An energy effective PID tuning method for the control of polybutadiene latex reactor based on closed-loop identification

The PBL (Polybutadiene Latex) production process is a typical batch process. Changes of the reactor characteristics due to the accumulated scaling with the increase of batch cycles require adaptive tuning of the PID controller being used. In this work we propose a tuning method for PID controllers based on the closed-loop identification and the genetic algorithm (GA) and apply it to control the PBL process. An approximated process transfer function for the PBL reactor is obtained from the closed-loop data by using a suitable closed-loop identification method. Tuning is performed by GA optimization in which the objective function is given by ITAE for the setpoint change. The proposed tuning method showed good control performance in actual operations.     

FUZZY GAIN SCHEDULED CSTR WITH GA-BASED PID

In this article, a new control scheme, the gain scheduled genetic algorithm (GA)-based PID is proposed for a continuous stirred tank reactor (CSTR). A CSTR is a highly nonlinear process that exhibits stability in certain regions and instability in other regions. The proposed control scheme implements the characteristics of the genetic algorithm's (GA) global optimization to optimize the PID's three control parameters, k_p, k_i, k_d, to obtain the best control effect by minimizing the integral square error online. The PID controller parameters tuned by the GA for each region are gain scheduled by a fuzzy logic scheduler. Fuzzy gain scheduling is a special form of fuzzy control that uses linguistic rules and fuzzy reasoning to determine the controller parameter transition policy for the dynamic plant subject to large changes in its operating state. Simulation results show the feasibility of using the proposed controller for the control of the dynamical nonlinear CSTR.     

具有遗传算法优化BP神经网络调节的HVDC PID控制器设计

高压直流输电(HVDC)定电流控制是直流输电系统的控制方式之一,为了使HVDC PID控制器具有较好的自适应性,实现控制器参数自动调整,提出一种具有遗传算法优化BP神经网络调节的HVDC PID控制器。该控制器首先利用遗传算法对BP神经网络的初始权值进行优化,再利用BP神经网络对PID三个参数进行在线调整。仿真研究表明:具有遗传算法优化BP神经网络调节的PID控制器可以保证系统动态响应具有较好的快速性和对系统扰动的自适应性,是可行的。     

ENHANCED IMC-PID CONTROLLER DESIGN WITH LEAD-LAG FILTER FOR UNSTABLE AND INTEGRATING PROCESSES WITH TIME DELAY

In this article, a design method for a PID controller is proposed based on IMC principles for control of open loop integrating and unstable first-order processes with time delay. The design is based on H₂ optimal closed-loop transfer function for set point changes and step input disturbances. The method has one tuning parameter, and systematic guidelines are provided for the selection of this tuning parameter based on peak value of the sensitivity function. The performance of the designed controller is verified on various integrating and unstable processes, and it is observed that nominal and robust control performance is achieved with the proposed design method. Improved closed-loop performance was obtained when compared to other methods recently reported in the literature. Further, the proposed method provides good closed-loop performance even when there are large uncertainties in the process parameters.     

Discrete system identification and self-tuning control of dissolved oxygen concentration in a stirred reactor

This work presents the applications of discrete-time system identification and generalized minimum variance (GMV) control of dissolved oxygen (DO) level in a batch bioreactor in which Saccharomyces cerevisiae is produced at aerobic condition. Air flow rate and mixing rate were varied to determine the maximum local liquid phase volumetric mass transfer coefficient (K _L a). Maximum K _L a value was determined at a mixing rate of 600 rpm and air flow rate of 3.4 Lmin⁻¹. For control purpose, manipulated variable was selected as air flow rate due to its effectiveness on the K _L a. To examine the dynamic behavior of the bioreactor, various input signals were utilized as a forcing function and three different model orders were tested. A second0order controlled auto regressive moving average (CARMA) model was used as the process model in the control algorithm and in the system identification step. It is concluded that the ternary input is more suitable than the other input types used in this work for system identification. Recursive least squares method (RLS) was used to determine the model parameters. GMV control results were compared with the traditional PID control results by using performance criteria of IAE and ITAE for different types of DO set point trajectories. DO concentration in the batch bioreactor was controlled more successfully with an adaptive controller structure of GMV than the PID controller with fixed parameters.     

PARAMETER IDENTIFICATION AND THE SELF-TUNING ADAPTIVE CONTROL BY A SENSITIVITY APPROACH

Parameter deviation identification and optimal controller tuning are derived by the sensitivities of the parameters. There are non-iterative and iterative algorithms which can be used for parameter identification, no matter the data come from an open-loop test or from a closed-loop operation. The controller tuning is based on the parameter sensitivities of an optimal regulator. The same tuning algorithm has been proved satisfactory for the PID control of the first order process with delay. By sequential implementations of both the identification and tuning, a self-tuning adaptive control system can be obtained. Numerical examples show the feasibility of such algorithms.     

PARAMETER IDENTIFICATION AND THE SELF-TUNING ADAPTIVE CONTROL BY A SENSITIVITY APPROACH

Parameter deviation identification and optimal controller tuning are derived by the sensitivities of the parameters. There are non-iterative and iterative algorithms which can be used for parameter identification, no matter the data come from an open-loop test or from a closed-loop operation. The controller tuning is based on the parameter sensitivities of an optimal regulator. The same tuning algorithm has been proved satisfactory for the PID control of the first order process with delay. By sequential implementations of both the identification and tuning, a self-tuning adaptive control system can be obtained. Numerical examples show the feasibility of such algorithms.     

基于神经网络和多模型的非线性自适应PID控制及应用

针对一类未知的单输入单输出离散非线性系统,提出了基于神经网络和多模型的非线性自适应PID控制方法。该方法由线性自适应PID控制器、神经网络非线性自适应PID控制器以及切换机构组成。采用线性自适应PID控制器可保证闭环系统所有信号有界;采用神经网络非线性自适应PID控制器可改善系统性能;通过引入合理的切换机制,能够在保证闭环系统稳定的同时,提高系统性能。理论分析表明,该方法能够保证闭环系统所有信号有界,如果适当地选择神经网络的结构和参数,系统的跟踪误差将收敛于任意给定的紧集。将所提出的方法应用于连续搅拌反应釜,仿真结果验证了所提出方法的有效性。由于该方法基于增量式数字PID控制器,在工业过程中有着广阔的应用前景。     

一阶时滞不稳定对象的PID控制器多目标优化设计

针对一阶时滞（FOPDT）不稳定对象,提出了一种基于参数稳定空间的多目标遗传算法进行PID控制器的优化设计。该算法首先采用两级控制结构,以保证PID控制器参数稳定空间的存在性,然后,基于该稳定空间提出了一种多目标遗传算法,用来求解带约束的多目标PID控制器参数整定问题。数值例子和生物反应器仿真研究表明所提方法的有效性。     

Analytical design of PID controller cascaded with a lead-lag filter for time-delay processes

An analytical method for the design of a proportional-integral-derivative (PID) controller cascaded with a second-order lead-lag filter is proposed for various types of time-delay process. The proposed design method is based on the IMC-PID method to obtain a desired, closed-loop response. The process dead time is approximated by using the appropriate Pade expansion to convert the ideal feedback controller to the proposed PID·filter structure with little loss of accuracy. The resulting PID·filter controller efficiently compensates for the dominant process poles and zeros and significantly improves the closed-loop performance. The simulation results demonstrate the superior performance of the proposed PID·filter controller over the conventional PID controllers. A guideline for the closed-loop time constant, λ, is also suggested for the FOPDT and SOPDT models.     

PID controller tuning for desired closed-loop responses for SISO systems using impulse response

Most of the proportional-integral-derivative (PID) controller tuning methods reported in literature are based on the approximate plant models (FOPDT or SOPDT models) derived from the step response of the plant. In this paper, a new method of designing PID controllers using ‘impulse response’ instead of ‘step response’ of the plant is presented. Treating the impulse response of the plant as a statistical distribution, the ‘mean’ and the ‘variance’ of the distribution are calculated and used in the calculation of PID controller parameters. Thus, the proposed method requires no approximation of the plant by any model. In this paper, a direct synthesis approach to PID controller design is proposed that makes use of Maclaurin series of the desired closed-loop transfer function, truncated up to the first three terms. PID controller parameters are synthesized to match the closed-loop response of the plant to the desired closed-loop response. Formulae for the calculation of PID controller tuning parameters are derived for the desired closed-loop response models of the types FOPDT and SOPDT. Only stable SISO systems are considered. The PID controllers tuned result in closed-loop responses very close to the desired response and perform equally well compared to other tuning methods reported in literature.     

基于次最优模型降阶的自整定PID控制器

针对过程工业中含有振荡环节和大滞后环节较难控制的被控对象,设计了一种基于频域辨识的自整定PID控制器.该控制器采用次最优模型降阶算法,辨识出二阶加纯滞后的模型;然后基于给定的相位裕度和幅值裕度,整定出PID参数.并通过仿真实验表明对于不同的被控对象,自整定得到的PID参数均能取得较好的控制效果.     

Experimental Research on Parallel Conversion Control of Drying Temperature in a Closed-Loop Heat Pump Dryer

The paper proposes a method to handle the challenge of temperature control in a closed-loop heat pump dryer that can operate both in heating cycle and refrigeration cycle. It is hard to avoid the violent fluctuations of drying temperature as the operation mode changes. Hence, the parallel conversion control is introduced to realize the accurate control of drying temperature. The main idea of the controller design lies in the use of different membership functions and fuzzy control rules for the two operation modes. Experimental results show that the temperature fluctuations are reduced when using parallel conversion control with fast and stable response as compared to the fixed-frequency and PID control system. One of the parameters, R _f , representing the value of undershoot ratio is 0.4, 0.48, and 0.64 when drying temperature is fixed at 30, 35, and 38°C, respectively, with a parallel conversion controller. In order to study the application prospects of the parallel conversion control system, a comparison experiment with different compressor frequency ranges was also conducted.     

基于期望闭环系统响应的网络化串级控制系统PID整定

研究有常数网络诱导时延的一类典型网络化串级控制系统的PID控制器参数整定问题。系统中的网络诱导时延为常数,主对象和副对象均为一阶惯性加纯迟延环节(FOPDT)。主控制器是有超前滞后校正环节的PID,副控制器是带有滤波环节的PID。基于过程模型和期望闭环系统响应整定PID参数,采用一阶P偄de近似纯迟延环节,以解析的形式推导出了主控制器和副控制器整定参数的表达式,并采用双线性变换法得到离散时间PID控制器。提出了网络化串级控制系统的控制性能综合指标,并基于TrueTime工具箱在不同网络诱导时延时对网络化串级控制系统进行了仿真研究。仿真结果和性能指标计算结果均验证了所提算法的可行性和有效性。     

一种新颖的鲁棒PID控制器设计方法

提出一种新颖的鲁棒PID控制器设计方法,基于最小最大原理寻找一组合理的PID控制器参数,使得闭环系统在一定模型变化范围内仍具有满意的控制品质,降低对模型不确定的敏感程度,提高系统的鲁棒性。鉴于直接求解min-max问题的困难,首先推导出不确定性最坏情况下性能指标的上界,然后将min-max问题转化为最坏性能指标上界的min问题,计算量小且求解容易。同时,证明了该算法的鲁棒稳定性。CSTR仿真例子表明了算法的有效性。     

APPLICATION OF ADAPTIVE PID CONTROL WITH GENETIC ALGORITHM TO A POLYMERIZATION REACTOR

This article describes the application of adaptive PID control with genetic algorithm (GA) to a jacketed batch polymerization reactor. This method was used to keep the polymerization reactor temperature at the desired optimal path, which was determined by the Hamiltonian maximum principle method. The reactor was simulated and the model equations of this jacketed polymerization reactor were solved by means of Runge-Kutta-Felthberg methods. A genetic algorithm can be a good solution for finding the optimum PID parameters because unlike other techniques it does not impose many limitations and it is simple. In this research, suitability of these parameters was checked by the integral absolute error (IAE) criterion. The control parameters in the PID algorithm were changed with time during the control of a polymerization reactor. It was seen that the genetic algorithm was able to tune the PID controller used in this system in terms of higher robustness and reliability by changing the parameters continuously.     

Synthesis of PID controller for unstable and integrating processes

Properly designed controllers provide stable closed-loop response for open-loop unstable processes. Internal model controller equivalent PID tuning rules for low order unstable plus dead time systems are synthesized in this work. The controller is approximated near the vicinity of zero (origin). Controller parameters are derived by equating the closed-loop response to a control-signature (desired closed-loop response) involving a user defined tuning parameter, λ. Simulations are carried out to show the performance of the proposed tuning scheme for both set point and disturbance rejection cases.     

ADAPTIVE PID CONTROL WITH AN ADJUSTABLE IDENTIFICATION INTERVAL

Adaptive PID control with an adjustable identification interval is proposed. The difficulty of choosing a suitable sampling time for control systems is prevented. The identification algorithm depends on that the identification interval is different from the sampling interval. Tuning of the PID controller is based on an adaptive first-order plus dead time model at each identification step. The model is obtained directly from an identified second-order one. Two illustrative examples are presented to show the considerable promise that the method exhibits.     

基于LonWorks的GA优化复合模糊PID控制系统设计

设计了基于LonWorks的复合模糊PID控制系统,分析了该系统的硬件组成和软件实现方案.在控制嚣设计上,改进了传统的模糊PID控制器结构,采用三个子模糊控制器组成一个复合模糊PlD控制器.把过渡时间作为各子模糊控制器的公共输入并采用遗传算法(GA)对复合模糊PID控制器的隶属度函数进行了优化.通过Mstlab仿真表明,该控制器在参数确定上更具客观性,在控制带有大滞后环节和非线性环节的工业对象时响应速度更快、抗干扰能力更强、动态性能更好.     

单神经元PID倒立摆系统及其仿真研究

为解决双PD倒立摆控制器参数不可调的难题,利用单神经元PID控制算法简单、权值可调的特点,针对倒立摆系统,设计出基于小车位移和摆杆摆角两个回路的单神经元PID控制器。通过仿真实验研究,证明了该控制方案的有效性和可行性。最后,将该控制方案与目前通常使用的双PD控制及LQR控制进行了比较。