基于灰色预测的大时滞过程的控制研究

针对传统PID控制对大时滞过程控制效果不佳的情况,采用基于GM(1,1)灰色模型对被控对象系统行为进行预测,并用等维新息、提高原始数据列的光滑度和改变初始条件三者结合的方法对该模型进行改进,得到一个改进的GM(1,1)模型.同时以GM(1,1)模型的发展系数a作为决定预测步长的依据,将其与传统PID结合,组成灰色预测控制系统对大时滞过程进行控制.仿真结果表明,与传统PID控制相比,该方法具有较强的适应性和鲁棒性,控制性能也得到了较明显的改善.     

基于灰色预测PID的风光互补发电机控制研究

为了能够提高风光互补发电机控制的效果,深入地研究了灰色预测PID在风光互补发电机控制中的应用.构造了风光互补发电机的数学模型;剖析了灰色预测的基本原理.针对风光互补发电机控制的实际情况,将灰色预测PID技术引入到风光互补发电机的控制中,提出了风光互补发电机灰色预测PID控制系统,并且提出了相应的算法流程;分别利用灰色预测PID控制技术、模糊PID控制技术和传统PID控制技术对风光互补发电机进行控制仿真研究,仿真结果表明灰色预测PID技术可以获得较好的控制效果.     

PID增益基于神经模糊网络的预测控制

提出一种基于二次逼近模型的PID增益预测控制,并阐述了该系统的结构、算法和应用特点.通过二次逼近建模的方法,提高了建模的算法速度和实际逼近精度以及较强的动态补偿能力.运用这种模型的预测和传统的PID相结合,使控制系统具有增益自适应能力和较好的鲁棒性.通过仿真实例对该方法的特点和性能进行了验证.     

支持向量机在网络广义预测控制中的应用

在网络控制系统的研究中,支持向量机(SVM) 在网络广义预测控制中的应用具有良好控制效果和稳定性.为提高网络性能,对网络控制系统进行模型预测,并将SVM作为广义预测控制(GPC) 算法中的预测模型,采用支持向量机的广义预测控制算法.进行预估技术和队列机制,对被控对象选择最合适的控制信号,降低了时延对网络控制系统的危害性,并通过Matlab上仿真结果表明,与PID控制相比较,基于SVM的GPC算法在网络控制方面超调量较小,调整时间较短,控制效果更好.     

射电望远镜PID控制系统仿真

射电望远镜控制系统是射电望远镜设计中的重点,控制系统的控制精度直接影响到射电望远镜的测量精度和灵敏度.通过对射电望远镜系统建模分析,发现传统的PID算法存在积分饱和缺陷问题,会造成系统超调,调节时间延长等不利结果.将积分分离PID控制算法引入到射电望远镜控制器设计中,利用LabVIEW实时仿真模块的功能,搭建了半实物仿真模型,分别采用传统PID和积分分离PID控制,对系统进行半实物仿真实验,提高控制器的控制性能,实验结果表明,积分分离PID控制效果良好.     

预估模糊逻辑控制在电厂水处理中的应用

在电厂水处理加药控制系统中,由于被控对象具有纯滞后、大惯性、难以建立数学模型等特点,常规的PID控制难以满足加药系统的控制要求。着重介绍了水处理加药系统的估模糊逻辑控制方案及其设计方法.并进行了仿真分析,表明采用预估模糊逻辑算法对加药系统控制能够得到满意的效果。     

基于PLC的内模PID窑炉温度控制系统设计

在工业生产过程当中,PID调节是经典控制理论中最典型的闭环控制方法,但是对于非线性大时滞大惯性的陶瓷窑炉温度控制系统.传统PID控制的效果不佳.本文简单介绍了基于内模控制原理的PID参数整定方法,详细地阐述了基于内模PID的陶瓷窑炉温度控制在西门子PLC S7-200中的算法实现.通过MATLAB软件仿真,将内模PID的陶瓷窑炉温度控制效果与传统PID控制进行比较,论证了其优良特性.     

模糊与天牛须优化算法对恒压供水PID系统的控制对比研究

针对目前城市二次供水恒压控制系统中存在非线性与滞后、间歇性扰动等带来的高层用户用水欠稳问题,建立供水数学模型,将模糊和天牛须优化算法分别引入到恒压供水PID控制算法中,分别设计了模糊和天牛须优化算法的Matlab程序和控制模型并进行仿真数据对比分析.仿真对比结果表明,相较于传统PID算法,模糊PID算法的抗干扰波动小,...     

基于模糊PID控制的掘进机恒功率控制系统的研究

针对悬臂式掘进机截割过程中负载变化剧烈、随机性大的问题,利用PLC和模糊PID控制实现对掘进机截割电机的恒功率控制,建立了仿真模型,并利用Simulink进行了仿真分析.结果表明:通过PLC和模糊PID控制的掘进机截割恒功率控制系统提高了系统的控制精度和稳定性,使掘进机能够根据不同的煤岩特性,调节摆动速度,提高了掘进机的工作效率.     

清洗机器人自适应PID控制研究

对于清洗机器人这样一个复杂的非线性、时变系统,常规PID控制方法难以达到满意的控制效果.提出了一种BP神经网络与常规PID(比例、积分、微分)控制相结合的智能控制方法,利用辨识网络逼近被控系统获得BP神经网络学习所需梯度信息,从而实现PID控制参数的在线调整,以适应控制系统的动态变化.对系统辨识模型进行了正弦输入下的逼近仿真;在对建立的运动学模型经离散化处理,得到其传递函数的基础上,对控制系统进行的角阶跃输入响应进行了对比仿真分析.仿真结果表明,辨识模型能很好的逼近被控对象,基于BP神经网络的自适应PID控制方法在解决清洗机器人控制问题时,提高了控制的响应时效,增强了系统的稳定性,获得了比传统PID控制更好的控制品质.     

Control of the common rail pressure in gasoline engines through an extended state observer based MPC

In this paper, a model predictive control (MPC) solution, assisted by extended state observer (ESO), is proposed for the common rail pressure control in gasoline engines. The rail pressure dynamic, nonlinear with large uncertainty, is modeled as a simple first order system. The discrepancy of the model from the real plant is lumped as ``total disturbance'', to be estimated in real-time by ESO and then mitigated in the nonlinear MPC, assuming the total disturbance does not change in the prediction horizon. The nonlinear MPC problem is solved using the Newton/generalized minimum residual (GMRES) algorithm. The proposed ESO-MPC solution, is compared with the conventional proportional-integral-differential (PID) controller, based on the high-fidelity model provided in the benchmark problem in IFAC-E-CoSM. Results show the following benefits from using ESO-MPC relative to PID (benchmark): 1) the disturbance rejection capability to fuel inject pulse step is improved by 12% in terms of recovery time; 2) the transient response of rail pressure is improved by 5% in terms of the integrated absolute tracking error; and 3) the robustness is improved without need for gain scheduling, which is required in PID. Additionally, increasing the bandwidth of ESO allows reducing the complexity of the model implemented in MPC, while maintaining the disturbance rejection performance at the cost of high noise-sensitivity. Therefore, the ESO-MPC combination offers a simpler and more practical solution for common rail pressure control, relative to the standard MPC, which is consistent with the findings in simulation.     

Multivariable control strategy based on bifurcation analysis of an industrial gas-phase polymerization reactor

In an industrial gas-phase polyethylene reactor, the safe operating range of temperature is rather narrow. Even within this temperature range, temperature excursions must be avoided because they can result in low catalyst productivity and significant changes in product properties. If the manipulated variable for temperature control saturates (i.e., the cooling water valve position is completely open), then the reactor operates without a feedback temperature controller, leading to oscillatory behavior and limit cycles. In this work, it has been demonstrated that the saturation in the manipulated variable and the complex non-linear dynamic behavior are removed when auxiliary manipulated variables, obtained by bifurcation analysis, are used in a multivariable control strategy for the reactor temperature control. Two control structures are proposed and compared considering their impact in the reactor production and polymer melt index. In the first control structure, the designed PID controller for the reactor temperature is considered and a switching strategy with a PI controller for the auxiliary manipulated variables is included. In the second control structure, the designed PID controller for the reactor temperature is also used, however, a MPC controller for the auxiliary manipulated variables is considered. The results suggest that the use of gain-scheduling strategy in the PID temperature controller with a MPC controller for the auxiliary manipulated variables avoids the saturation of the manipulated variable and, hence, the undesired non-linear dynamic behavior, reducing the production loss and improving the product quality.     

基于动力学等价的一种通用控制器的设计

在动力学运动方程的基础上,构建了一种系统状态观测器,该观察器能够精确估计被控对象的位置、速度和加速度而无需知道其数学模型。在此基础上,设计了一种通用控制器,该控制器通过系统运动的位置、速度和加速度的负反馈作用,把原被控对象的输出轨迹引导控制到期望的系统输出轨迹,能提高系统的控制品质和鲁棒性能。分析了PID控制器、内模控制器、状态控制器、预测控制器和鲁棒控制器的算法特性,指出这些控制器与所设计的通用控制器在动力学意义上具有等价性。     

A GESO based MPC approach to contour error control of networked motion control system

This paper presents a generalised extended state observer (GESO) based model predictive control (MPC) approach to contour error control for networked multi-axis motion system (NMAMS) with network-induced delays. First, the uncertainties induced by the network-induced delays are modelled as an additive bounded disturbance, and a novel model predictive controller based on the GESO is designed for the uniaxial trajectory tracking control system. The GESO is used to estimate the system state and the disturbance simultaneously, and the effects of the uncertainties induced by the delays are eliminated by the proposed GESO based controller. Then the contour error estimation method is adopted, and a PID controller is designed to compensate the contour error. Finally, experiments are carried out to demonstrate the effectiveness of the proposed method.     

一种通用学习网络自适应算法及其在预测控制中的应用

针对黑箱过程的辨识与控制,本文提出了一种选择通用学习网络(universal learning network,ULN)节点间延迟时间参数的自适应算法,并将其应用于对控制对象中的纯滞后参数的辨识．将通用学习网络与PID控制器相结合,应用于包含大滞后的系统的模型预测控制(model predictive control,MPC)中．仿真结果证明通用学习网络能够有效地辨识被控对象的纯滞后时间,并能够作为预估器应用于模型预测控制系统中．     

溶解氧浓度的前馈神经网络建模控制方法

针对污水处理过程溶解氧(DO)浓度控制问题,提出了一种基于前馈神经网络的建模控制方法(FNNMC).本文构造了神经网络建模控制系统,通过对建模神经网络和控制神经网络隐含层学习率的分析,证明了学习算法的收敛性以及整个系统的稳定性.最后,本文基于国际基准的Benchmark Simulation Model No.1 (BSMl)进行了仿真实验,验证了合理选取学习率的重要性,并通过与PID和模型预测控制(MPC)等已有控制方法的比较,验证了神经网络建模控制方法针对污水处理过程溶解氧浓度控制具有良好的建模能力,更高的控制精度以及更好的动态响应能力.     

Control of the Tennessee Eastman process using input-output models

This paper presents a case-study where model predictive control is applied to control a nonlinear, open-loop unstable process called the Tennessee Eastman Challenge Process. Both the base case and transitions between different operating points are considered. The control scheme is based on an input-output model identified from plant data. The Model Predictive Controller (MPC) controller acts as a supervisory controller that dictates the setpoints for a lower level PID loop structure. Simulations are presented to illustrate its effectiveness or disturbance rejection and setpoint tracking.     

Drilling performance analysis of drill column machine using proposed neural networks

Air-ratio is an important engine parameter that relates closely to engine emissions, power, and brake-specific fuel consumption. Model predictive controller (MPC) is a well-known technique for air-ratio control. This paper utilizes an advanced modelling technique, called online sequential extreme learning machine (OSELM), to develop an online sequential extreme learning machine MPC (OEMPC) for air-ratio regulation according to various engine loads. The proposed OEMPC was implemented on a real engine to verify its effectiveness. Its control performance is also compared with the latest MPC for engine air-ratio control, namely diagonal recurrent neural network MPC, and conventional proportional–integral–derivative (PID) controller. Experimental results show the superiority of the proposed OEMPC over the other two controllers, which can more effectively regulate the air-ratio to specific target values under external disturbance. Therefore, the proposed OEMPC is a promising scheme to replace conventional PID controller for engine air-ratio control.     

汽化冷却系统汽包水位的模糊控制

针对汽化冷却系统汽包水位控制常用的三冲量控制方法中采用PID作为控制器的一些不足,提出一种新的控制方法:用模糊控制完全替代PID算法,构建水位模糊控制器和假水位模糊控制器,并通过假水位判定,构成双控制器切换控制系统,对汽包水位进行控制,以期获得抗干扰能力强、鲁棒性好,控制精度高的优良调节品质。     

Online linearization-based neural predictive control of air–fuel ratio in SI engines with PID feedback correction scheme

Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online identification does not suit the real-time controller, due to its heavy computational burden. This work presents a computationally efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling air–fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives satisfactory performance and also adapts to the change in engine systems very quickly.