基于模型预测控制及智能寻优的水泥粉磨优化控制

本文提出一种基于运行状态软测量和成本软约束的多变量模型预测控制(MPC)。MPC控制与传统的专家经验控制和模糊控制相比,通过模型对系统工艺参数的预测,不断地学习更新模型,更符合水泥粉磨大时延、多工况的特性。应用中通过对水泥粉磨装置的阶跃响应实验,建立多变量预测控制模型,解决水泥粉磨系统生产过程的不确定性。在此基础上,通过增量学习和机器学习找到最优运行参数,使水泥粉磨的MPC控制一直保持在最优工况。     

线性规划实现动态优化的模型预测控制策略

为减小模型预测控制算法中动态优化部分的计算复杂度,提出了用线性规划而非二次规划解决模型预测控制动态优化方法。对单输入单输出和多输入多输出模型预测控制的情形,以控制增量、输出增量和偏移变量作为优化变量,建立线性等式约束和不等式约束,并引入线性目标函数,形成线性规划问题。通过加入多种软约束,可改善动态过程的性能指标,达到平稳控制的目的。最后通过一个实例验证了方法的有效性。     

基于二次型性能指标的不确定混沌系统最优控制器设计

研究具有不确定性并带有扰动的混沌控制系统。首先基于成熟线性理论中的二次型最优控制理论将扰动非线性的混沌系统模型转换为无扰动的等价线性系统模型,然后给出了针对伪线性系统的基于二次型性能指标的最优控制器设计方法。对Liu混沌系统的参数不确定性的仿真结果表明：控制品质有了较大的改善和提高。     

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。     

预测控制的哲学思想分析及其新架构的提出

预测控制属于控制科学,从"过去-现在-将来"的哲学视角看其产生与发展存在必然性。预测控制的性能指标是优化求解预测控制的核心,其结构决定预测控制系统的功能。由于系统优化要求,常规预测控制系统的性能指标由被控变量跟踪项与控制能量变化项组成,但仍不全面。本文从马克思主义唯物辩证法出发,由事物普遍联系的规律提出了理应在性能指标结构中引入表征操作变量与约束边界之间距离的函数,来规避由于预测控制系统与外界联系的约束性对控制造成不利的影响;从全局与局部的角度出发,提出了预测控制的多变量控制与区间控制的特性可以为预测控制的结构改进提供前提;从绝对与相对的辨证范畴进一步探讨了性能指标结构的改进结果。基于上述分析,从马克思主义认识论出发,将预测控制性能指标的改进方案与化工过程中裕量的概念相结合,提出了考虑裕量损失函数的预测控制新架构。最后,将理论结合实践,通过仿真实例验证了预测控制新架构的效果。     

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。     

循环流化床燃烧系统无终端约束字典序经济预测控制

针对循环流化床锅炉(CFBB)燃烧系统非线性、约束、多变量耦合等过程特性和多目标燃烧优化要求,提出一种无终端约束字典序经济模型预测控制策略。基于字典序多目标优化思想,将CFBB稳定燃烧工况作为最重要控制目标,将燃烧系统经济性能作为次重要目标,构建分层滚动时域优化控制问题。设计关于稳定燃烧性能指标的终端域条件,建立无显式终端约束的稳定字典序经济模型预测控制策略。这不仅降低了多目标燃烧控制器的在线计算量,同时并行实现CFBB燃烧系统的稳定控制和经济性能优化。最后通过仿真对比验证本文提出方法的有效性。     

基于一阶滞后传递函数的换热器模型预测控制

针对换热器本身具有较强的纯滞性、约束性等特性,提出将模型预测控制应用于换热器控制中。首先将换热器近似处理为一阶惯性环节,推导出其传递函数作为预测模型;而后以输出误差与控制量加权之和的二次型性能指标为目标函数对其进行滚动优化,使全过程达到最优;最后,通过反馈校正构成系统的闭环优化,提高控制的鲁棒性。通过仿真实例证明:模型预测控制能够较好地处理换热器控制上的纯滞后等问题,具有较好的鲁棒性。     

增量预测控制策略的稳定性分析

针对开环稳定的约束线性系统,提出一种增量预测控制策略的稳定性分析方法.利用Lipschitz条件、弱能控性和稳态问题强对偶性假设,将二次型性能指标变换为新性能指标,建立闭环系统的Lyapunov函数,从而得到增量预测控制策略的闭环稳定性结论.最后,采用数值仿真验证了该方法的有效性.     

受扰动非线性系统的反馈线性化最优控制

研究具有外界扰动作用下的非线性系统基于状态反馈精确线性化的最优控制器设计问题。首先基于微分同胚将受扰动非线性系统模型转变为无扰动的伪线性系统模型,然后给出了在关系度等于系统阶数情况下基于二次型性能指标的最优控制器设计方法,通过求解Riccati方程得到系统最优扰动抑制控制律。最后通过仿真实例表明了该方法的有效性。     

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.     

On improving accuracy of computationally efficient nonlinear predictive control based on neural models

For nonlinear processes the classical model predictive control (MPC) algorithm, in which a linear model is used, usually does not give satisfactory closed-loop performance. In such nonlinear cases a suboptimal MPC strategy is typically used in which the nonlinear model is successively linearised on-line for the current operating point and, thanks to linearisation, the control policy is calculated from a quadratic programming problem. Although the suboptimal MPC algorithm frequently gives good results, for some nonlinear processes it would be beneficial to further improve control accuracy. This paper details a computationally efficient nonlinear MPC algorithm in which a neural model is linearised on-line along the predicted trajectory in an iterative way. The algorithm needs solving on-line only a series of quadratic programming problems. Advantages of the discussed algorithm are demonstrated in the control system of a high-purity ethylene–ethane distillation column for which the classical linear MPC algorithm does not work and the classical suboptimal MPC algorithm is slow. It is shown that the discussed algorithm can give practically the same control accuracy as the algorithm with on-line nonlinear optimisation and, at the same time, the algorithm is significantly less computationally demanding.     

Reference Trajectory Optimization Under Constrained Predictive Control

Chemical process systems often need to respond to frequently changing product demands. This motivates the determination of optimal transitions, subject to specification and operational constraints. However, direct implementation of optimal input trajectories would, in general, result in offset in the presence of disturbances and plant/model mismatch. This paper considers reference trajectory optimization of processes controlled by constrained model predictive control (MPC). Consideration of the closed‐loop dynamics of the MPC‐controlled process in the reference trajectory optimization results in a multi‐level optimization problem. A solution strategy is applied in which the MPC quadratic programming subproblems are replaced by their Karush‐Kuhn‐Tucker optimality conditions, resulting in a single‐level mathematical program with complementarity constraints (MPCC). The performance of the method is illustrated through application to two case studies, the second of which considers economically optimal grade transitions in a polymerization process.     

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional (2D) systemtheory, an integrated predictive iterative learning control (2D-IPILC) strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control (ILC) andmodel predictive control(MPC) is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By minimizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type (Ptype) ILC despite the model error and disturbances.     

Optimizing process economics online using model predictive control

Optimizing process economics in model predictive control traditionally has been done using a two-step approach in which the economic objectives are first converted to steady-state operating points, and then the dynamic regulation is designed to track these setpoints. Recent research has shown that process economics can be optimized directly in the dynamic control problem, which can take advantage of potential higher profit transients to give superior economic performance. However, in practice, solution of such nonlinear MPC dynamic control problems can be challenging due to the nonlinearity of the model and/or nonconvexity of the economic cost function. In this work we propose the use of direct methods to formulate the nonlinear control problem as a large-scale NLP, and then solve it using an interior point nonlinear solver in conjunction with automatic differentiation. Two case studies demonstrate the computational performance of this approach along with the economic performance of economic MPC formulation.     

Dantzig–Wolfe decomposition and plant-wide MPC coordination

Due to the enormous success of model predictive control (MPC) in industrial practice, the efforts to extend its application from unit-wide to plant-wide control are becoming more widespread. In general, industrial practice has tended toward a decentralized MPC architecture. Most existing MPC systems work independently of other MPC systems installed within the plant and pursue a unit/local optimal operation. Thus, a margin for plant-wide performance improvement may be available beyond what decentralized MPC can offer. Coordinating decentralized, autonomous MPC has been identified as a practical approach to improving plant-wide performance. In this work, we propose a framework for designing a coordination system for decentralized MPC which requires only minor modification to the current MPC layer. This work studies the feasibility of applying Dantzig–Wolfe decomposition to provide an on-line solution for coordinating decentralized MPC. The proposed coordinated, decentralized MPC system retains the reliability and maintainability of current distributed MPC schemes. An empirical study of the computational complexity is used to illustrate the efficiency of coordination and provide some guidelines for the application of the proposed coordination strategy. Finally, two case studies are performed to show the ease of implementation of the coordinated, decentralized MPC scheme and the resultant improvement in the plant-wide performance of the decentralized control system.     

化工过程预测控制的在线优化实现机制

多层结构的预测控制已逐渐成为工业过程控制领域的主流控制方案。在此控制架构基础上,根据操作工或工艺优化所给定期望值的不同,将稳态优化问题具体化为两种基本情况,并对此提出基于复合目标函数的优化问题,可针对不同过程要求退化为线性、二次或二者兼有的优化问题形式。为保证最优目标的可行性并在一定程度上避免关键变量饱和,对不可行的期望值适当调整。将所得最优目标增量化处理后送入模型预测控制动态控制层,确保了上下层之间变量传递的一致性。包含约束的全混槽反应器系统仿真实例表明,流程的优化实现层可针对不同的过程要求有效给出最优目标以便动态控制,说明了该优化流程的可行性。     

Energy efficient model predictive building temperature control

Many systems used in buildings for heating, ventilating, and air-conditioning waste energy because of the way they are operated or controlled. This paper explores the application of model predictive control (MPC) to air-conditioning units and demonstrates that the closed-loop performance and energy efficiency can be improved over conventional approaches. This work focuses on the problem of controlling the vapor compression cycle (VCC) in an air-conditioning system, containing refrigerant which is used to provide cooling. The VCC considered in this work has two manipulated variables that affect operation: compressor speed and the position of an electronic expansion valve. The system is subject to constraints, such as the range of permissible superheat, and also needs to regulate temperature variables to set points. An MPC strategy is developed for this type of system based on linear models identified from data obtained from a first-principles model of the VCC. The MPC strategy incorporates economic measures in the objective function as well as control objectives. Tests are carried out on a simulated VCC system that is linked to a simulation of a realistic building that is developed in the U.S. Department of Energy Computer Simulation Program, EnergyPlus. The MPC demonstrated significantly better tracking control relative to conventional approaches (a reduction of 70% in terms of the integral of squared error for step changes in the temperature set-point), while reducing the VCC energy requirements by 16%. The paper describes the control approach in detail and presents results from the tests.     

Model predictive control: past,present and future

More than 15 years after model predictive control (MPC) appeared in industry as an effective means to deal with multivariable constrained control problems, a theoretical basis for this technique has started to emerge. The issues of feasibility of the on-line optimization, stability and performance are largely understood for systems described by linear models. Much progress has been made on these issues for non-linear systems but for practical applications many questions remain, including the reliability and efficiency of the on-line computation scheme. To deal with model uncertainty ‘rigorously’ an involved dynamic programming problem must be solved. The approximation techniques proposed for this purpose are largely at a conceptual stage. Among the broader research needs the following areas are identified: multivariable system identification, performance monitoring and diagnostics, non-linear state estimation, and batch system control. Many practical problems like control objective prioritization and symptom-aided diagnosis can be integrated systematically and effectively into the MPC framework by expanding the problem formulation to include integer variables yielding a mixed-integer quadratic or linear program. Efficient techniques for solving these problems are becoming available.