复杂工业过程新型控制方法--混杂系统控制理论的研究

系统地评价了基于不同框架的混杂系统的建模,控制,优化,分析与设计问题,着重分析了基于混合逻辑动态系统的研究方法,指出了研究混杂系统需解决的关键技术问题,为研究新一代复杂工业控制系统指出了一个新的方向。     

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。     

基于混合逻辑动态法的污水处理除氮过程的优化控制

应用混合逻辑动态系统法（MLD）,对活性污泥1号模型（ASM1）进行了简化,利用有关活性污泥法的专家经验,建立了连续进水间歇曝气活性污泥法除氮动态模型.再采用预测控制的方法对该过程进行优化控制,通过仿真结果可知：将MLD法应用到活性污泥法建模和优化控制中,可以更加深入地挖掘相关的专家经验知识,并将这些专家经验知识和连续变量模型相结合,使模型更加精确,控制和优化的效果更好.为污水处理领域研究提供了一条新的途径.     

基于Min-Max的预测控制鲁棒参数设计

工业控制中模型的不确定性是不可避免的.提出基于Min-Max的预测控制器鲁棒参数设计方法,充分考虑到模型的不确定性.仿真结果表明,控制器在对象模型一定范围内变化时仍具有较好的控制品质,不需要重新整定控制器参数,提高了系统的鲁棒性能.     

偏差加权协调预测控制算法研究

提出适用于瘦系统的偏差加权协调控制算法，并且分析其参数确定方法和稳定性。实际应用表明该算法能实现平稳过渡．控制效果良好。     

具有预测校正的固定床反应器状态估计

本文建立了基于后集中方法的固定床反应器的非线性状态估计算法，并分别用正交配置法集中偏微分方程、Gear法积分常微分方程、Hybrid法求解非线性代数方程，实现状态估计器的在线估计．针对工业实际过程中浓度测量滞后较大、采样时间间隔较长的特点，提出了浓度估计的预测校正，进一步提高了状态估计器对模型误差的克服能力．同时，在实验和仿真两方面考察了固定床反应器状态估计器的性能，无论是收敛性还是鲁棒性，其品质都令人满意．     

小城市污水量预测方法及相关规划参数的确定

无为县隶属于安徽省芜湖市,中心城区规划建设用地规模45．98km2,规划期末人口40万。该文论述了污水专项规划中污水量预测的四种方法：单位人口综合用水量指标法、不同性质用地用水量指标法、单位建设用地综合用水量指标法及分类用水指标法,并以无为县为例,根据现状数据,周边城市数据及自身发展特点综合比较后确定了各参数的取值,得出了无为县2030年的规划预测污水量。     

基于改进回声状态网络的游离氧化钙预测控制

在非线性时延水泥烧成系统中,针对传统预测控制方法调节时间长、控制精度不高的问题,提出一种改进的在线型回声状态网络预测控制模型。首先将带有L1范数约束项的递归最小二乘法与回声状态网络相结合构建在线型预测模型,解决传统预测控制模型辨识精度较低、无法进行实时预测的问题;然后基于改进的回声状态网络预测模型,构建预测控制模型结构,并采用具有全局优化能力的粒子群算法进行滚动优化,保证实际输出量快速、准确、平稳地跟随被控量的设定值;最后利用改进的预测控制模型对水泥烧成系统中的游离氧化钙含量进行预测控制仿真实验,结果表明改进的预测控制模型具有良好的性能和应用前景。     

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.     

基于状态校正的积分对象预测控制算法及其应用

针对具有大滞后、强动态干扰的积分特性对象,提出了基于一阶状态校正的预测控制算法。算法考虑预测状态与其对应的实际测量值偏差的积分作用,在一阶维度上施加状态校正,从而大幅提高控制器的预测准确性与应对模型失配的能力。通过仿真对比验证了状态校正算法的有效性。本算法已实际应用于某炼厂闪蒸罐的液位控制,取得了较好的控制效果。     

约束优化控制的约束调整与目标协调及在重油分离器过程中的作用

In this paper, the feasibility and objectives coordination of real-time optimization (RTO) are systemically investigated under soft constraints. The reason for requiring soft constraints adjustment and objective relaxation simultaneously is that the result is not satisfactory when the feasible region is apart from the desired working point or the optimization problem is infeasible. The mixed logic method is introduced to describe the priority of the constraints and objectives, thereby the soft constraints adjustment and objectives coordination are solved together in RTO. A case study on the Shell heavy oil fractionators benchmark problem illustrating the method is finally presented.     

基于2次核SVM的单步非线性模型预测控制

A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.     

Lyapunov‐based MPC with robust moving horizon estimation and its triggered implementation

In this work, we consider moving horizon state estimation (MHE)‐based model predictive control (MPC) of nonlinear systems. Specifically, we consider the Lyapunov‐based MPC (LMPC) developed in (Mhaskar et al., IEEE Trans Autom Control. 2005;50:1670–1680; Syst Control Lett. 2006;55:650–659) and the robust MHE (RMHE) developed in (Liu J, Chem Eng Sci. 2013;93:376–386). First, we focus on the case that the RMHE and the LMPC are evaluated every sampling time. An estimate of the stability region of the output control system is first established; and then sufficient conditions under which the closed‐loop system is guaranteed to be stable are derived. Subsequently, we propose a triggered implementation strategy for the RMHE‐based LMPC to reduce its computational load. The triggering condition is designed based on measurements of the output and its time derivatives. To ensure the closed‐loop stability, the formulations of the RMHE and the LMPC are also modified accordingly to account for the potential open‐loop operation. A chemical process is used to illustrate the proposed approaches. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4273–4286, 2013     

基于混杂策略的预测控制不可行与约束优先级处理

A hybrid approach using MLD (mixed logical dynamical) framework to handle infeasibility and constraint prioritization issues in MPC (model predictive control) based on input-output model is introduced. By expressing constraint priorities as propositional logics and by transforming the propositional logics into inequalities,the infeasibility and constraint prioritization issues are solved in the MPC. Constraints with higher priorities are met first, and then these with lower priorities are satisfied as much as possible. This new approach is illustrated in the control of a heavy oil fractionator-Shell column. The overall control performance has been significantly improved through the infeasibility and control priorities handling.     

Simulation‐Based Design of Dual‐Mode Controller for Non‐Linear Processes

This paper presents a simulation‐based approach for designing a non‐linear override control scheme to improve the performance of a local linear controller. The higher‐level non‐linear controller monitors the dynamic state of the system and calculates an override control action whenever the system is predicted to move outside an acceptable operating regime under the local controller. The design of the non‐linear override controller is based on a cost‐to‐go function, which is constructed by using simulation or operation data. The cost‐to‐go function delineates the admissible region of state space within which the local controller is effective, thereby yielding a switching rule.     

Decentralized Control System Design under Uncertainty Using Mixed‐Integer Optimization

Decentralized control system design comprises the selection of a suitable control structure and controller parameters. In this contribution, the optimal control structure and the optimal controller parameters are determined simultaneously using mixed‐integer dynamic optimization (MIDO) under uncertainty, to account for nonlinear process dynamics and various disturbance scenarios. Application of the sigma point method is proposed in order to approximate the expectation and the variance of a chosen performance index with a minimum number of points to solve the MIDO problem under uncertainty. The proposed methodology is demonstrated with a benchmark problem of an inferential control for a reactive distillation column. The results are compared with established heuristic design methods and with previous deterministic approaches.