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。     

Enhanced control of integrating cascade processes with time delays using modified Smith predictor

This paper presents a simple cascade controller in the enhanced modified Smith predictor structure for control of integrating processes. The proposed structure consists of two control loops, a secondary inner loop and a primary outer loop. The method has totally three controllers of which the secondary loop has one controller and the primary loop has two controllers. The secondary loop controller is designed using IMC technique. The primary loop set-point tracking and disturbance rejection controllers are designed using direct synthesis method. The primary set-point tracking controller is designed as a PID with lag filter and the primary disturbance rejection controller is designed as a PD with lead-lag filter. Simulation studies have been carried out on various cascade integrating processes with/without zero. The present method gives significant disturbance rejection both in the inner and outer loops and also shows significant improvement when compared to the recently reported methods.     

基于均匀分布NSGAII算法的污水处理多目标优化控制

针对污水处理过程控制中能耗过大、出水水质超标严重等问题，提出了一种基于均匀分布的NSGAII(non-dominated sorting genetic algorithm II based on uniform distribution, UDNSGAII)多目标优化智能控制系统。首先，该方法以污水处理能耗和出水水质作为优化目标，建立多目标优化模型。其次，为了获得溶解氧和硝态氮的优化设定值，提高Pareto解的性能，该算法将种群映射到目标函数对应的超平面, 并在该平面上进行聚类以增加解的多样性。此外，加入分布性判断模块和分布性加强模块提高解的分布性。最后，采用比例积分微分(proportional integral derivative, PID)控制器对溶解氧和硝态氮的优化设定值进行底层跟踪控制。为了验证该算法的有效性，采用国际基准的污水处理仿真平台(benchmark simulation model No.1, BSM1)来进行实验。结果显示，所提出的UDNSGAII多目标优化控制方法能够在满足出水水质达标的同时，有效地降低污水处理过程能耗。     

基于多目标粒子群算法的污水处理智能优化控制

为了满足污水处理过程出水水质排放达标的同时降低运行能耗,提出了一种基于多目标粒子群的污水处理多目标智能优化控制方法。首先,通过分析污水处理运行数据,建立了基于自适应回归核函数的污水处理能耗和出水水质模型;其次,设计出一种污水处理多目标优化方法,利用多目标粒子群优化算法同时对污水处理自适应能耗和出水水质模型进行优化,获得溶解氧和硝态氮浓度的优化设定值;最后,利用PID控制器对溶解氧和硝态氮浓度优化设定值进行跟踪控制,实现了污水处理过程的多目标优化控制。基于污水处理基准仿真平台BSM1的实验结果显示,该多目标优化控制方法不但能够保证出水水质达标,而且能有效降低污水处理过程的能耗。     

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.     

基于多策略自适应差分进化算法的污水处理过程多目标优化控制

针对污水处理过程中的能耗过高和出水水质不达标等问题,提出一种基于多策略自适应差分进化算法的污水处理过程多目标优化控制方法。首先,在常规跟踪控制结构的基础上,增加对第3、4单元溶解氧浓度的跟踪控制,扩大了能耗和出水水质的优化调节范围。然后,设计一种多策略自适应差分进化算法（MSADE）,该算法采用多策略融合变异和排序优选方法,选取合适的变异策略和较优的随机个体引导种群变异,并根据进化过程信息自适应地更新交叉率,以提升算法的收敛性和pareto解的多样性。最后,将MSADE算法与PID控制器相结合,并构建以能耗和出水水质为优化目标的多目标优化问题,实现对溶解氧和硝态氮浓度设定值的动态寻优和跟踪控制。基于国际基准仿真平台BSM1进行验证,结果表明所提的多目标优化控制方法能有效降低污水处理过程的能耗并提升出水水质。     

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。     

化工过程多回路PID控制系统模式切换参数自整定

化工过程一般为多变量系统,但其主要控制方案为分散多回路PID常规控制。由于多变量系统内部存在不同程度的耦合作用,各控制回路之间存在相互影响,当其他回路进行手动/自动模式切换时,本回路等效被控对象将会发生突变,导致本回路的原有控制参数不能适应等效被控对象的变化,造成控制性能下降,甚至闭环系统不稳定。为避免这种情况的发生,从整个系统的角度研究控制回路模式切换时的稳定性,采用多变量频域Nyquist阵列设计法。基于对角优势下正Nyquist稳定性判据,从Gershgorin圆边界点的角度定量分析各个控制回路在模式切换前后的稳定性变化程度,从而确定各回路控制器增益的调整方向及程度,实现各回路的控制器参数在控制回路模式切换瞬间的自动整定,尽可能抵消控制回路模式切换对整个系统的扰动,保证整个系统的闭环稳定性。以Shell公司重油分馏塔的多回路PID控制系统为例,将3个PID控制回路依次投用时,根据Gershgorin圆边界点进行控制参数的自整定,闭环系统仍能保持一定的控制性能,否则闭环系统将不稳定。     

Application of model predictive control to the BSM1 benchmark of wastewater treatment process

Wastewater treatment processes are difficult to be controlled because of their complex and nonlinear behavior. This paper applied model predictive control (MPC) to the Benchmark Simulation Model 1 (BSM1) wastewater treatment process to maintain the effluent quality within regulations-specified limits. Good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. In presence of influent disturbances, two approaches have been studied: the addition of a feedforward action based on the measurement of the influent flow rate; the use of nonlinear model predictive controller by addition of a penalty function. The effects of two approaches were visible on the decrease of ammonium and nitrogen concentration which were considered as being of major importance. The results show that MPC can be effectively used for control in wastewater treatment process. By comparing performances, the nonlinear model predictive control strategy with penalty function demonstrates best with small effluent quality index and acceptable aeration and pumping energy consumption.     

带前馈的PID控制回路的控制器性能评估

提出一种评估带前馈的PID控制回路的控制器性能的方法.该方法通过求取在最优前馈和反馈控制器作用下的最小过程输出方差,并将其与实际的输出方差比较,以评估控制器的性能,同时可以得到实现该最小方差的最优PID控制器的参数.仿真结果表明,引入前馈作用可以改善PID控制回路的性能,并且采用本文提出的评估方法可以得到一个合理的评估结果.     

基于模糊神经网络的污水处理生化除磷过程控制

针对污水处理生化除磷过程中出水总磷难以实时达标的问题,提出了一种基于模糊神经网络(fuzzy neural network,FNN)的出水总磷控制方法。首先,通过分析污水处理生化除磷机理,确定了控制器的操作变量为生化反应池第五分区外部碳源(external carbon, EC)与溶解氧(dissolved oxygen, DO)传递系数。其次,设计了一种基于FNN的出水总磷控制器,采用梯度下降算法更新控制器参数;最后,将基于FNN的出水总磷控制器应用于污水处理过程基准仿真平台(benchmark simulation model No.1,BSM1),实验结果表明,基于FNN的出水总磷控制器能够保证出水总磷的达标排放,具有较好的控制效果。     

A modified active disturbance rejection control for a wastewater treatment process

Waste water treatment process (WWTP) control has been attracting more and more attention. However, various undesired factors, such as disturbance, uncertainties, and strong nonlinear couplings, propose big challenges to the control of a WWTP. In order to improve the control performance of the closed-loop system and guarantee the discharge requirements of the effluent quality, rather than take the model dependent control approaches, an active disturbance rejection control (ADRC) is utilized. Based on the control signal and system output, a phase optimized ADRC (POADRC) is designed to control the dissolved oxygen and nitrate concentration in a WWTP. The phase advantage of the phase optimized extended state observer (POESO), convergence of the POESO, and stability of the closed-loop system are analyzed from the theoretical point of view. Finally, a commonly accepted benchmark simulation model no. 1. (BSM1) is utilized to test the POESO and POADRC. Linear active disturbance rejection control (LADRC) and the suggested proportion-integration (PI) control are taken to make a comparative research. Both system responses and performance index values confirm the advantage of the POADRC over the LADRC and the suggested PI control. Numerical results show that, as a result of the leading phase of the total disturbance estimation, the POESO based POADRC is an effective and promising way to control the dissolved oxygen and nitrate concentration so as to ensure the effluent quality of a WWTP.     

A modified active disturbance rejection control for a wastewater treatment process

Waste water treatment process (WWTP) control has been attracting more and more attention. However, various undesired factors, such as disturbance, uncertainties, and strong nonlinear couplings, propose big challenges to the control of a WWTP. In order to improve the control performance of the closed-loop system and guarantee the discharge requirements of the effluent quality, rather than take the model dependent control approaches, an active disturbance rejection control (ADRC) is utilized. Based on the control signal and system output, a phase optimized ADRC (POADRC) is designed to control the dissolved oxygen and nitrate concentration in a WWTP. The phase advantage of the phase optimized extended state observer (POESO), convergence of the POESO, and stability of the closed-loop system are analyzed from the theoretical point of view. Finally, a commonly accepted benchmark simulation model no. 1. (BSM1) is utilized to test the POESO and POADRC. Linear active disturbance rejection control (LADRC) and the suggested proportion-integration (PI) control are taken to make a comparative research. Both system responses and performance index values confirm the advantage of the POADRC over the LADRC and the suggested PI control. Numerical results show that, as a result of the leading phase of the total disturbance estimation, the POESO based POADRC is an effective and promising way to control the dissolved oxygen and nitrate concentration so as to ensure the effluent quality of a WWTP.     

Model predictive control for wastewater treatment process with feedforward compensation

Being an optimizing technology, model predictive control (MPC) can now be found in a wide variety of application fields. The main and most obvious control goal to be achieved in a wastewater treatment plant is to fulfill the effluent quality standards, while minimizing the operational costs. In order to maintain the effluent quality within regulation-specified limits, the MPC strategy has been applied to the Benchmark Simulation Model 1 (BSM1) simulation benchmark of wastewater treatment process. After the discussion of open loop responses of outputs to manipulated inputs and measured influent disturbances, the strategies of feedback by linear dynamic matrix control (DMC), quadratic dynamic matrix control (QDMC) and nonlinear model predictive control (NLMPC), and improvement by feedforward based on influent flow rate or ammonium concentration have been investigated. The simulation results indicate that good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. Compared to DMC and QDMC, NLMPC with penalty function brings little improvement. Two measured disturbances have been used for feedforward control, the influent flow rate and ammonium concentration. It is shown that the performance of feedforward with respect to the influent ammonium concentration is much higher than for the feedforward with respect to the influent flow rate. However, this latter is slightly better than the DMC feedback. The best performance is obtained by combining both feedforward controllers with respect to the influent ammonium concentration and flow rate. In all cases, the improvement of performance is correlated with more aeration energy consumption.     

计量泵行程调节的鲁棒非脆弱PID控制器设计

This job focuses on the stroke regulation of a class of high-precision metering pumps.A parametertuning method of robust non-fragile PID(proportional-integral-derivative)controllers is proposed with the assumption that a PID controller has additive gain perturbations.An H-infinite robust PID controller can be obtained by solving a linear matrix inequality.This approach can guarantee that the closed-loop control systems is asymptotically stable and the H-infinite norm of the transfer function from the disturbance to the output of a controlled system is less than a given constant to attenuate disturbances.The simulation case shows that the control performance of the proposed strategy is significantly better than the traditional PID approach in the situation with perturbations of controller parameters.     

鲁棒模型预测控制系统的评估基准

在控制系统的性能评估中,基准的设计是个重要问题。将基本设计极限理论推广到模型预测控制系统(MPC),建立性能评估基准。直接考虑多输入多输出系统的频域扰动,建立输出反馈鲁棒模型预测控制器。此控制器仅仅依赖于过程参数,也是令闭环系统达到控制性能极限的基准控制器。建立了用于评估的性能指标,提出基于此基准的性能评估程序,用以评价其他模型预测控制系统的性能。数学算例证实了这一评估程序的有效性。     

新一代的自适应模型预测控制器

提出了新一代的自适应模型预测控制器,自适应MPC控制器由MPC控制模块、在线辨识模块、性能监控模块3个模块组成,相互协调配和来实现自适应MPC控制。除了控制器功能设计以外,其余过程均可自动进行。对于新建MPC应用,首先进行多变量测试与辨识,在模型符合控制要求时,自动进入控制器投运。通过控制器性能监视发现模型不满足控制要求精度时,触发一次多变量模型测试与辨识过程,替换原有模型进行控制,保证控制器性能始终处于最佳状态。自适应MPC控制器在PTA装置上的应用表明了算法的有效性。     

核电站蒸发器水位的二自由度模型驱动控制

针对核电站蒸发器水位被控对象的非自平衡、非线性等特性,传统PID控制难以获得期望的控制要求,设计了二自由度模型驱动PID(TDF MD PID)串级控制系统,并加入前馈控制,以克服蒸汽流量的影响。该方法通过PD反馈补偿器改善蒸发器水位对象的开环不稳定特性,使其达到稳定,然后进行控制。仿真结果表明,与传统串级PID相比,TDF MD PID结构简单,参数易于整定,具有快速的跟踪能力和很强的鲁棒性,有效地提高了系统的控制性能。     

一种比例因子自调整的模糊预测函数控制

针对由主、副被控对象级联而成的大纯滞后系统,提出一种内回路采用PI控制、外回路采用具有比例因子自调整的模糊增量型预测函数控制策略,它将预测函数控制和具有比例因子自调整的模糊控制进行综合来共同控制由内回路和主被控对象构成的广义被控对象。大量的仿真实验表明:本方法较其他方法控制效果更好,即使在模型严重失配时,本方法仍具有很强的鲁棒性和抗干扰能力。     

模糊自整定PID控制器的设计与仿真

制浆碱回收蒸发工段第一步就是通过蒸汽将黑液进行浓缩,蒸汽的温度控制效果对后续燃烧工段和苛化工段起着举足轻重的作用。主管道蒸汽温度控制通常具有大惯性、大延迟、时变等特性,采用常规的PID控制难以获得满意的控制效果。为此,提出一种模糊自整定PID控制器的串级控制算法,该算法很大程度上提高了主蒸汽温度的控制品质。针对隶属度函数对模糊推理模型的精度影响和控制算法的特点编写了模糊规则,并且根据动态性要求的不同,分两种情况进行比较。仿真表明,模糊自整定PID串级控制算法在整体性能上要优于传统PID串级控制。