模糊RBF神经网络在锅炉水位控制中的应用

水位控制是工业锅炉控制系统中一个重要的环节,其控制质量的优劣直接影响到锅炉的安全和经济效益。本文将一种基于模糊RBF神经网络的PID控制器应用与工业锅炉水位的控制当中,它结合了传统PID以及神经网络和模糊控制的优点,可以在线调整得到一组最优的PID控制参数。仿真结果表明这种控制器具有较好的适应性,控制效果得到明显改善。     

Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System

In this paper, speed control of Brushless DC motor using Bat algorithm optimized online Adaptive Neuro-Fuzzy Inference System is presented. Learning parameters of the online ANFIS controller, i.e., Learning Rate (η), Forgetting Factor (λ) and Steepest Descent Momentum Constant (α) are optimized for different operating conditions of Brushless DC motor using Genetic Algorithm, Particle Swarm Optimization, and Bat algorithm. In addition, tuning of the gains of the Proportional Integral Derivative (PID), Fuzzy PID, and Adaptive Fuzzy Logic Controller is optimized using Genetic Algorithm, Particle Swarm Optimization and Bat Algorithm. Time domain specification of the speed response such as rise time, peak overshoot, undershoot, recovery time, settling time and steady state error is obtained and compared for the considered controllers. Also, performance indices such as Root Mean Squared Error, Integral of Absolute Error, Integral of Time Multiplied Absolute Error and Integral of Squared Error are evaluated and compared for the above controllers. In order to validate the effectiveness of the proposed controller, simulation is performed under constant load condition, varying load condition and varying set speed conditions of the Brushless DC motor. The real time experimental verification of the proposed controller is verified using an advanced DSP processor. The simulation and experimental results confirm that bat algorithm optimized online ANFIS controller outperforms the other controllers under all considered operating conditions.     

Online tuning fuzzy PID controller using robust extended Kalman filter

Fuzzy PID controllers have been developed and applied to many fields for over a period of 30 years. However, there is no systematic method to design membership functions (MFs) for inputs and outputs of a fuzzy system. Then optimizing the MFs is considered as a system identification problem for a nonlinear dynamic system which makes control challenges. This paper presents a novel online method using a robust extended Kalman filter to optimize a Mamdani fuzzy PID controller. The robust extended Kalman filter (REKF) is used to adjust the controller parameters automatically during the operation process of any system applying the controller to minimize the control error. The fuzzy PID controller is tuned about the shape of MFs and rules to adapt with the working conditions and the control performance is improved significantly. The proposed method in this research is verified by its application to the force control problem of an electro-hydraulic actuator. Simulations and experimental results show that proposed method is effective for the online optimization of the fuzzy PID controller.     

A hybrid intelligent system for PID controller using in a steel rolling process

With the aim to improve the steel rolling process performance, this research presents a novel hybrid system for selecting the best parameters for tuning in open loop a PID controller. The novel hybrid system combines rule based system and Artificial Neural Networks. With the rule based system, it is modeled the existing knowledge of the PID controller tuning in open loop and, with Artificial Neural Network, it is completed the rule based model that allow to choose the optimal parameters for the controller. This hybrid model is tested with a long dataset to obtain the best fitness. Finally, the novel research is validated on a real steeling roll process applying the hybrid model to tune a PID controller which set the input speed in each of the gearboxes of the process.     

基于Matlab-Simulink-SISO及SRO的电磁轴承PID控制参数整定

介绍了电磁轴承PID控制系统, 采用Matlab SISO SRO开发电磁轴承控制仿真系统,研究PID控制参数对电磁轴承控制的影响,给出在不同的PID控制参数下的仿真结果,为电磁轴承控制系统的设计提供了一种简洁高效的方法。     

基于CMAC-PID控制器的水压加载系统研究

为了实现水压加载系统能够动态精确跟踪给定压力的要求,利用小脑模型关节控制器(CMAC)结构简单、收敛速度快、具有局部学习能力的特点,提出了一种除了系统动态误差以外把系统指令信号也作为CMAC的输入信号,并把CMAC控制器与常规PID控制器并联构成的复合控制方法;通过在MATLAB中的编程仿真试验,结果表明这种方法可以得到比常规PID控制更好的控制指标,达到了试验要求,而且具有良好的抗干扰能力,从而证明了该方法的可行性和有效性,可以用来实现对给定信号的跟踪。     

大时滞网络自适应主动队列管理新算法

针对PID控制器无法严格处理主动队列管理（AQM）中的大时滞情况,且不能随着变化的网络环境在线调节参数,提出了一种基于增益自适应Smith预估控制和模糊控制的大时滞网络的自适应PID主动队列管理（GAS-FPID）算法。引入增益自适应Smith预估控制器实现滞后补偿,模糊控制器来实现PID参数动态网络环境的在线调整;NS2仿真表明,所提出算法能克服滞后的影响,能快速的适应动态网络环境,具有很好的稳定性和鲁棒性。     

Robust Model Reference Adaptive Intelligent Control

In this paper a Neural Network based Model Reference Adaptive Control scheme (NNMRAC) is proposed. In this scheme, the controller is designed by using parallel combination of the conventional Model Reference Adaptive Control (MRAC) scheme and Neural Network (NN) controller. In the conventional MRAC scheme, the controller is designed to realize plant output converging to reference model output based on the plant which is linear. This scheme is used to control linear plant effectively with unknown parameters. However, it is difficult for a nonlinear system to control the plant output in real time applications. In order to overcome the above limitations, the NN-MRAC scheme is proposed to improve the system performances. The control input of the plant is given by the sum of the MRAC output and NN controller output. The NN controller is used to compensate the nonlinearities and disturbances of the plant that are not taken into consideration in the conventional MRAC. The simulation results clearly show that the proposed NN-MRAC scheme have better steady state and transient performances than those of the current adaptive control schemes. Thus, the proposed NN-MRAC scheme named as Robust Model Reference Adaptive Intelligent Control (RMRAIC) is found to be extremely effective, efficient and useful in the field of control system.     

量子神经网络在PID参数调整中的应用

提出一种基于量子神经网络(QNNs)的比例积分微分(PID)参数在线调整方法.通过构造受控量子旋转门,给出一个量子神经元模型,其中包括输入量子比特相位的旋转角度和控制量2种设计参数.在此基础上提出一个量子神经网络模型,利用梯度下降法设计该模型的学习算法,并将其用于PID参数的在线调整,实验结果表明,QNNs的调整能力及...     

基于模糊RBF神经网络的PID及其应用

针对传统的PID控制器参数固定而导致在控制中效果差的问题,提出一种基于模糊RBF神经网络智能PID控制器的设计方法。该方法结合了模糊控制的推理能力强与神经网络学习能力强的特点,将模糊控制与RBF神经网络相结合以在线调整PID控制器参数,整定出一组适合于控制对象的kp, ki, kd参数。将算法运用到电机控制系统的PID参数寻优中,仿真结果表明基于此算法设计的PID控制器改善了电机控制系统的动态性能和稳定性。     

改进PSO电液伺服系统的在线PID参数整定

针对传统的电液伺服系统PID控制器参数在线整定难以达到最优的问题,提出了一种解决方法。根据系统的动态模型,在系统时变参数的变化范围内取若干值,得到一组相应数目的定参数系统模型。针对这组模型,采用改进PSO整定PID参数,获得一组近似最优化的PID参数,拟合数据得到PID参数曲线,利用该曲线进行电液伺服系统的在线整定。该方法可实现近似最优的PID参数在线整定,控制系统的性能得到了明显的提高。仿真结果证明了该方法的有效性。     

拟人智能控制在温控系统中的应用

针对实际多变量、强耦合非线性温控系统，设计了自适应拟人智能控制器。采用了分区控制思想，根据不同的误差和误差变化趋势，采用了不同的PID控制策略。为了更好地克服各种外界因素的影响，构造了误差观测器，在线实时调整控制器参数。同时，为了有效克服系统时滞特性的影响，进一步引入了提前补偿的控制思想。实际系统运行结果表明了该控制策略的有效性。     

基于自整定模糊PID控制的Buck变换器设计与仿真

DC-DC功率变换器在各个领域应用广泛,工业应用中大多采用PID控制。尽管PID控制具有结构简单、鲁棒性和可靠性高等特点,但PID参数不能随系统内部参数的变化而自行调整,导致无法达到最优控制。为此,设计一种基于模糊控制理论的、可在线自整定参数的PID算法,并用Matlab2012对典型Buck变换器和模糊自整定PID算法仿真。结果表明:模糊PID控制器既可实现高精度、高鲁棒性控制,又能完成PID参数的在线自整定。     

基于遗传算法优化的跳汰机排料系统PID控制仿真

针对传统PID控制方法应用于跳汰机排料系统时难以获得最优控制参数、控制效果差的问题,提出一种基于遗传算法的PID控制参数优化方法,介绍了基于遗传算法优化的PID控制结构、参数优化方法及步骤,并以某矿井跳汰机排料系统为例,对基于该方法的PID控制器的控制性能进行了仿真研究。仿真结果表明,该方法能够实现PID控制参数的在线优化,收敛速度快,具有较强的鲁棒性;基于该方法的PID控制器具有良好的动、静态性能,无超调现象,控制精度高。     

基于模糊RBF神经网络控制器的锅炉汽包水位控制的实现

锅炉是典型的复杂热工系统。对蒸汽锅炉而言，维持汽包水位在一定的范围内是保证锅炉安全运行的首要条件。本文介绍了一种锅炉汽包水位控制器，采用基干模糊RBF神经网络整定的PID控制方法。通过对阶跃输入信号作用下系统动态性能的仿真分析，表明该控制器具有较好的适应性，控制效果得到明显改善。     

基于Adaptive Interaction理论的PID控制器的设计及应用

本文在Adaptive Interaction理论的基础上,提出了一种新的自调整 PID 控制器。这种新的控制器根据输入及其误差信号进行在线训练,通过误差评价函数的最小化,在模型未知的情况下能很好地调整比例、积分、微分三个参数。对于被控对象的变化具有鲁棒性,很大程度上解决了传统的 PID 控制器对于非线性、不稳定系统控制效果不佳及在线调整困难的问题。通过仿真实例,验证了应用 Adaptive Interaction 理论的 PID 控制器的有效性和实用性。     

基于多步预测性能指标的模糊控制器参数优化设计

针对一类PID型模糊控制器，提出基于多步预测性能指标的模糊控制器参数优化设计方法．通过最小化多步预测性能指标和调整比例因子，定量给出了控制器的设计准则，对于现场操作人员具有一定的指导意义．仿真实例验证了算法的有效性和鲁棒性．     

红外成像目标模拟器方位系统的对偶自校正PID控制

红餐成像目标模拟器方位系统是一参数快时变系统,针对该系统的具体情况提出了一种对偶自校正ＰＩＤ控制器,在-每一适应步,通过谱分解得到最优ＰＩＤ参数,然后基于双重指 标进行对偶校正,得到一种既保持对偶效应,垒十分简单易行的对偶自校正ＰＩＤ控制器,成功地消除了传统自适应控制系统的“关断”、“终止”和“猝发”等现象,收到了良好的的控制效果,该控制器适于参数随机变化或快进变系统。     

网络控制系统的模糊自适应PID控制

针对网络控制系统中网络时延补偿的问题,提出了一种模糊自适应PID控制器的设计方法,通过利用在线时延估计方法对时延进行预估计,根据估计时延值在线调节PID三个参数,从而改善系统的性能。对基于控制局域网络(CAN)总线的典型工业过程进行了仿真实验。结果表明,与常规PID的控制器比较,采用设计的PID控制器能补偿网络上的时延,更加有效的抑制了时延对网络控制系统的影响并提高了系统的性能。     

Chaos synchronization of nonlinear gyros using self-learning PID control approach

Since chaotic systems are important nonlinear deterministic systems that display complex, noisy-like and unpredictable behavior, synchronizing chaotic systems has become an important issue in the engineering community. Due to the proportional-integral-derivative (PID) controller has a simple architecture and easily designed, it was widely used in the industrial applications. However, the traditional PID controller usually needs some manual retuning before being used to practically application. To tackle this problem, this paper proposes a self-learning PID control (SLPIDC) system which is composed of a PID controller and a fuzzy compensator. The PID controller which is used to online approximate an ideal controller is the main controller. The controller gain factors of the PID controller can automatically tune based on the gradient descent method. The fuzzy compensator is designed to dispel the approximation error between the ideal controller and PID controller upon the system stability in the Lyapunov sense. From the simulation results, it is verified that the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized by the proposed SLPIDC scheme without the chattering phenomena in the control effort after the controller parameters learning.