Robust MPC for Linear Systems with Structured Time‐Varying Uncertainties and Saturating Actuator

In this paper, a synthesis of model predictive control (MPC) algorithm is presented for uncertain systems subject to structured time‐varying uncertainties and actuator saturation. The system matrices are not exactly known, but are affine functions of a time varying parameter vector. To deal with the nonlinear actuator saturation, a saturated linear feedback control law is expressed into a convex hull of a group of auxiliary linear feedback laws. At each time instant, a state feedback law is designed to ensure the robust stability of the closed‐loop system. The robust MPC controller design problem is formulated into solving a minimization problem of a worst‐case performance index with respect to model uncertainties. The design of controller is then cast into solving a feasibility of linear matrix inequality (LMI) optimization problem. Then, the result is further extended to saturation dependent robust MPC approach by introducing additional variables. A saturation dependent quadratic function is used to reduce the conservatism of controller design. To show the effectiveness, the proposed robust MPC algorithms are applied to a continuous‐time stirred tank reactor (CSTR) process.     

Plant-wide hierarchical optimization and control of an industrial hydrocracking process

Hydrocracking is a crucial refinery process in which heavy hydrocarbons are converted to more valuable, low-molecular weight products. Hydrocracking plants operate with large throughputs and varying feedstocks. In addition the product specifications change due to varying economic and market conditions. In such a dynamic operating environment, the potential gains of real-time optimization (RTO) and control are quite high. At the same time, real-time optimization of hydrocracking plants is a challenging task. A complex network of reactions, which are difficult to characterize, takes place in the hydrocracker. The reactor effluent affects the operation of the fractionator downstream and the properties of the final products. In this paper, a lumped first-principles reactor model and an empirical fractionation model are used to predict the product distribution and properties on-line. Both models have been built and validated using industrial data. A cascaded model predictive control (MPC) structure is developed in order to operate both the reactor and fractionation column at maximum profit. In this cascade structure, reactor and fractionation units are controlled by local decentralized MPC controllers whose set-points are manipulated by a supervisory MPC controller. The coordinating action of the supervisory MPC controller accomplishes the transition between different optimum operating conditions and helps to reject disturbances without violating any constraints. Simulations illustrate the applicability of the proposed method on the industrial process.     

Multi-input single-output control for extending the operating range: Generalized split range control using the baton strategy

Split range control is used to extend the steady-state operating range for a single output (controlled variable) by using multiple inputs (manipulated variables). The standard implementation of split range control uses a single controller with a split range block, but this approach has limitations when it comes to tuning. In this paper, we introduce a generalized split range control structure that overcomes these limitations by using multiple independent controllers with the same setpoint. Undesired switching between the controllers is avoided by using a baton strategy where only one controller is active at a time. As an alternative solution we consider model predictive control (MPC), but it requires a detailed dynamic model and does not allow for using only one input at a time.     

基于参数模糊自整定PID温度控制系统

针对PVC聚合釜釜内温度控制系统的大时滞、非线性等特点和目前采用釜内温度串级控制系统的不足,提出一种采用基于模糊控制原理的PID参数自整定控制方案,实现PID参数依据不同的生产负荷进行自我调整。实践证明,提高了控制器的动态响应性能和控制器的控制精度。     

Control of polystyrene batch reactors using neural network based model predictive control (NNMPC): An experimental investigation

Controlling batch polymerization reactors imposes great operational difficulties due to the complex reaction kinetics, inherent process nonlinearities and the continuous demand for running these reactors at varying operating conditions needed to produce different polymer grades. Model predictive control (MPC) has become the leading technology of advanced nonlinear control adopted for such chemical process industries. The usual practice for operating polymerization reactors is to optimize the reactor temperature profile since the end use properties of the product polymer depend highly on temperature. This is because the end use properties of the product polymer depend highly on temperature. The reactor is then run to track the optimized temperature set-point profile. In this work, a neural network-model predictive control (NN-MPC) algorithm was implemented to control the temperature of a polystyrene (PS) batch reactors and the controller set-point tracking and load rejection performance was investigated. In this approach, a neural network model is trained to predict the future process response over the specified horizon. The predictions are passed to a numerical optimization routine which attempts to minimize a specified cost function to calculate a suitable control signal at each sample instant. The performance results of the NN-MPC were compared with a conventional PID controller. Based on the experimental results, it is concluded that the NN-MPC performance is superior to the conventional PID controller especially during process startup. The NN-MPC resulted in smoother controller moves and less variability.     

一种染色机温度控制器的设计

以染色过程的温度跟踪控制系统为背景,针对温度对象动态范围宽,其动态特性随温度变化且存在结构变化,常规PID控制不能适应的特点,设计了一种参数可变的PID控制器。在详细分析染色过程温度对象动态特性的基础上,根据染色过程的实际温度和温度偏差．应用模糊推理实时改变控制器增益,并引入了积分分离算法,有效地克服了积分饱和问题。实际运行结果表明,该控制器能适应染色过程动态特性的变化,保证了温度控制的精度,可有效地提高染色质量。     

Model predictive control of an electric arc furnace off-gas process

This paper shows that an electric arc furnace off-gas system can provide valuable manipulated variables for feedback control which can improve furnace efficiency and contribute to safety in the workplace. Model predictive control (MPC) is used to illustrate this concept using practically motivated control objectives. An initial verification of a non-linear furnace model with plant data is shown. The design of MPC controllers for the furnace is discussed and results are shown by way of simulation. Evaluation of the final controller against traditional manual operation is done, and the setpoint tracking capability of the controller is tested.     

Sensor validation and process fault diagnosis for FCC units under MPC feedback

Model predictive control (MPC) has been widely applied in industry, especially in the refining industry. As all feedback controllers require correct sensor measurements, unreliable sensors can cause the MPC controller to move the process in an erroneous manner. Data validation of sensor measurements is a prerequisite in applying advanced control, particularly multivariable control which depends on many sensors. However, little research work is available on how feedback controllers like MPC complicate the task of sensor validation and process fault diagnosis. In theory, a controller can transfer the effect of a sensor fault in a controlled variable to the manipulated variables. In this paper, principal component analysis (PCA) is applied to detect, identify and reconstruct faulty sensors in a simulated FCC unit. A base PCA model is generated by perturbing the process throughout the operating region. Performance of MPC with and without data validation is compared. The same base PCA model is applied to detect and identify dynamic process faults. We demonstrate that process faults can be detected and diagnosed at an early stage.     

Adaptive hierarchical tuning of fuzzy controllers

Fuzzy controller design includes both linear and non-linear dynamic analysis. The knowledge base parameters associated within the fuzzy rule base influence the non-linear control dynamics while the linear parameters associated within the fuzzy output signal influence the overall control dynamics. For distinct identification of tuning levels, an equivalent linear controller output and a normalized non-linear controller output are defined. A linear proportional-integral-derivative (PID) controller analogy is used for determining the linear tuning parameters. Non-linear tuning is derived from the locally defined control properties in the non-linear fuzzy output. The non-linearity in the fuzzy output is then represented in a graphical form for achieving the necessary non-linear tuning. Three different tuning strategies are evaluated. The first strategy uses a genetic algorithm to simultaneously tune both linear and non-linear parameters. In the second strategy the non-linear parameters are initially selected on the basis of some desired non-linear control characteristics and the linear tuning is then performed using a trial and error approach. In the third method the linear tuning is initially performed off-line using an existing linear PID law and an adaptive non-linear tuning is then performed online in a hierarchical fashion. The control performance of each design is compared against its corresponding linear PID system. The controllers based on the first two design methods show superior performance when they are implemented on the estimated process system. However, in the presence of process uncertainties and external disturbances these controllers fail to perform any better than linear controllers. In the hierarchical control architecture, the non-linear fuzzy control method adapts to process uncertainties and disturbances to produce superior performance.     

Model predictive control of a fluidized bed dryer with an inline NIR as moisture sensor

Fluid bed drying and near infrared (NIR) spectroscopy are technologies widely used to dry and measure moisture content and other pharmaceutical granular materials’ attributes, respectively. This work focused on controlling a bench top fluid bed dryer using an industrial control system, the model predictive control (MPC) strategy, and NIR measurements of the moisture content of pharmaceutical powders. The MPC was implemented to reach the desired drying end-point while simultaneously manipulating two variables: airflow and inlet air temperature. These two manipulated variables were constrained based on the physical and chemical behavior of the process. The results showed that the use of the MPC with the inline NIR produced an adequate control performance and resulted at the same time in a reduction in energy consumption of as much as 60% in one case when compared with the current industrial practices.     

遗传算法优化的Fuzzy+变论域自适应Fuzzy-PID复合控制策略研究

提出了一种用遗传算法优化的Fuzzy+变论域Fuzzy-PID复合控制器的新方法。该控制器由Fuzzy控制和变论域Fuzzy-PID控制两部分组成。在系统的动态阶段,采用Fuzzy控制使其具有最优的动态性能;当系统进入稳态阶段,采用变论域自适应Fuzzy-PID控制使其具有最优的稳态性能。用遗传算法离线搜索出一组最优的PID参数作为在线调节的初始值,在在线部分,以离线搜索出的PID参数为基础,通过变论域的模糊推理在线调整系统瞬态响应的PID参数,使系统具有良好的自适应能力。 采用加权平滑切换的方式,保证两种不同控制过渡的平稳性。将提出的复合控制策略应用于变风量空调系统的室温串级控制中,计算机仿真结果表明,该方法使系统具有良好的动、稳态性能,抗干扰性和鲁棒性好。     

Comparison of PI and MPC for control of a gas recovery unit

This study compares PI and MPC controls via a computer simulation for a gas recovery unit (GRU), which consists of three distillation columns operated in series: a de-ethanizer, a depropanizer and a debutanizer. In addition, the de-ethanizer feed is preheated by the bottoms product from the de-ethanizer, which causes additional process coupling. Rigorous models are developed for the columns including column pressure dynamics and heat transfer dynamics. The process is a highly coupled system and has interactive constraints that determine the feasible operating regions. A decentralized PI control system with override controls for the constraints was designed and implemented on the GRU simulator and was compared with an industrial MPC controller. The MPC controller was observed to outperform the decentralized control system due to its multivariable constraint control capability. Since the simulator is available to other university researchers, it can serve as a challenge problem for multivariable control and identification. Three MPC controllers with different strategies for controlling the bottom level of the first column were implemented on the GRU process. The first MPC controller does not directly control the level, the second one moves the setpoint to the PI level controller, and the third one controls the level directly by manipulating the flow. The results show that including level into the MPC controller improves composition control for cases in which the manipulated variable for the level control has a significant impact on compositions.     

变风量空调系统中的室温模糊自适应控制

本文提出了变风量空调系统中室温控制方案。针对控制对象的大惯性、大时延特点,采用了串级控制策略;针对对象的非线性和不确定性,主控器采用了一种新的模糊自整定PID参数的方式,经仿真验证,该主控器具有良好的动、静态性能,特别是在鲁棒性方面大大优于常规PID控制器。     

电厂化学水处理控制系统中 PLC 控制系统设计与预测控制研究

采用 PLC 与工业控制计算机共同构成控制系统的方法,解决了化学水处理控制系统自动化程度低的问题.实际运行表明,化学水处理过程可通过 PLC 控制系统进行有效的自动监控.针对再生后出水中磷酸盐含量过高问题,本文首先在 Matlab/simulink 环境下,对化学水处理系统进行建模仿真,其次本文采用模型预测控制算法,对出水磷酸盐浓度进行控制,并与传统 PID 算法进行对比,仿真结果证明模型预测控制(Model Predictive Control,MPC)算法控制效果优于传统 PID,使出水磷酸盐达到了工艺出水水质要求.     

基于神经网络的聚氯乙烯汽提过程自适应解耦控制

聚氯乙烯汽提过程具有高度非线性和时变性等特点,是一类复杂的非线性工业过程.首先基于动态模糊神经网络建立了数据驱动的聚氯乙烯树脂（PVC）汽提过程的被控对象模型;然后采用一种神经网络分散式解耦控制器对汽提过程进行解耦,得到浆料流量-塔顶温度和蒸汽流量-塔底温度两个单变量系统;最后采用BP神经网络PID控制器对系统进行控制.仿真实验结果验证了所提出集成控制策略的有效性.     

一种基于DMC的新型预测PID控制器及其整定(英文)

本文提出一种基于动态矩阵控制(DMC)算法预测特性的新型PID控制方法.在考虑将来的输出期望偏差罚函数最小的前提下,由DMC计算出控制变量的值.继而构造基于DMC的预估器用以预测将来时刻的系统输出.根据将来时刻的多步预测偏差,PID控制器产生当前时刻的实际控制增量.文中也给出了基于DMC的预估器及PID控制器的参数整定方法.仿真结果表明,与常规的PID控制和DMC控制相比,所提方法具有良好的控制性能,扰动抑制尤其优良.     

基于RBFNN的非线性多变量系统的控制策略研究

针对非线性多变量大时滞系统,研究了一种基于神经网络的智能控制策略。它以经典的PID控制为基础,通过神经网络参数整定,用于多变量系统的解耦控制;用预测模型超前预测系统输出,以克服系统的时滞。该文给出了网络的结构和算法,对一组二变量强耦合时变系统进行了仿真。仿真结果表明,控制器能根据系统运行状态获得对应于某种最优控制律下的PID参数,解耦后的系统具有较好的动态和静态性能。该方法具有控制精度高、响应速度快的优点,并具有较强的自适应性和鲁棒性。     

FCMAC在蒸汽发生器水位控制中的仿真研究

蒸汽发生器是一个高度复杂的非线性时变系统,在其工况变化时具有逆动力学效应,使得蒸汽发生器的水位控制变得复杂.而其工作性能直接影响反应堆的安全运行.采用传统PID控制方案难以达到控制要求.针对蒸汽发生器的水位控制提出一种基于模糊小脑模型连接控制(FCMAC)与PID联合的控制方案.方案中,将传统的CMAC神经网络和模糊控制原理相结合,形成的FCMAC神经网络实现前馈控制,实现被控对象的逆动态模型;PID实现反馈控制,保证系统稳定性,且抑制扰动.仿真结果表明,该方案具有响应快,超调量小,较强抑制干扰能力等良好性能.     

Real-time control of a water-gas shift reactor by a model-based fuzzy gain scheduling technique

A model-based fuzzy gain scheduling technique is proposed. Fuzzy gain scheduling is a form of variable gain scheduling which involves implementing several linear controllers over a partitioned process space. A higher-level rule-based controller determines which local controller is executed. Unlike conventional gain scheduling, a controller with fuzzy gain scheduling uses fuzzy logic to dynamically interpolate controller parameters near region boundaries based on known local controller parameters. Model-based fuzzy gain scheduling (MFGS) was applied to PID controllers to control a laboratory-scale water-gas shift reactor. The experimental results were compared with those obtained by PID with standard fuzzy gain scheduling, PID with conventional gain scheduling, simple PID and a nonlinear model predictive control (NMPC) strategy. The MFGS technique performed comparably to the NMPC method. It exhibited excellent control behaviour over the desired operating space, which spanned a wide temperature range. The other three PID-based techniques were adequate only within a limited range of the same operating space. Due to the simple algorithm involved, the MFGS technique provides a low cost alternative to other computationally intensive control algorithms such as NMPC.     

非线性系统的蚁群优化预测PID控制

针对非线性、时变及大惯性系统的控制问题,提出了一种基于蚁群算法的预测PID控制算法。该算法以神经网络作为预测模型,将预测控制和PID控制相结合,并用蚁群算法在线优化控制器参数,其中以常规的Ziegler-N ichols方法整定的控制器参数为基础,选取蚁群优化变量的动态搜索区间。该算法考虑了控制能量受限情况下,非线性系统的预测控制问题。计算机仿真结果表明,该非线性控制方案具有较好的鲁棒性,相对传统PID控制策略还表现出了良好的动态性能,能够满足对再热汽温对象的控制要求。