时变扰动系统的广义最小方差控制性能评估

针对存在时变扰动的控制系统,提出一种基于广义最小方差的控制性能评估方法。首先,使控制系统的实际输出信号在突变扰动下保持稳定;然后以控制系统广义输出信号方差最小为优化目标,设计得到一个基准控制器;再以该控制器作用下的广义输出信号方差为基准,以此评估时变扰动系统的控制性能。仿真结果验证了该方法的有效性。     

基于加权偏离度统计方法的预测控制性能评估算法

针对带区域约束条件的预测控制系统性能评估问题,在考虑过程输出变量约束类型的基础上,提出了基于加权偏离度统计方法的控制性能评估算法。该方法依据控制要求的不同,将输出变量分为质量变量和约束变量,并结合工程经验合理选择变量的权重。基于系统闭环运行数据和约束设置,通过计算变量的加权偏离度得到控制系统的性能评估指标,从而为预测控制器的参数调整和性能提升提供了决策依据。系统仿真实例和工程应用证明了该评估算法对区域预测控制系统性能评估的有效性。     

基于状态预测的塑料薄膜张力反馈控制器设计

塑料薄膜工业生产过程中,张力控制系统存在明显的非线性和滞后性,严重影响了塑料薄膜产品的质量。为了提高塑料薄膜张力控制系统的控制精度以及动态性能,基于神经网络对系统进行状态预测,并利用预测的系统状态设计了反馈控制器。通过神经网络在线辨识动态非线性模型,构建了神经网络动态辨识器;运用泰勒级数展开法计算预测未来时刻的神经网络权值,并建立状态预测器;根据预测状态设计了塑料薄膜张力系统的反馈控制器;通过对比仿真实验验证了所设计反馈控制器能够明显改善塑料薄膜张力控制系统的控制精度及控制性能。     

微管挤出中精密注气系统模糊PID控制的设计与研究

建立了适用于微管挤出的精密注气控制系统,设计模糊PID控制器以实现对PID参数的在线调整,确定其模糊控制规则并在MATLAB环境下计算出模糊控制查询表,设计实现模糊PID控制的PLC程序.经仿真分析证明此模糊PID控制系统的性能明显优于常规PID控制,能够很好地实现此类复杂系统的精密控制.     

模型预测控制的应用研究

针对双容水箱液位控制系统单输入、单输出、时变、非线性、耦合和滞后的特征，以双容水箱液位为被控对象，设计基于状态方程模型的MPC预测控制器，对水箱液位进行控制。仿真结果表明：该控制器可以满足模型多输入、多输出、精确控制、抗干扰、耦合和非线性特征的控制需求，比传统PID控制器有更明显的优势。     

基于多属性性能评估的焦炉加热燃烧过程在线优化控制方法

针对焦炉加热燃烧过程中控制器参数难以适应由加热煤气热值和结焦时间变化等因素引起的火道温度波动的问题, 设计了一种基于多属性性能评估的焦炉加热燃烧过程优化控制方法。首先通过分析焦炉加热燃烧过程的工艺特点及生产需求, 针对过程参数周期差异较大的特点, 提出了基于信息熵的多属性性能评估模型, 实现控制系统的在线性能评估。针对控制系统性能评估不合格的情况, 建立了以火道温度偏差、偏差变化率和调节时间为目标的多目标优化模型, 并采用差分进化算法进行求解, 通过控制器参数的在线调节, 保证焦炉火道温度的稳定。仿真结果表明该优化控制方法在加热煤气热值和结焦时间变化时能较好地抑制火道温度的波动。     

风力发电机组模糊控制器的设计与仿真

风力发电机组是复杂的多变量非线性系统,受参数变化和外部干扰严重,具有强耦合与强时变性,针对此特点本文设计了模糊控制器,建立了风力发电系统仿真模型,使得在额定风速以下时,根据风速变化,控制电机转速获得最佳叶尖速比,实现跟踪最大功率的目标;在额定风速以上时,控制风轮的桨距角以调节风能利用系数,实现风力发电机组的恒功率输出。通过Matlab仿真,对所设计的模糊控制器进行了仿真研究,结果表明:模糊控制器能够显著提高风力发电系统的稳定性和鲁棒性,具有良好的性能和控制效果。     

俄罗斯3KT-15电铲控制系统改造方案设计

俄罗斯3KT-15系列电铲的控制系统主要是以继电器为主要元件的顺序控制器,此控制系统组成时需要大量的机械触点,本身的可靠性不高。本文对其原有的控制系统设计了一种改造方案,建立以可编程序控制器(PLC)为控制核心的新型控制系统。改造后的控制系统将在保持原有系统所有功能的基础上增加检测和报警功能,同时还减少了部分硬件电路,使系统的可靠性提高,系统维护量减小。     

多变量预测控制在锅炉燃烧系统中的应用

针对锅炉燃烧系统多变量、强耦合、强干扰、大滞后的复杂特性,采用基于状态空间模型的预测控制算法设计多输入多输出预测控制器,以一个控制目标为主,同时协调处理多个控制目标的优化方法,很好地实现了锅炉燃烧控制的三大主要任务,从而为这一复杂系统的过程控制提供了一种新的思路;文中还详细研究了预测控制器关键参数对控制性能的影响规律,据此优选参数,可以获得很好的控制效果.     

基于神经网络的电子节气门系统模型参考自适应控制

使用多层感知器神经网络模型来识别和控制非线性电子节气门系统。首先,神经网络模型在不同运行条件下辨识,它代表非线性节气门伺服系统的动态特性。其次,使用油门辨识器网络模型来设计和训练神经网络控制器模型,从而使节气门系统的追踪控制位置遵循参考模型。油门辨识器网络模型用于辅助以离线模式训练的神经网络控制器。神经网络控制器使用相同的输入来进行训练,这些输入被反馈到实际的节气门系统以产生相同的输出。通过调整神经网络控制器的权重和偏差参数,使用自适应算法来减小输出之间的差异。对使用神经网络控制器的节气门控制系统的跟踪控制性能与使用经典自适应PID控制器进行比较。仿真结果表明:采用神经网络控制器可实现跟踪控制,满足控制性能的所有需求。     

基于模型预测残差闭环潜能指标的MPC控制器实时性能监控

由于模型预测控制器对模型失配等不确定因素具有较强的鲁棒性,因此现有的多步预测误差方法不能及时显著地检测到由模型失配导致的MPC控制器性能潜能的变化。针对上述问题,提出一种改进的多步预测误差方法和实时性能监控策略。考虑到MPC控制器的模型预测残差能有效反映模型失配等信息,利用预测残差对现有多步预测误差方法进行改进,改进的方法能够更好地检测由模型失配引起的MPC控制器性能潜能的改变。在连续搅拌槽加热器(continuous stirred tank heater,CSTH)系统上的仿真实验验证了该方法的可行性与有效性。     

Enhanced Performance Assessment of Subspace Model‐Based Predictive Controller with Parameters Tuning

This study focuses on performance assessment of model predictive control. An MPC‐achievable benchmark for the unconstrained case is proposed based on closed‐loop subspace identification. Two performance measures can be constructed to evaluate the potential benefit to update the new identified model. Potential benefit by tuning the parameter can be found from trade‐off curves. Effect of constraints imposed on process variables can be evaluated by the installed controller benchmark. The MPC‐achievable benchmark for the constrained case can be estimated via closed‐loop simulation provided that constraints are known. Simulation of an industrial example was done using the proposed method.     

一种对预测控制系统经济性能评估的实用方法及其工业应用

In this article, an approach for economic performance assessment of model predictive control (MPC) system is presented. The method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian (LQG) benchmark other than conventional minimum variance control (MVC) to estimate the potential of reduction in variance. The LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance, and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability reduction. Combining the LQG benchmark directly with benefit potential of MPC control system, both the economic benefit and the op-timal operation condition can be obtained by solving the economic optimization problem. The proposed algorithm is illustrated by simulated example as well as application to economic performance assessment of an industrial model predictive control system.     

基于改进DOB的矿物研磨过程最优运行先进反馈控制(英文)

Advanced feedback control for optimal operation of mineral grinding process is usually based on the model predictive control (MPC) dynamic optimization. Since the MPC does not handle disturbances directly by controller design, it cannot achieve satisfactory effects in controlling complex grinding processes in the presence of strong disturbances and large uncertainties. In this paper, an improved disturbance observer (DOB) based MPC advanced feedback control is proposed to control the multivariable grinding operation. The improved DOB is based on the optimal achievable H 2 performance and can deal with disturbance observation for the nonminimum-phase delay systems. In this DOB-MPC advanced feedback control, the higher-level optimizer computes the optimal operation points by maximize the profit function and passes them to the MPC level. The MPC acts as a presetting controller and is employed to generate proper pre-setpoint for the lower-level basic feedback control system. The DOB acts as a compensator and improves the operation performance by dynamically compensating the setpoints for the basic control system according to the observed various disturbances and plant uncertainties. Several simulations are performed to demonstrate the proposed control method for grinding process operation.     

Performance assessment of cascade controllers for nitrate control in a wastewater treatment process

A cascade control strategy is proposed to the benchmark simulation model 1 (BSM1) to enhance the treatment performance of nitrogen removal in a biological wastewater treatment plant. The proposed control approach consists of two control loops, a primary outer loop and a secondary inner loop. The method has two controllers of which the primary loop has a model predictive control (MPC) controller and the secondary loop has a proportional-integralderivative (PID) controller, which is a cascade MPC-PID controller. The primary MPC controller is to control the nitrate concentration in the effluent, and the secondary PID controller is to control the nitrate concentration in the final anoxic compartment. The proposed method controls the nitrate concentrations in the effluent as well as in the final anoxic reactor simultaneously to strictly satisfy the quality of the effluent as well as to remove the effects of disturbances more quickly by manipulating the external carbon dosage rate. Because the control performance assessment (CPA) technique has the features of determining the capability of the current controller and locating the best achievable performance, the other novelty of this paper is to suggest a relative closed-loop potential index (RCPI) which updates the CPA technology into a closed-loop cascade controller. The proposed method is compared with a cascade PID-PID control strategy and the original PID controller in BSM1 and an improved performance of the suggested cascade MPC-PID controller is obtained by using the CPA approach.     

Explicit model predictive control of gas–liquid separation plant via orthogonal search tree partitioning

Solutions to constrained linear model predictive control (MPC) problems can be pre-computed off-line in an explicit form as a piecewise linear (PWL) state feedback defined on a polyhedral partition of the state space. This admits implementation at high sampling frequencies in real-time systems with high reliability and low software complexity. Recently, algorithms that determine an approximate explicit PWL state feedback solution by imposing an orthogonal search tree structure on the partition, have been developed, and it has been shown that they may offer computational advantages. This paper considers the application of an approximate approach to the design of an explicit model predictive controller for a two-input two-output laboratory gas–liquid separation plant, including experimental evaluation. The approximate explicit MPC controller achieves performance close to that of the conventional MPC, but requires only a fraction of the real-time computational machinery, thus leading to fast and reliable computations.     

Bounded robust control of constrained multivariable nonlinear processes

This work focuses on control of multi-input multi-output (MIMO) nonlinear processes with uncertain dynamics and actuator constraints. A Lyapunov-based nonlinear controller design approach that accounts explicitly and simultaneously for process nonlinearities, plant-model mismatch, and input constraints, is proposed. Under the assumption that all process states are accessible for measurement, the approach leads to the explicit synthesis of bounded robust multivariable nonlinear state feedback controllers with well-characterized stability and performance properties. The controllers enforce stability and robust asymptotic reference-input tracking in the constrained uncertain closed-loop system and provide, at the same time, an explicit characterization of the region of guaranteed closed-loop stability. When full state measurements are not available, a combination of the state feedback controllers with high-gain state observes and appropriate saturation filters, is employed to synthesize bounded robust multivariable output feedback controllers that require only measurements of the outputs for practical implementation. The resulting output feedback design is shown to inherit the same closed-loop stability and performance properties of the state feedback controllers and, in addition, recover the closed-loop stability region obtained under state feedback, provided that the observer gain is sufficiently large. The developed state and output feedback controllers are applied successfully to non-isothermal chemical reactor examples with uncertainty, input constraints, and incomplete state measurements. Finally, we conclude the paper with a discussion that attempts to put in perspective the proposed Lyapunov-based control approach with respect to the nonlinear model predictive control (MPC) approach and discuss the implications of our results for the practical implementation of MPC, in control of uncertain nonlinear processes with input constraints.     

An improved LQG benchmark for MPC economic performance assessment and optimisation in process industry

Performance assessment and optimisation for MPC have attracted much research interest. As a typical performance assessment benchmark, the LQG benchmark is regressed from asymmetrical points, leading to unnecessary computation and unsatisfactory regression results. To tackle this problem, an equigrid LQG benchmark was proposed for the two‐layer MPC assessment and optimisation, and the optimal setpoint for MPC was calculated to replace the experiential one. Then the LQG benchmark for sensitivity analysis was introduced. Economic performance assessment of the control system in a delayed coking furnace shows the effectiveness of the proposed approach. © 2012 Canadian Society for Chemical Engineering     

A model predictive control package for undergraduate education

Model predictive control (MPC) techniques are extremely profitable control strategies and are well accepted in the chemical processing industry so it is important that chemical engineering graduates have a fundamental understanding of MPC. This understanding will help them make contributions in industry where these control strategies abound. Without such knowledge, graduates would not understand a major part of the control structure present in modern manufacturing systems and would have difficulty understanding how to modify and improve those chemical manufacturing systems to take advantage of new technology.In this paper we describe a new software package developed and tested by the authors for teaching undergraduates the fundamentals of MPC including its suggested application in the classroom. The package is similar to existing industrial model predictive control packages in that the same steps are required to implement a model predictive controller as follows: model identification, controller configuration, controller simulation and tuning. We describe our experience using the package to introduce MPC to an advanced undergraduate process control II class. The package has also been used to provide an MPC laboratory experience for a graduate class on industrial process control.