流程雁阵的多目标跟踪优化方法

流程雁阵(Process Goose Queue,PGQ)[1]为流程系统的分解协调优化提供了一个新的方法,然而,目前PGQ方法尚存在许多不足,如简单地将个体PGQ的状态跟踪处理为单目标优化问题,这与实际流程操作不符;而多级PGQ系统优化仍采用传统的数学规划方法,对模型要求苛刻且依赖于初值的选取。为此,论文提出了一个面向流程雁阵多目标跟踪的优化方法。首先对多级PGQ系统进行了结构优化,然后将NSGA-Ⅱ用于多级PGQ系统中个体PGQ的多目标优化求解,得到Pareto解集;在此基础上,将逼近理想解捧序法(TOPSIS)和扩展傅里叶幅值灵敏度分析法(EFAST)应用于个体PGQ的多目标决策,并从Pareto解集选取最优解在多级PGQ系统中逐级传递,实现流程系统的分解协调优化。仿真实例验证了方法的可行性和有效性。     

复杂工业过程的可变时域分散协调优化方法

针对大型工业生产过程,提出了一种基于约减状态空间分解的可变预测窗口长度预测控制算法。采用预测控制滚动优化的方法,在每个滚动窗口内,通过内外部状态分解减小优化问题求解规模,在此基础上,采用可变时间窗口长度的方式巧妙地解决了多时间尺度子系统之间的优化协调问题。以某炼铁厂的实际生产数据为基础进行了仿真实验,并与现有的三种预测控制算法进行了比较。结果表明,该方法很好地处理了计算实时性和优化性能之间的矛盾,综合性能最优。     

流程工业生产过程先进控制软件的开发与应用

简要介绍了流程工业生产过程先进控制技术与软件的发展与现状,在此基础上探讨了已取得大量成功应用的国产高级多变量鲁棒预测控制软件APC-Adcon的控制算法、设计与实现方法以及工程应用等问题。     

一种新型的预测函数PID控制算法的研究

针对应用于工业过程中的系统具有大时滞、时变性、非线性等特点,采用传统的PID控制方法难以实现良好的控制效果.将预测函数控制算法和PID控制算法相结合,提出了一种新型的预测函数PID控制算法.该算法具有预测函数控制算法鲁棒性强和PID控制算法抗干扰性好的优点.仿真结果表明,与常规预测函数控制算法相比,该控制算法满足系统对快速性和稳态精度的要求,具有良好的鲁棒性和抗干扰能力.     

流程工业的组合积分系统及其先进控制

给出了流程丁业中组合积分系统的定义,分析了其特性.基于模型预测控制的基本原理,设计了组合积分系统的先进控制算法,并成功应用于实际工业过程之中,大大提高了控制的精度和经济效益.     

广义预测控制器系数直接算法

为了简化广义预测控制算法的分析与设计，提出了广义预测控制器系数直接计算方法．该方法利用过程模型直接递推，把广义预测控制律表达成控制器系数与参考轨迹及过程历史信息乘积的形式．其控制器系数计算只与模型参数及设计参数有关，避免了在线求解Diophantine方程、输出预测表达式及自由响应项，简化了设计思路，减少了在线运算量．在一个DCS控制的非线性液位装置上得到的对比实验结果表明该方法是可行和有效的．     

并联系统分布预测控制

并联结构的过程网络控制系统存在精确建模困难、算法计算时间长的问题。如果缩短计算时间,则会导致控制精度不足,为此提出了一种分布式预测控制算法。将并联系统分解为多个子系统,每个子系统通过相互协作、迭代计算,完成整个并联系统的控制。并联系统存在独特的竞争性耦合形式。通过定义并联系统竞争性耦合结构,完成分布式预测控制算法的高效迭代,有效减少了计算量。优化过程中考虑了竞争性约束。在保证每个子系统最优的前提下,通过运行算法给出的迭代计算步骤,达到系统整体最优,实现了低成本在线实时优化与控制的目标。以燃气锅炉供暖系统为例,利用MATLAB对分布式预测控制算法进行仿真研究,并将其与集中式预测控制算法仿真结果进行对比。结果验证了该算法的有效性和实用性。     

基于递推闭环子空间辨识的自适应预测控制方法

针对存在噪声干扰与时变特性的线性系统的模型不确定性问题,提出了一种基于递推闭环子空间辨识的自适应预测控制方法.通过结合PID(proportional-integral-derivative)控制采用新的目标函数,对闭环子空间预测控制算法进行改进,推导出具有类似PID结构的闭环子空间预测控制算法;采用固定输入输出数据集大小的递推方法将改进后的算法在线实施,通过采用一种简单直观的更新方法代替LQ分解,有效提高了在线计算效率.最后,通过仿真实验验证了方法的有效性.     

广义预测控制综述

总结了近年来广义预测控制的递推算法、直接 算法、组合算法、稳定的广义预测控制算法、连续的广义预测控制算法、非线性系统的广义 预测控制算法等各方面的理论研究成果,概述了广义预测控制的稳定性和鲁棒性分析的结论 ,讨论了进一步研究的方向．     

基于状态空间模型的双时间尺度预测控制算法

多时间尺度问题在控制领域已得到广泛关注。针对多时间尺度问题的典型代表——双时间尺度问题,学者们已提出多种控制算法来分析处理。这些控制算法大多是根据奇异摄动法将控制系统分解为快、慢两个独立的子系统,并对子系统分别进行优化求解,这类算法往往忽略子系统之间控制与输出的耦合作用。本文在上述控制算法的基础上,提出一种考虑快、慢子系统双方控制与输出间的耦合作用的双时间尺度预测控制算法。该算法以系统原状态空间模型分解得到的快、慢两个子系统模型为被控对象,将两个模型的预测值信息整合到同一预测控制优化问题中,实现对整个系统的控制。仿真实例验证了该方法的有效性。     

Nonlinear predictive controller based on S-PARAFAC Volterra models applied to a communicating two-tank system

This paper proposes a new predictive controller approach for nonlinear process based on a reduced complexity homogeneous, quadratic discrete-time Volterra model called quadratic S-PARAFAC Volterra model. The proposed model is yielded by using the symmetry property of the Volterra kernels and their tensor decomposition using the PARAFAC technique that provides a parametric reduction compared to the conventional Volterra model. This property allows synthesising a new nonlinear-model-based predictive control (NMBPC). We develop the general form of a new predictor, and therefore, we propose an optimisation algorithm formulated as a quadratic programming under linear and nonlinear constraints. The performances of the proposed quadratic S-PARAFAC Volterra model and the developed NMBPC algorithm are illustrated on a numerical simulation and validated on a benchmark as a continuous stirred-tank reactor system. Moreover, the efficiency of the proposed quadratic S-PARAFAC Volterra model and the NMBPC approach are validated on an experimental communicating two-tank system.     

A Decomposed-model Predictive Functional Control Approach to Air-vehicle Pitch-angle Control

The requirements for the pitch-angle control of an air vehicle are a very fast response with as few vibrations as possible. The vibrations can damage the equipment that is carried within the body of the vehicle. The main problem to deal with is the relatively fast and under damped dynamics of the vehicle and the slow actuators and sensors. We have solved the problem by using a predictive approach. The main idea of this approach is a process output prediction based on a decomposed process model. The decomposition enables the extension of the model-based approach to processes with integrative behavior such as in the case of a rocket’s pitch-angle control. The proposed approach is not only useful in this case but it gives us a framework to design the control for a wide range of processes. We compared the predictive design methodology with the classical compensator control approach, known from aerospace system control. The advantage of the new approach is the reduced vibrations during the transient response.     

A predictive control strategy for nonlinear NOx decomposition process in thermal power plants

For the load-dependent nonlinear properties of the nitrogen oxide (NOx) decomposition process in thermal power plants, a local-linearization modeling approach based on a kind of global Nonlinear AutoRegressive Moving Average with eXogeneous input (NARMAX) model, named the exponential ARMAX (ExpARMAX) model, is presented. The ExpARMAX model has exponential-type coefficients that depend on the load of power plants and are estimated offline. In order to take advantage of existing conventional controllers and to reduce the cost of the industrial identification experiment, we propose a model structure that makes it possible for the ExpARMAX model to be identified using commercial operation data. On the basis of the model, a long-range predictive control strategy, without resorting to parameter estimation online, is investigated. The influence of some intermediate variables treated as process disturbances is studied, and the scheme using a set of multi-step-ahead predictors of the intermediate variables to improve control performance is also presented. A simulation study shows that the ExpARMAX model can give satisfactory modeling accuracy for the NOx decomposition (de-NOx) process in a large operating range, and the control algorithm proposed significantly improves the control performance.     

纯滞后过程模糊预测控制研究

针对复杂工业过程控制,提出一种基于T—S模糊模型的预测控制方法,从参考轨迹和可测的过程变量提取特征信息,并利用最优控制理论,构成了具有模糊模型的纯滞后预测控制系统。经过跟踪调节和定值干扰调节实验仿真,仿真结果表明基于T—S模糊模型的预测控制方法的有效性和可行性,系统的跟踪效果良好,调节品质优于单纯的线性调节器。     

Nonlinear system modeling and predictive control using the RBF nets-based quasi-linear ARX model

On the basis of the single-input single-output (SISO) RBF-ARX model proposed in previous works [Peng, H., et al. (2003b). Stability analysis of the RBF-ARX model based nonlinear predictive control. In Proceedings of the ECC2003; Peng, H., et al. (2003c). Modeling and control of nonlinear nitrogen oxide decomposition process. In Proceedings of the CDC’03; Peng, H., et al. (2004). RBF-ARX model based nonlinear system modeling and predictive control with application to a NO_x decomposition process. Control Engineering Practice, 12, 191–203; Peng, H., et al. (2007). Nonlinear predictive control using neural nets-based local linearization ARX model—Stability and industrial application. IEEE Transactions on Control Systems Technology, 15, 130–143] the multi-input multi-output (MIMO) RBF-ARX model and its state-space representation are derived to describe the dynamics of a class of multivariable nonlinear systems whose working-point varies with time and which may be linearized around the working-point. The proposed MIMO RBF-ARX model has a basic regression-model structure that is analogous to the linear ARX model structure, and the elements of its regression matrices are composed of Gaussian radial basis function (RBF) neural networks that are dependent on the working-point state of the current system. An off-line estimation approach to parameters and orders of the MIMO RBF-ARX model is presented, and, on the basis of the estimated MIMO RBF-ARX model, a predictive control strategy is designed to control the underlying nonlinear system. A case study on a simulator of a thermal power plant is also given to illustrate the effectiveness of the nonlinear modeling and control method proposed in this paper.     

连续碳酸化分解过程分解梯度控制策略研究

连续碳酸化分解过程是烧结法生产氧化铝的关键工序之一,其分解率梯度与末槽分解率直接影响产品的产量和质量。针对此过程具有强非线性、强耦合性、大时滞、难以建立精确的数学模型等特点,在分析过程机理特征和总结专家经验的基础上,提出了分解率梯度专家控制和末槽分解率预测前馈补偿相结合的控制策略,以首槽进料阀门和1#~5#槽CO2通气阀门为控制对象,采用产生式规则建立了由首槽进料量专家控制规则和1#~5#槽分解率专家控制规则共同组成的专家知识库,并引入了末槽分解率预测模型修正控制输出,最后开发了连续碳酸化分解过程分解率梯度专家控制系统。实际运行结果表明,各槽分解率的合格率提高了2%以上,实现了分解率梯度的稳定优化控制,提高了产品的质量。     

Predictive functional control based on fuzzy model forheat-exchanger pilot plant

In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi-Sugeno type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong nonlinear behavior. It has been shown that in the case of nonlinear processes, the approach using fuzzy predictive control gives very promising results. The proposed approach is potentially interesting in the case of batch reactors, heat-exchangers, furnaces, and all the processes that are difficult to model     

Energy management for multi-microgrid system based on model predictive control

To reduce the computation complexity of the optimization algorithm used in energy management of a multi-microgrid system, an energy optimization management method based on model predictive control is presented. The idea of decomposition and coordination is adopted to achieve the balance between power supply and user demand, and the power supply cost is minimized by coordinating surplus energy in the multi-microgrid system. The energy management model and energy optimization problem are established according to the power flow characteristics of microgrids. A dual decomposition approach is imposed to decompose the optimization problem into two parts, and a distributed predictive control algorithm based on global optimization is introduced to achieve the optimal solution by iteration and coordination. The proposed method has been verified by simulation, and simulation results show that the proposed method provides the demanded energy to consumers in real time, and improves renewable energy efficiency. In addition, the proposed algorithm has been compared with the particle swarm optimization (PSO) algorithm. The results show that compared with PSO, the proposed method has better performance, faster convergence, and significantly higher efficiency.     

Wavelets Analysis on Structural Model for Default Prediction

In recent years, to improve predictive ability of corporate defaults has become an important problem. In this paper, regarding on characteristics of listed companies, we sampled 100 companies according to industry types, constructed wavelet structural model, experimented with wavelet decomposition proceeds to get low frequency and high frequency sequence, built the prediction model for both sequences, and then using the prediction of future returns to reconstruct predictive returns, thus avoiding accumulated prediction process with earnings volatility of time series model, therefore enhanced the precision of default prediction. Finally we compared wavelet structural model with time series structural model based on the predictive default distance of China’s listed companies.     

On-line predictive model for disassembly process planning adaptation

This paper presents an on-line predictive model for disassembly process adaptation. The prediction enables a planner to adapt the process plan based on the condition of the product (e.g., degree of rustiness, deformation) during process execution. This model tries to correlate the product physical condition, used as an explanatory variable, with the component value and disassembly cost, the response variables. The core of the approach is based on an inference engine that used a kernel regression. A simple methodology for integrating the predictive planner in a disassembly system is presented and exemplified by a case study of the disassembly of a ratio.