Optimal tuning of a control system for a second-order plant with time delay

An engineering method for optimizing the parameters of PI and PID controllers for a second-order controlled plant with time delay is considered. An integral quality criterion involving minimization of the control output is proposed for optimizing the control system, which differs from the existing ones in that the effect the control output has on the technological process is taken into account in a correct way. The use of such control makes it possible to minimize the expenditure of material and/or energy resources, to limit the wear, and to increase the service life of the control devices. The unimodal nature of the proposed quality criterion for solving optimal controller tuning problems is numerically confirmed using the optimization theory. A functional correlation between the optimal controller parameters and dynamic properties of a controlled plant is determined for a single-loop control system with the use of calculation methods. The results from simulating the transients in the control system using the proposed and existing functional dependences are compared. The proposed calculation formulas differ from the existing ones by having simple structure, high accuracy of searching for the optimal controller parameters; they allow efficient control to be obtained and can be used for tuning automatic control systems in a wide range of controlled plant dynamic properties. The obtained calculation formulas are recommended for being used by engineers specializing in automation for designing new and optimizing the existing control systems.     

Neural network‐based PID gain tuning of chemical plant controller

Plant control systems are now highly automated and are used in many industries. The control performance changes with the passage of time because of deterioration of plant facilities. For this reason, human experts tune the control system to improve overall plant performance. In this study, a PID control system for the oil refining chemical plant process is discussed. In oil refining, thousands of control loops are used in plants in order to keep the product quality at the desired value and to assure the safety of plant operation. Due to the ambiguity of the interference between control loops, it is difficult to estimate the plant dynamical model accurately. Using a neuro emulator and a recurrent neural networks model (RNN model) for emulation and tuning of parameters, a PID gain tuning system for a chemical plant controller is constructed. Numerical experiments using actual plant data demonstrate the effect of the proposed method. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 171(4): 28–36, 2010; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20973     

A procedure for tuning automatic controllers with determining a second-order plant model with time delay from two points of a complex frequency response

The problem of obtaining the mathematical model of a plant in the course of adaptively tuning the operating automatic closed-loop control systems is considered. A new method is proposed for calculating the parameters of a model with four free coefficients represented by two inertial sections with a time delay. The model parameters are calculated from the data of experiments on determining two points of a plant??s complex frequency response. The results from checking the performance of the method in combination with obtaining information on the plant dynamics by applying the Fourier transform to the impulse transient response of the system are presented. The PID controller is tuned using a parameter scanning algorithm with directly checking the amplitude-frequency response of the closed-loop system, using which the stability margin can be calculated and different quality criteria can be applied.     

An express method for optimally tuning an analog controller with respect to integral quality criteria

An express method for optimally tuning analog PI and PID controllers is considered. An integral quality criterion with minimizing the control output is proposed for optimizing control systems. The suggested criterion differs from existing ones in that the control output applied to the technological process is taken into account in a correct manner, due to which it becomes possible to maximally reduce the expenditure of material and/or energy resources in performing control of industrial equipment sets. With control organized in such manner, smaller wear and longer service life of control devices are achieved. A unimodal nature of the proposed criterion for optimally tuning a controller is numerically demonstrated using the methods of optimization theory. A functional interrelation between the optimal controller parameters and dynamic properties of a controlled plant is numerically determined for a single-loop control system. The results obtained from simulation of transients in a control system carried out using the proposed and existing functional dependences are compared with each other. The proposed calculation formulas differ from the existing ones by a simple structure and highly accurate search for the optimal controller tuning parameters. The obtained calculation formulas are recommended for being used by specialists in automation for design and optimization of control systems.     

Data‐Driven Controller Tuning for Nonminimum Phase Plants with Stability Constraints

This paper addresses the model reference control problem, which is a typical control problem found in data‐driven controller tuning methods. For nonminimum phase plants, the unstable zeros of the plant should be included in the reference to avoid destabilization of the resulting closed‐loop system and improve tracking performance. First, we propose a data‐driven controller tuning method with closed‐loop stability taken into consideration and with the tuned controller parameters in the time domain. If the plant has unstable zero(s), the proposed method would not lead to destabilizing controller in the worst case. Closed‐loop stability is checked using linear inequalities described with input/output data. This contributes to reducing computation in the proposed method. Moreover, this paper proposes a data‐driven controller tuning method for nonminimum phase plants estimating the unstable zero(s) using a flexible reference model at each parameter update and reflecting them into the resulting reference model. The effectiveness of the proposed method is confirmed through numerical experiments.     

基于RBF神经网络的PID整定在直线驱动无绳电梯中的应用

为克服无绳电梯中直线驱动系统的常规PID参数只能离线调节的不足,提出了基于RBF神经网络的PID参数在线整定方法。首先从PMLSM动态数学模型出发,阐述了RBF神经网络原理及PI自整定的实现过程,给出了基于RBF神经网络的PMLSM控制系统框图。最后,借助MATLAB/Simulink软件对PMLSM控制系统进行了建模与仿真。仿真结果表明提出的方法鲁棒性强、转矩响应快速及转速超调量小,在直线驱动无绳电梯领域具有良好的应用前景。     

Design of an Implicit GMV‐PID Controller Using Closed Loop Data

In industrial processes, PID control has been applied in many real systems. The control performance strongly depends on the PID parameters. Although some schemes for tuning PID parameters have been proposed, these schemes need system parameters which are estimated by system identification in order to calculate the PID parameters. On the other hand, model‐free controller design schemes represented by virtual reference feedback tuning (VRFT) or FRIT have received much attention in the last few years. These methods can calculate control parameters using closed loop data and are expected to reduce computational costs. In this paper, a type of implicit PID controller using closed loop data is proposed. In the proposed method, the PID parameters are calculated on the basis of the implicit generalized minimum variance control. The control performance can be suitably adjusted by means of only a user‐specified parameter. The effectiveness of the proposed method is numerically and experimentally evaluated.     

基于模糊神经网络再励学习控制器设计及其在倒车模型中的应用

考虑到神经网络学习算法的特点，给出了一种基于再励学习的自组织模糊CPN。它结合了模糊自组织CPN和再励算法的优点，在控制过程中在线调整网络结构以及对网络参数学习，学习效率高，控制结构简单。可以不要求受控对象的学习模型，实现在线控制，应用在倒车模型中仿真结果展示了所设计系统的良好控制性能。     

基于遗传算法的HVDC整流器自抗扰控制器设计

针对自抗扰控制器(ADRC)参数不易整定的不足,在ADRC的参数整定中应用了一种综合考虑了控制器动态性能和被控对象输入受限两方面的改进遗传算法,该算法应用到直流输电系统的整流器控制器的设计中可提高HVDC的鲁棒性。仿真结果表明所设计的整流器定电流自抗扰控制器具有良好的控制性能,对系统的外扰和模型参数的摄动具有良好的适应性,所提出的整流器自抗扰控制器设计方法有效可行。     

Adaptive transfer function-based control of nonlinear process. Case study: Control of temperature in industrial methane tank

The state model-based transfer function models are applied for adaptation of linear controller and disturbance compensator in a feedback/feed-forward control system of nonlinear process. An advantage of the presented adaptation method is the avoidance of artificial disturbances or iterative identification procedures for on-line estimation of process dynamic parameters. The adaptation is based on linearization of the process model at each sampling time about the current state point, independent of the process being at steady-state or transient conditions. The linear time-varying dynamics model is updated on-line using measured values of process variables and reduced to the first-order plus time delay transfer function models in order to directly apply well-developed controller tuning rules. Computational aspects of the adaptation method are discussed and computation algorithms are presented. The adaptive feedback/feed-forward control system was applied for controlling temperature in industrial methane tank, dynamic parameters of which vary in a wide range due to variations of methane-tank process load and external conditions. The heat balance-based process state model is developed and validated using observation data of real plant. Computer simulation of the proposed control system performance under extreme operating conditions demonstrates fast adaptation of controller parameters, robust behaviour and significant improvement in the controllers' performance compared to that of fixed-gain controllers. Copyright © 2007 John Wiley & Sons, Ltd.     

智能PID控制器及其在锅炉过热汽温自适应控制中的仿真研究

提出了一种新型智能PID控制器，并在电厂主汽温串级控制系统中进行仿真研究。智能PID控制器由模糊PD控制器和自调节积分环节并联组成，其特点是：模糊控制器的规则库由自适应神经元在线调节，积分器的增益由模糊推理机在线整定。因而该控制系统是无模型控制系统，无须被控对象的精确数学模型，系统可以实现参数的自整定，具有很强的自学习能力。对某超临界600MW直流锅炉主汽温控制的仿真结果表明，这种控制器可以实现多个工况点的控制，具有很强的自适应能力，并且在大范围的负荷变化和时变控制系统中仍具有稳定的控制效果、较强的鲁棒性和较好的抗扰动性能。     

基于最优开环截止频率学习的永磁伺服系统PI控制器参数整定方法

提出一种基于最优开环截止频率学习的永磁伺服系统PI控制器参数整定方法。针对永磁同步电机转速/电流级联的双PI控制系统,基于同步旋转坐标系下电机频域数学模型,推导出电流环和速度环PI控制器参数的解析表达式。根据期望的系统要求,从时域分析入手,对电流环和速度环分别构建性能评价函数,通过迭代给定激励信号下的最小评价函数值,学习性能最优的开环截止频率,从而实现电流环和速度环PI控制器参数整定。对拖伺服平台上的实验结果验证了所提方法的正确性和可行性。     

A quantitative design approach to PSS tuning

This paper presents the application of quantitative feedback design techniques for tuning stabilizers in multi-machine power systems. This approach facilitates easy handling of multiple plant models thereby yielding robust and reliable stabilizer parameters. Methods of incorporating closed-loop stability and damping performance requirements into the design are explained. In the proposed sequential tuning technique, bounds on the stabilizer frequency response are computed for stability and performance at each of the given set of operating conditions of the system. A manual controller shaping then yields the desired stabilizer parameters. Application to an illustrative textbook example of an 11-bus, four-generator system is also included.     

不稳定大时滞过程的串级PID控制

针对一类不稳定大时滞过程，提出了一种基于内模控制（IMC）框架下改进的串级PID控制结构，给出了参数设计的一般准则。系统双闭环均按照IMC原理设计为经典反馈控制结构，内环控制器主要用于镇定不稳定对象和抑制主要干扰，外环用于改善系统性能。内外环控制器均只用一个调节参数，调节参数和对象参数与闭环系统性能解析关系明确。仿真表明，该方法能有效控制滞后时间与时间常数之比大于1的大时滞过程，鲁棒性强，结构设计和调节简单，适合工程应用。     

基于Laguerre模型的过热汽温自适应预测PI控制系统

针对火电厂锅炉过热汽温控制的特点,设计1种基于Laguerre模型的自适应预测PI控制器。该预测控制器采用对时延具有良好逼近能力的正交Laguerre函数模型作为预测模型,利用带遗忘因子的最小二乘法在线辨识Laguerre预测模型的系数,以提高系统适应工况变化的能力;滚动优化指标采用比例积分型结构,以提高系统的快速性和鲁棒性。通过对具有严重参数不确定性、扰动以及大迟滞的电厂过热汽温被控对象进行仿真研究,结果表明该方法能够很好地适应对象特性的变化,且控制系统的性能比常规串级控制系统有较大提高。     

一种Smith预估系统的鲁棒PID控制算法

根据内模控制原理和现代鲁棒控制理论,针对Ｓｍｉｔｈ预估补偿方法,提出了一种新的一维鲁棒ＰＩＤ控制器设计方法,其优点是控制器的参数可以解析得到,且只有一个可调参数,被调参数与系统的鲁棒性有直接的联系,可较好地用于大纯滞后控制对象,为提高Ｓｍｉｔｈ预估系统的鲁棒性提供了一种有效的ＰＩＤ整定算法。     

Decentralized robust adaptive-output feedback controller for power system load frequency control

In this paper, a new model reference-decentralized robust adaptive-output feedback controller is proposed for the load frequency control (LFC) of large-scale power systems with unknown parameters. This control strategy requires only local input–output data and can follow random changes in the operating conditions. The controller is designed such that the trajectory errors and the control gains of each area remain uniformly bounded. In the proposed method, firstly an adaptive observer is designed to estimate the state variables and system parameters using local data only. Then a locally linear combination of the estimated states and the model reference states are used to design a robust adaptive-output feedback controller for each area. Simulation results for a three-area power system show that the proposed controller achieves good performance even in the presence of plant parameter changes and system non-linearities. Received: 18 October 2001/Accepted: 24 October 2001     

A design of multivariable,self‐tuning PID controllers with an internal model structure

Self‐tuning control schemes (STC) are useful for systems with unknown or slowly time‐varying parameters. Some single‐input/single‐output PID control schemes based on STCs have been proposed for such systems. However, there are a lot of multivariable systems in real process industries. And these systems often have relatively large time delays. In this paper, a design scheme of self‐tuning PID control system is proposed for multivariable systems with unknown parameters and time delays. The controlled object is equipped with an internal model in order to compensate the time delay and also unstable zeros. Subsequently, a multivariable PID controller is designed for the augmented or compensated system. The PID parameters are calculated recursively based on the relationship between the minimum variance control law and the PID control law. A simulation example is presented to demonstrate the effectiveness of the proposed scheme. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 146(4): 58–64, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10241     

APF直流侧电压控制小信号建模及参数整定

直流侧电压控制是并联型有源滤波器APF(active power filter)的关键环节,精确设计控制器参数有助于提高系统性能。在传统直流侧电压控制模型基础上建立了其控制小信号模型,进而提出一种基于小信号模型的PI参数整定方法。采用零极点配置将含有右半平面零点的小信号模型转化为普通的二阶滞后系统,根据劳斯判据和两步整定法详细讨论了PI参数的选取原则,重点分析不同PI参数对系统性能的影响。该方法在实验参数已知的情况下,可以准确计算出PI控制器参数,在保证系统谐波补偿性能的同时具有较小的直流侧电压超调。仿真和样机实验结果都表明了该方法的可行性。     

Robust self‐tuning PI decoupling control of uncertain multivariable systems

In this paper, for a class of multivariable systems with strong couplings, a robust self‐tuning PI decoupling controller is developed by combining a self‐tuning PI controller with a feedforward decoupling compensator and a filter. To determine the gains and other parameters of the PI decoupling controller, we first introduced a reduced order model. The parameters of the reduced order model are identified by using a normalized projection algorithm with dead zone. The gains of the PI controller together with other parameters are tuned online according to the certainty equivalent principle. By resorting to time‐varying operation, we presented the bounded‐input bounded‐output stability conditions and convergence conditions of the closed‐loop system. Simulation results on a synthetic system and a twin‐tank level system show the effectiveness of the proposed method. Copyright © 2011 John Wiley & Sons, Ltd.