基于多模型混合的广义最小方差控制性能评估

针对实际工业过程中控制系统经常会受到时变扰动的影响,致使针对单一扰动模型设计的性能评估方法不再适用于时变扰动控制系统的问题,提出了基于多模型混合的广义最小方差控制性能评估方法．该方法综合考虑被控对象输出方差与控制器输出方差的两个指标,同时提出了一种“判断—加权”的控制器设计策略．首先,在任一时间段选取使广义输出方差最小的控制器,并判断其与上一时间段采用的控制器是否一致;然后,在此基础上采用多模型混合思想进行控制器设计,并将其作用下的广义输出方差作为性能评估的基准．通过乙烯裂解炉仿真,验证了本文所述方法在时变系统性能评估中的有效性．     

基于多模型混合最小方差控制的时变扰动控制系统性能评估

在实际工业过程中,控制系统经常会受到时变扰动的影响,致使针对单一扰动模型设计的最小方差控制准则不再适用于评估时变扰动控制系统的性能. 当多个扰动信号同时出现时,采用常规多模型切换方法会发生间歇切换进而产生较大的暂态误差,不能准确评估系统当前性能. 针对上述问题,本文提出了一种基于多模型混合最小方差控制准则的性能评估方法. 首先根据每个扰动模型分别制定最小方差控制器,组成多模型最小方差控制器,然后在每个时间点混合多模型最小方差控制器,并将在其作用下的输出方差作为最终的性能评估基准,该方法既 充分考虑到每个扰动的特性,又避免了常规多模型切换方法因间歇切换而产生的暂态误差对评估结果准确性带来的影响,实现了准确、可靠地评估时变扰动控制系统的性能. 通过仿真,验证了基于多模型混合最小方差控制准则的性能评估方法的有效性.     

多时变扰动下的PID控制性能评估

时变扰动是影响控制系统性能的主要因素之一。针对存在多个时变扰动的情况,提出了一种基于多模型加权的PID最小可达方差控制性能评估方法。首先,根据每个扰动的特性设计子PID最小可达方差控制器;然后,基于扰动作用时间构造权重以设计多模型加权控制器,再以此控制器作用下系统输出所能达到的最小方差作为基准评估PID控制系统的性能。最后,通过仿真实例验证所提方法的有效性。     

基于输出方差限制的广义多变量控制系统性能评价

研究具有线性时变扰动的多变量控制系统性能评价的方法.通过将时变扰动分为三类定常扰动, 进而构造一个加权的性能指标,权值矩阵与不同扰动类型和优先级相对应.在指定合理的输出方差后, 运用对角关联矩阵方法明确计算出广义多变量系统闭环输出方差的上下限值.经过ITAE (Integral of time-weighted absolute value of the error) 寻优得到最 小输出方差下的控制器参数,并给出可实现的最优控制器模型.仿真实例证明了该方法计算的简便性和有效性.     

一种改进的自抗扰解耦方法及其应用仿真

针对一类多入多出系统的解耦问题,提出了一种基于扩张状态观测器(ESO)的动态解耦方法。将系统输入变量间的耦合作用,被控对象参数时变和外界干扰视为一个总的扰动,用ESO估计扰动并反馈到控制器进行补偿,从而实现动态解耦,对解耦后的子系统按照极点配置方法设计出控制器。采用参数动态确定法确定ESO的参数。上述动态解耦方法简化了解耦过程,放松了对系统模型精度的要求,计算量小,响应速度快,鲁棒性强。并通过对常压蒸馏塔模型进行仿真控制,并与模糊PID解耦控制方法对比,结果表明本方法有效可行。     

基于参数辨识的自适应解耦控制算法的研究

多变量模型的复杂结构、强耦合性、被控对象参数的未知、慢时变等问题要求控制器必须具有良好的自适应性,针对以上问题提出了一种基于改进的广义最小方差闭环自适应解耦控制器实现更好的自适应,其由参数可调的控制器和自适应控制律组成,此控制器通过将闭环系统方程的传递函数矩阵等于期望的对角矩阵来实现解耦,同时改进的辨识算法可进行在线辨识控制器的参数实现同步自适应解耦。通过以CARMA为多变量控制模型,采用该方法进行仿真有效的解决了多变量之间的耦合性。结果表明该方法能够适应相应的变化,跟踪性能较好,且具备良好的解耦能力,进而保证了闭环系统的稳定性,从而验证了此方法能够效提高控制系统的稳定性和鲁棒性。     

一种实用的模型自适应MPC策略

在丙烯精馏塔的控制中,由于被控对象的非线性特性,采用线性模型的模型预测控制器难以保持良好的控制性能。本文提出基于系统稳态模型的模型自适应MPC策略,利用稳态模型在不同操作点上被控变量对操纵变量及扰动变量的相对变化率的变化,来刷新RMPCT控制器中各通道的模型增益。在模型输出对输入的相对变化率的计算中,使用主操作区间内的变化率以替代实际操作点的变化率,并采用设定模型变化域和控制模型变化频度的方法,以解决模型变化过大,与模型变换周期和RMPCT控制周期不协调而引起的系统不稳定等问题。实际投运效果表明:采用该控制策略,塔顶、塔底温度控制偏差与传统RMPCT比下降了一个数量级,有利于稳定与提高产品质量。     

基于ESO的动态解耦方法及其应用仿真*

针对多变量系统控制中的耦合问题,提出了一种基于扩张状态观测器（ESO）的动态解耦方法。该方法将系统输入变量间的耦合作用、被控对象参数时变和外界干扰视为一个总的扰动,用ESO估计该总扰动并反馈到控制器进行补偿,从而实现动态解耦;对解耦后的每个子系统,分别设计出了基于误差最小二乘指标的神经元自适应PID（NAPID）控制器。该方法简化了解耦过程,放松了对系统模型的要求,计算量小、鲁棒性强。最后用该法对蒸馏塔进行控制仿真,仿真时使用混沌优化方法对ESO的参数进行了离线优化,并给出了与模糊PID解耦控制方法对比的     

一种基于初始闭环系统的性能评估方法

基于初始闭环系统的输出方差和最小方差指标, 提出了一种新的性能评估方法. 在过程时滞变化的情况下, 基于最小方差指标的评估可能会得到错误的结论, 而新的方法可以避免这一缺点. 扩展的性能指标以控制器投运后的初始状态作为零基准, 能够更准确地反映操作工系统性能的变化, 能够很好地替代最小方差指标. 利用交互矩阵可将扩展指标推广到多变量系统的评估中, 本文将这一算法应用于精馏塔过程的评估. 精馏塔过程的仿真示例验证了方法的有效性, 表明过程时滞变化时用扩展指标来进行评估更能反映系统性能的变化.     

多变量预测控制工程应用的控制模型前馈解耦策略

针对多变量预测控制计算量大、控制效果对扰动和模型失配敏感等特点,提出一种适用于预测控制工程应用的控制模型前馈解耦策略.基于结构分析,保留重要的被控变量与操作变量配对关系,将不重要的被控变量与操作变量配对作为前馈引入进行补偿,简化了系统结构,降低了系统耦合程度,减弱了预测控制器对扰动和模型失配的敏感程度,极端情况下形成的单入单出或小规模多入多出系统有效减小了在线计算量;基于分布式预测控制思想,给出控制模型前馈解耦策略的分散优化策略,进一步减小了系统规模和在线计算量.最后,通过仿真验证了所提策略的可行性与有效性.     

Multivariable disturbance observer-based H2 analytical decoupling control design for multivariable systems

In this paper, an H₂ analytical decoupling control scheme with multivariable disturbance observer for both stable and unstable multi-input/multi-output (MIMO) systems with multiple time delays is proposed. Compared with conventional control strategies, the main merit is that the proposed control scheme can improve the system performances effectively when the MIMO processes with severe model mismatches and strong external disturbances. Besides, the design method has three additional advantages. First, the derived controller and observer are given in analytical forms, the design procedure is simple. Second, the orders of the designed controller and observer are low, they can be implemented easily in practice. Finally, the performance and robustness can be adjusted easily by tuning the parameters in the designed controller and observer. It is useful for practical application. Simulations are provided to illustrate the effectiveness of the proposed control scheme.     

Minimum variance control and performance assessment of time-variant processes

This paper is concerned with (1) an explicit solution of a minimum variance control law for linear time-variant (LTV) processes in the transfer function form, and (2) performance assessment of LTV processes using minimum variance control as the benchmark. It is shown that there exists a time-variant, absolute lower bound of process variance that is achievable under LTV minimum variance control and can be estimated from routine operating data. This lower bound can subsequently be used to assess the benefit of implementing LTV control such as adaptive control. The proposed methods are illustrated through simulated examples and an industrial case study.     

动态数据校正技术用于GMVC控制器性能评估的改进

在实际工业生产过程中,控制器性能的好坏直接影响了系统的收益;事实上,由于工作环境的复杂性,控制器容易受到各种干扰信号的影响,这通常会导致控制效果不符合预期;因此,对控制器的性能进行评估显得尤为重要;针对反馈系统中测量噪声的干扰问题,分析了测量噪声对基于广义最小方差控制(GMVC)的控制器性能评估(CPA)结果的影响;为了提升CPA的精度,提出了一种动态数据校正(DDR)方法来降低测量噪声对系统性能的影响;首先,在SISO和MIMO系统中引入基于GMVC的CPA;接着讨论了测量噪声对CPA结果的影响;最后,采用DDR滤波器来提升CPA结果的准确性;仿真中系统在不同情况下输出的比较验证了DDR滤波器的良好性能.     

An expert autotuner for multiloop SISO controllers

In this paper, an on-line expert autotuner for a class of 2-input-2-output multivariable process control applications is proposed. The autotuning controller, which uses a pattern-recognition technique is designed with a view to its practical implementation in multivariable processes. The main idea of the autotuning methodology is to use the observed multiloop responses with reference to the single-loop responses such that proper detuning of the SISO controllers is achieved. Customized identification techniques in SISO and MIMO environments based on closed-loop responses are developed for this application. Simulation results for a range of 2-input-2-output multivariable processes characterized by the Relative Gain (RG) and the relative dynamics are used to evaluate the performance of the autotuning controller under different conditions. The time response of the autotuning controller is compared to that of Biggest Log Modulus Tuning (BLT) method with a few distillation column models proposed in the literature.     

A dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlinear processes

 Conventional industrial control systems are in majority based on the single-input-single-output design principle with linearized models of the processes. However, most industrial processes are nonlinear and multivariable with strong mutual interactions between process variables that often results in large robustness margins, and in some cases, extremely poor performance of the controller. To improve control accuracy and robustness to disturbances and noise, new design strategies are necessary to overcome problems caused by nonlinearity and mutual interactions. We propose to use a dynamically-constructed, feedback fuzzy neural controller (DCF-FNC) from the input–output data of the process and a reference model, for direct model reference adaptive control (MRAC) to deal with such problems. The effectiveness of our approach is demonstrated by simulation results on a real-world example of cold mill thickness control and is compared with the performances of the conventional PID controller and the cascade correlation neural network (CCN). Exploiting the advantage of intelligent adaptive control, both the CCN and our DCF-FNC significantly increases the control precision and robustness, compared to the linear PID controller, with our DCF-FNC giving the best results in terms of both accuracy and compactness of the controller, as well as being less computationally intensive than the CCN. We argue that our DCF-FNC feedback controller with both structure and parameter learning can provide a computationally efficient solution to control of many real-world multivariable nonlinear processes in presence of disturbances and noise.     

Decoupling Controller with Disturbance Observer for LTI MIMO Systems

In this paper, a decoupling multivariable control strategy for linear time‐invariant (LTI) multi‐input/multi‐output (MIMO) systems is proposed. The strategy includes a multivariable disturbance observer (MDOB) and a decoupling controller. This MDOB is introduced to improve the system performances when the system encounters severe external disturbances. H₂ optimal scheme is utilized to design the MDOB filter. The controller is developed based on an inverse control method, through which the design process can be simplified. Simulation results certify the effectiveness of the proposed control strategy.     

Partially decoupled approach of extended non-minimal state space predictive functional control for MIMO processes

This paper presents a partially decoupled design of the state space predictive functional control for MIMO processes. The multivariable process is first treated into MISO process by a simple Cramer's rule solution to linear equations which provides a balance between model complexity and control system design, and then the derived MISO process based extended state space predictive functional control is presented. The overall design of the controller enables the controller to consider both the process state dynamics and the output dynamics, thus improved control performance for tracking set-points and disturbance rejection is resulted. The proposed controller is tested on both model match and model mismatch cases to demonstrate its superiority. In addition, a closed-form of transfer function representation that facilitates frequency analysis of the control system is provided to give further insight into the proposed method.     

Performance assessment of control loops with time-variant disturbance dynamics

If a process is subject to time varying disturbance dynamics (or time varying disturbance models), the time invariant minimum variance control for one type of disturbance dynamics is no longer minimum variance control for another type of disturbance. An explicit solution to a time-invariant optimal control that can optimize overall performance of time-variant processes is derived in this paper and is used as a benchmark to assess control performance of time variant process under time invariant control. This work is a continuation of the work by Huang [Can. J. Chem. Eng. 77(5) (1999) 1044]. It is shown that this performance benchmark can be found from routine operating data through time series analysis and optimization technique. The developed performance assessment technique is illustrated by a simulated example and applied to an industrial process.