用以平衡全局搜索能力和局部开发能力,这样提高了算法的收敛精度,并将其与PID控制器进行仿真分析.通过搭建仿真平台,验证了该算法的可行性.仿真结果表明:RandW-SecPSO算法在优化四旋翼飞行器分数阶控制器的参数上要好于粒子群算法(PSO),与PSO算法相比调节时间缩短了0.7s,上升时间减少了0.2s,超调量减小了8％,具有收敛速度快、超调量小、稳定性好等优点.总之RandW-SecPSO算法优化分数阶PID动态响应特性比PID要好很多.     

混合粒子群算法优化分数阶PID控制参数研究

分数阶比例-积分-微分(PID)控制器是一种把PID控制器的整数阶次推广到分数的比例、积分、微分控制器,它比传统的PID控制器更能精确地控制复杂的被控系统.而参数的取值对控制效果的好坏起着决定性作用,为此提出了一种混合粒子群算法BFA-PSO优化参数值.该算法将具有趋化、繁殖和驱散特点的细菌觅食算法和参数少,易于优化的粒子群算法相结合来计算出精确的分数阶PID控制器的参数值.通过对传统PID控制器和分数阶PID控制器参数优化的实验仿真,结果表明基于该算法的分数阶PID控制不仅无超调量、收敛速度快,而且鲁棒性强、收敛精度高,可用于控制不同的对象和过程.     

灰狼算法优化分数阶模糊控制器参数

PID模糊控制在工业控制中是最广泛的一种控制方法,在一些复杂的实际系统中,应用分数阶PID模糊控制器在整定系统参数性能上优于整数模糊控制器.分数阶模糊控制器具有较多的控制参数,这些控制参数直接影响了模糊控制器的性能.用传统的算法校准分数阶模糊控制器并不能得到最佳的参数值,而且标定参数的过程较为复杂.因此提出用灰狼优化算法(Grey Wolf Optimizer,GWO)优化分数阶模糊控制器的参数.将基于灰狼优化算法的分数阶模糊控制器优化方法与其他五种典型的基于群智能的优化方法进行了比较.实验结果表明,该方法的控制效果更好.     

航空发动机分数阶PID控制器的参数自整定方法

控制器作为航空发动机的大脑,是保障发动机正常运行的核心部件,随着对发动机控制器精度和时效性的要求越来越高,传统PID控制器的性能亟需进一步提升.本文提出了改进的分数阶PID离线和在线参数整定方法,应用于涡扇发动机推力的控制中.首先,利用Caputo分数阶微积分定义建立分数阶PID模型,实现时域上的数值计算;其次,基于对数正态分布提出了改进的布谷鸟算法,实现了分数阶PID离线参数整定;然后,结合RBF网络设计参数线上整定方法,解决了参数在线整定问题;最后将相关理论应用于发动机推力的控制中,结果表明,相比其他几种优化算法,改进的布谷鸟优化算法对分数阶PID控制参数整定效果最好;利用RBF神经网络对分数阶PID进行在线整定时控制效果稳定,且分数阶PID的控制效果优于传统的PID控制,能提高对推力的控制能力.     

基于Lbest PSO和NNs的电液伺服系统输出力PID控制研究

为了实现电液伺服系统输出力的稳定控制,结合局部最优粒子群优化算法和神经网络模型,提出一种PID控制器设计方法。该方法将神经网络模型(NNS)与PID控制器耦合,得到基于神经网络的PID控制器参数整定结构;再采用局部最优粒子群优化算法(Lbest PSO)确定神经网络的权重,从而得到基于局部最优粒子群优化算法和神经网络的PID控制算法;最后将提出的PID控制算法用于控制虚拟的电液伺服加载系统,以进行仿真实验。仿真结果表明,由该PID控制器控制的电液伺服系统的输出力平稳地收敛于给定力,从而提高了系统的稳定性。     

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。     

基于预测双反馈的3D打印机温度优化控制

针对FDM型3D打印机喷头温度控制系统的非线性、大滞后等特点,传统PID控制难以满足实际的性能要求.为提高控制系统性能,提出预测模型双反馈变论域分形模糊分数阶PID控制器.分数阶PID控制为系统提供更多的控制余度;变论域模糊控制的论域能够随着误差的变化而变化,实现不同时刻选择不同论域的模糊规则;短反馈算法的引入能够构成双层反馈控制,实现对喷头温度的快速、精准控制;系统反馈环节建立预测模型,实现更加精确的控制.针对变论域模糊分数阶PID控制器中参数选择,提出一种改进的鲸鱼算法进行参数优化,增强控制器的自适应能力.仿真结果表明,改进鲸鱼优化算法的预测模型双反馈变论域模糊分数阶PID控制的喷头温度控制系统较传统的PID控制具有更好的控制效果,系统响应速度快、抗干扰能力强、稳定性好,能够满足实际工业需求.     

基于人群搜索算法的PID控制器参数优化

关于PID控制器在工业控制领域应用优化问题,PID参数优化成为工业自动化研究的热点.PID参数优化对于系统的稳定性、可靠性和快速响应等特性有着重要的意义.为了改善和优化PID控制器性能,提出一种人群搜索算法(SOA),以PID三个参量为搜寻队伍,以误差绝对值和控制输入平方项的时间积分作为优化目标,经过迭代寻优计算得到系统最优控制量.通过对比遗传算法和粒子群算法PID参数优化,仿真结果表明,改进算法提高了系统的控制精度,系统响应速度快,鲁棒性好,为控制系统PID参数整定提供了参考.     

基于最优Oustaloup的分数阶PID参数整定

目前工程控制中大部分系统采用传统PID控制,由于分数阶PID继承了传统PID的优点,并且具有更好的控制品质及更强的鲁棒性,因此针对分数阶微积分的高精度数字实现及分数阶PID控制器在工程复杂系统中的实际应用,提出一种新的分数阶微积分高精度数字实现算法-最优Oustaloup数字实现,并建立控制系统的仿真模型,利用框图式模型结合最优ITAE性能指标来整定分数阶PID的参数。通过实例仿真验证,该方法能进一步优化控制器参数,提高控制精度及获得更好的控制效果,便于非线性系统及复杂系统的分数阶PID参数整定。     

Robust adaptive control of a bio-inspired robot manipulator using bat algorithm

This paper proposes a novel adaptive fractional order PID sliding mode controller (AFOPIDSMC) using a Bat algorithm to control of a Caterpillar robot manipulator. A fractional order PID (FOPID) control is applied to improve both trajectory tracking and robustness. Sliding mode controller (SMC) is one of the control methods which provides high robustness and low tracking error. Using hybridization, a new combined control law is proposed for chattering reduction by means of FOPID controller and high trajectory tracking through using SMC. Then, an adaptive controller design motivated from the SMC is applied for updating FOPID parameters. A metaheuristic approach, the Bat search algorithm based on the echolocation behavior of bats is applied for optimal design of the Caterpillar robot in order to tune the parameter AFOPIDSMC controllers (BA-AFOPIDSMC). To study the effectiveness of Bat algorithm, its performance is compared with five other controllers such as PID, FOPID, SMC, AFOPIDSMC and PSO-AFOPIDSMC. The stability of the AFOPIDSMC controller is proved by Lyapunov theory. Numerical simulation results completely indicate the advantage of BA-AFOPIDSMC for trajectory tracking and chattering reduction.     

Optimal design of fractional-order PID controller for five bar linkage robot using a new particle swarm optimization algorithm

This paper introduces a new version of the particle swarm optimization (PSO) method. Two basic modifications for the conventional PSO algorithm are proposed to improve the performance of the algorithm. The first modification inserts adaptive accelerator parameters into the original velocity update formula of the PSO which speeds up the convergence rate of the algorithm. The ability of the algorithm in escaping from local optima is improved using the second modification. In this case, some particles of the swarm, which are named the superseding particles, are selected to be mutated with some probability. The proposed modified PSO (MPSO) is simple to be implemented, fast and reliable. To validate the efficiency and applicability of the MPSO, it is applied for designing optimal fractional-order PID (FOPID) controllers for some benchmark transfer functions. Then, the introduced MPSO is applied for tuning the parameters of FOPID controllers for a five bar linkage robot. Sensitivity analysis over the fractional order of the PID controller is also provided. Numerical simulations reveal that the MPSO can optimally tune the parameters of FOPID controllers.     

基于改进BBO算法的分数阶PID控制器设计

针对分数阶PID(Fractional-Order Proportional-Integral-Derivative,FOPID)控制器参数整定,提出了一种改进生物地理学优化(Biogeography-Based Optimization,BBO)算法。该算法改进点主要包括:迁移操作中保留精英个体;变异操作中引入差分进化(Dtferential Evolution,ED)算法的变异策略;消除重复样本。仿真结果表明:在分数阶PID控制器参数整定中,与原始的BBO算法、遗传算法(Genetic Algorithm,GA)和粒子群算法(Particle Swarm Optimization,PSO)比较,提出的改进BBO算法具有超调量小、误差小,收敛更快的特点。     

Robust Finite-time Synchronization of Non-identical Fractional-order Hyperchaotic Systems and Its Application in Secure Communication

This paper proposes a novel adaptive sliding mode control (SMC) method for synchronization of non-identical fractional-order (FO) chaotic and hyper-chaotic systems. Under the existence of system uncertainties and external disturbances, finite-time synchronization between two FO chaotic and hyperchaotic systems is achieved by introducing a novel adaptive sliding mode controller (ASMC). Here in this paper, a fractional sliding surface is proposed. A stability criterion for FO nonlinear dynamic systems is introduced. Sufficient conditions to guarantee stable synchronization are given in the sense of the Lyapunov stability theorem. To tackle the uncertainties and external disturbances, appropriate adaptation laws are introduced. Particle swarm optimization (PSO) is used for estimating the controller parameters. Finally, finite-time synchronization of the FO chaotic and hyper-chaotic systems is applied to secure communication.      

基于改进型PI趋近律的无刷直流电机滑模控制

针对无刷直流电机模型参数时变的特性,为解决传统PID控制易受模型参数变化影响的弊端与传统滑模控制(SMC)的抖振问题,将分数阶PI与变速趋近律结合,提出了一种改进型PI趋近律的无刷直流电机滑模控制算法。该算法通过对趋近律的改进,既避免了模型参数变化的影响,又减小了系统输出的抖振。仿真与实验结果表明,与传统滑模控制、传统PI控制相比,具有响应快、鲁棒性强、超调小、抖振小等优点,是一种更优的控制方法。     

基于改进粒子群算法的PID参数优化与仿真

提出了一种基于改进的粒子群优化（PSO）算法的PID控制器参数整定方法。该方法采用了PSO的惯性权值自适应调整机制和粒子种群的动态更新策略,用以加速优化算法的收敛和维持群体的多样性。与常规的PSO算法相比,该方法简单易行,更容易找到全局最优解,优化效率和性能明显提高。将该算法应用非最小相位、一阶滞后等系统的PID控制器参数的优化,能够使控制系统获得较好的动态特性和很强的鲁棒性。仿真实验表明了所提出算法的有效性和优越性。     

基于PSO的钻机快速自适应PID控制

针对传统液压盘刹钻机PID控制系统响应速度慢、稳态误差大、参数整定周期长,以及无法满足随钻遇地层变化实时参数优选的不足,以恒钻压自动送钻为研究对象,构建了液压盘刹钻机控制模型,设计了粒子群算法(PSO)优选钻机PID控制参数,并实现在Simulink环境下自动调用优选出的PID参数,提高了钻机控制参数的快速、自适应整定。仿真结果表明,与传统采用Z-N经验公式法、试凑法和遗传算法(GA)实现钻机PID控制相比,基于PSO的钻机快速自适应PID优化控制能够有效地提升系统响应速度,降低系统稳态误差,满足钻机恒钻压随钻控制的实时性、准确性需求,实验结果证明了该方法的可行性和优越性。     

Longitudinal control of an intelligent vehicle using particle swarm optimization based sliding mode control

This paper focuses on the design of longitudinal controller for an intelligent vehicle which was built at Asian Institute of Technology based on sliding mode control. The proposed controller uses particle swarm optimization (PSO) for optimal tuning of sliding surface and controller gain in the sliding mode controller (SMC). The longitudinal control is conducted via controlling of throttle value angle using PSO-based SMC on the simplified first-order linear model of the intelligent vehicle and controlling of brake force using fuzzy logic. In order to achieve the desired headway time, integration of throttle valve angle control and brake force control is required. To obtain the optimal parameters of SMC, two equations velocity updating and position updating are applied. Firstly, the performance of proposed controller is evaluated by using MATLAB simulation to compare with conventional PD controller. Finally, the experimental results show that the proposed PSO-based SMC can perform efficiently in longitudinal control of the intelligent vehicle.     

Design of a chatter-free terminal sliding mode controller for nonlinear fractional-order dynamical systems

This paper investigates the problem of robust control of nonlinear fractional-order dynamical systems in the presence of uncertainties. First, a novel switching surface is proposed and its finite-time stability to the origin is proved. Subsequently, using the sliding mode theory, a robust fractional control law is proposed to ensure the existence of the sliding motion in finite time. We use a fractional Lyapunov stability theory to prove the stability of the system in a given finite time. In order to avoid the chattering, which is inherent in conventional sliding mode controllers, we transfer the sign function of the control input into the fractional derivative of the control signal. The proposed chattering-free sliding mode technique is then applied for stabilisation of a broad class of three-dimensional fractional-order chaotic systems via a single variable driving control input. Simulation results reveal that the proposed fractional sliding mode controller works well for chaos control of fractional-order hyperchaotic Chen, chaotic Lorenz and chaotic Arneodo systems with no-chatter control inputs.