模式切换类经济预测控制切换时间在线估计

针对一类非线性仿射系统，提出一种在线估计切换时间的经济模型预测控制算法，并将其拓展到长周期控制过程中。有限时间内，将切换时间作为变量实时更新估计，确定最优的切换操作点，以保证每一时刻都可以在控制目标可达的前提下经济性能最优，避免了传统切换经济预测控制策略可能出现的控制目标不可达或经济性能较差的情况。进一步，将该策略作为单周期应用到长周期优化控制过程中，当系统受到扰动时，开始一个新的优化控制周期，实现优化模式与控制模式的灵活切换，同时可以及时应对扰动的出现。该策略保证系统的综合性能最优，仿真结果证明了方法的有效性。     

基于Petri网的电加热炉温度控制系统

针对工业生产中常规的电加热炉温度控制的强非线性及大惯性等特性,应用混杂控制系统的思想,设计了基于Petri网的电加热炉温度控制系统.利用Petri网的监控功能,提出了基于控制Petri网的混杂控制方法,进而给出基于控制Petri网的过程混杂控制器的设计,最终实现电加热炉温度控制的快速性和稳定性.     

基于关联规则的条件状态模糊Petri网及其在故障诊断中的应用

模糊Petri网作为一种知识表达模型,能够用于工业过程系统故障推理和诊断。然而,模糊Petri网的建立大多需要先验知识,为此限制了其广泛应用。为了能够有效利用工业生产过程数据,提出了一种基于关联规则的条件状态模糊Petri网,并将其用于工业过程故障推理与诊断。采用数据挖掘的关联规则方法提取模糊Petri网的模糊规则及置信度,通过变量间的关联分析,将影响置信度的关键主元（条件量）提取出来,建立条件状态模糊Petri网;基于极大代数的迭代算法进行动态置信度逆向推理,可以获得工业过程的故障发生概率。该方法实现了故障诊断网络的数据驱动,从而提高故障诊断的快速性与准确性,某化学反应研究表明所提方法的有效性。     

基于关联规则的条件状态模糊Petri网及其在故障诊断中的应用

模糊Petri网作为一种知识表达模型,能够用于工业过程系统故障推理和诊断。然而,模糊Petri网的建立大多需要先验知识,为此限制了其广泛应用。为了能够有效利用工业生产过程数据,提出了一种基于关联规则的条件状态模糊Petri网,并将其用于工业过程故障推理与诊断。采用数据挖掘的关联规则方法提取模糊Petri网的模糊规则及置信度,通过变量间的关联分析,将影响置信度的关键主元(条件量)提取出来,建立条件状态模糊Petri网;基于极大代数的迭代算法进行动态置信度逆向推理,可以获得工业过程的故障发生概率。该方法实现了故障诊断网络的数据驱动,从而提高故障诊断的快速性与准确性,某化学反应研究表明所提方法的有效性。     

基于受控混杂Petri网的事件逻辑驱动的炼油厂动态调度建模方法

将受控混杂Petri网同事件逻辑网和数学规划相结合提出了一种适用于炼油等流程工业生产调度的动态建模方法.作为事件逻辑网和数学规划模型之间连接的桥梁,受控混杂Petri网将离散变量和连续变量统一在一个模型中.用受控混杂Petri网建立整个生产流程的仿真模型,事件逻辑网响应生产过程中的突发事件并结合经验规则做出逻辑判断,产生新的调度指令.生产过程重新调整后,上层数学规划模型进行相应更新,产生的优化结果作为设备新的运行参数.该模型既可以响应突发事件,又能及时进行优化,具有更好的动态优化特性.     

化工过程非稳态开工的缓冲升温修正切换控制

化工过程的开工大多表现为一个升温过程。为满足升温过程的快速性而又不失稳定性的要求,有学者提出将时间最优Bang-Bang控制与其他控制方法结合来控制升温过程,但由于Bang-Bang控制对切换次数和切换点要求严格,致使其在实际应用中不够理想。在Bang-Bang组合控制的基础上引入缓冲升温控制,将整个控制系统分为4部分:全幅升温、全幅降温、缓冲升温、PID控制。将温度变化率作为缓冲升温与PID控制的切换变量,将修正切换控制问题等价为非线性规划问题,优化选取全幅升温、全幅降温、缓冲升温最优切换时间点。实例对比分析表明,该切换开工控制方案不仅避免了因切换次数与切换点选取不当导致的不良问题,且超调小,稳定快。     

基于高斯混合模型与主元分析的多模型切换方法

针对多模型预测控制的模型切换问题,提出了一种基于工况判断的多模型切换方法,利用工业过程中的可测变量综合反映系统的动态特性,根据动态特性的变化进行多模型切换。首先利用高斯混合模型(GMM)将历史数据划分为若干个工况,然后利用不同工况下的历史数据建立负荷向量矩阵和预测模型,最后根据主元模型的平方预报误差(SPE)选择预测模型。以乙烯裂解炉的反应管出口温度(COT)的控制为例进行仿真,仿真结果表明:提出的方法实现了多个反应管出口温度的稳定均衡控制,当系统的工况发生改变时,通过不同主元模型的SPE统计量的比较,可以很容易地找到匹配的工况,并切换为相应的预测模型,解决了当系统动态特性发生改变时,预测模型切换滞后的问题。     

DMF回收精馏塔开车过程控制任务的Petri网建模及分析

以DMF回收的开车过程精馏塔为背景,分析了精馏塔开车过程这个混杂系统的行为特性和建模方法.在此基础上,基于控制任务对精馏塔开车过程进行划分,并使用赋时Petri网对划分后的精馏塔开车过程建模,并进行仿真分析.运用基于控制任务的建模方法有利于编程的实现,使开车过程中的操作更加具有目的性.     

化工过程切换控制研究中的连续特性

考虑化工切换控制切换点处的连续特性,提出新的化工切换控制仿真算法。通过与传统切换控制算法的对比和实例仿真分析发现,化工切换控制仿真算法下的切换控制曲线在切换点处缓和、平滑,仿真曲线更接近实际控制系统特性,提高了切换控制仿真的精确度。     

不对称系统输入反馈预测控制及可控性分析

研究了生产过程中一类特殊的非线性系统,不对称系统的预测控制方法。在正反方向的控制作用下,这类系统表现出不对称动态特性。此类系统的理论研究特别是控制方法研究十分有限。针对基于正反方向上的线性模型设计的正反模型预测控制方法与传统预测控制方法在结构上的差异进行了分析,说明了由于这种结构上的差异可能导致的模型失配及控制效果不佳,然后采用输入反馈方法对不对称系统正反模型预测控制方法进行了修正,将实际施加给对象的控制作用通过反馈形式纳入到下一步正反方向控制律的求解中。并且在无约束的条件下分析了正反模型预测控制方法的可控性。最后通过pH值控制的仿真实验验证了此两种不对称系统正反模型预测控制方法的控制效果。     

Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes

This paper proposes a switching multi-objective model predictive control (MOMPC) algorithm for constrained nonlinear continuous-time process systems. Different cost functions to be minimized inMPC are switched to satisfy different performance criteria imposed at different sampling times. In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system, the dual-mode control method is used to design the switching MOMPC controller. In this method, a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC. The correction termis computed if the states are out of the terminal set and the free-parameters of the local control laware computed if the states are in the terminal set. The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions. Finally, implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.     

Self-adaptive predictive functional control of the temperature in an exothermic batch reactor

In this paper we study a self-adaptive predictive functional control algorithm as an approach to the control of the temperature in an exothermic batch reactor. The batch reactor is located in a pharmaceutical company in Slovenia and is used in the production of medicines. Due to mixed discrete and continuous inputs the reactor is considered as a hybrid system. The model of the reactor used for the simulation experiment is explained in the paper. Next, we assumed an exothermic chemical reaction that is carried out in the reactor core. The dynamics of the chemical reaction that comply with the Arrhenius relation have been well documented in the literature and are also summarized in the paper. In addition, the online recursive least-squares identification of the process parameters and the self-adaptive predictive functional control algorithm are thoroughly explained. We tested the proposed approach on the batch-reactor simulation example that included the exothermic chemical reaction kinetic model. The results suggest that such an implementation meets the control demands, despite the strongly exothermic nature of the chemical reaction. The reference is suitably tracked, which results in a shorter overall batch-time. In addition, there is no overshoot of the controlled variable T, which yields a higher-quality production. Finally, by introducing a suitable discrete switching logic in order to deal with the hybrid nature of the batch reactor, we were able to reduce the switching of the on/off valves to a minimum and therefore relieve the wear-out of the actuators as well as reduce the energy consumption needed for control.     

Characteristic equations and robust stability of a simplified predictive control algorithm

In predictive control, control calculations are done such that the difference between the desired and the predicted response of the process is minimized. The number of points on the prediction horizon at which the error is minimized and the number of future control moves considered affect the on-line computational effort involved in the solution of the constrained optimization problem. Earlier papers have shown that the control performance obtained using the DMC algorithm can also be obtained by using a simplified algorithm where the error is minimized at one point and one future control move is calculated. Because of its computational advantages, the simplified algorithm is analyzed further in this paper. Its transfer function is compared with the transfer function of the DMC algorithm. Characteristic equations to select tuning parameters are presented. The paper also compares the robust stability of the simplified and the DMC algorithms on SISO and MIMO process models. The results provide additional support to the viability of the simplified algorithm and thus indicate that it is possible for some processes to benefit from predictive control with only modest computational resources.     

Distributed model predictive control of switched nonlinear systems with scheduled mode transitions

A method for the design of distributed model predictive control (DMPC) systems for a class of switched nonlinear systems for which the mode transitions take place according to a prescribed switching schedule is presented. Under appropriate stabilizability assumptions on the existence of a set of feedback controllers that can stabilize the closed‐loop switched, nonlinear system, a cooperative DMPC architecture using Lyapunov‐based model predictive control (MPC) in which the distributed controllers carry out their calculations in parallel and communicate in an iterative fashion to compute their control actions is designed. The proposed DMPC design is applied to a nonlinear chemical process network with scheduled mode transitions and its performance and computational efficiency properties in comparison to a centralized MPC architecture are evaluated through simulations. © 2013 American Institute of Chemical Engineers AIChE J, 59:860‐871, 2013     

铅锌烧结过程烧穿点的模糊预测控制方法

针对具有时滞、非线性、不确定等特性的铅锌密闭鼓风烧结过程,提出一种烧穿点的模糊预测控制方法。首先,基于台车速度和烧穿点之间的分段线性化关系,建立烧穿点的模糊T-S模型。然后,采用动态线性化方法获得烧穿点的全局模型,并在此基础上进行模型预测控制器设计,以实现烧穿点的模糊预测控制。最后,采用烧结生产历史数据,对模糊预测控制方法进行了仿真和实验研究。结果表明：模糊T-S模型能有效抑制垂直燃烧速度不确定性的影响,具有较高的辨识精度,能够满足工业现场生产指导的需要;此外,烧穿点的模糊预测控制方法比单纯的模糊控制调节时间短、超调量小,能快速有效地响应烧穿点的变化,具有一定的应用价值。     

一种基于多模型切换的阶梯式广义预测控制算法

针对一类模型参数突变的系统,提出一种基于多模型切换的阶梯式广义预测控制算法。采用多个固定模型、一个常规自适应模型和一个可重新赋初值的自适应模型并行辨识系统的动态特性。多个固定模型可以提高系统的暂态性能,常规自适应模型可以保证系统的稳定性,可重新赋初值的自适应模型可以进一步提高系统的暂态性能。在每个采样时刻基于性能指标切换到最优的局部模型作为当前模型,设计阶梯式广义预测控制器,从而实现系统全局的控制。最后的仿真结果表明,其控制效果明显优于单一模型的控制器。