A closed-loop particle swarm optimizer for multivariable process controller design

Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity. After the effectiveness, efficiency and robustness are tested by benchmark functions, CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.

Energy-Efficient Optimal Guaranteed Cost Intermittent-Switch Control of a Direct Expansion Air Conditioning System

To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The control strategy consists of an open loop optimization controller and a closed-loop guaranteed cost periodically intermittent-switch controller(GCPISC). The error dynamics system of the closed-loop control is modelled based on the GCPISC principle. The difference,compared to the previous DX A/C system control methods, is that the controller designed in this paper performs control at discrete times. For the ease of designing the controller, a series of matrix inequalities are derived to be the sufficient conditions of the lower-layer closed-loop GCPISC controller. In this way, the DX A/C system output is derived to follow the optimal references obtained through the upper-layer open loop controller in exponential time, and the energy efficiency of the system is improved. Moreover, a static optimization problem is addressed for obtaining an optimal GCPISC law to ensure a minimum upper bound on the DX A/C system performance considering energy efficiency and output tracking error. The advantages of the designed hierarchical controller for a DX A/C system with uncertain parameters are demonstrated through some simulation results.

Mean-field stochastic linear quadratic optimal control problems: closed-loop solvability

An optimal control problem is studied for a linear mean-field stochastic differential equation with a quadratic cost functional.The coefficients and the weighting matrices in the cost functional are all assumed to be deterministic.Closedloop strategies are introduced,which require to be independent of initial states;and such a nature makes it very useful and convenient in applications.In this paper,the existence of an optimal closed-loop strategy for the system(also called the closedloop solvability of the problem)is characterized by the existence of a regular solution to the coupled two(generalized)Riccati equations,together with some constraints on the adapted solution to a linear backward stochastic differential equation and a linear terminal value problem of an ordinary differential equation.

Design of a Preview Controller for Articulated Heavy Vehicles

The paper presents a preview controller design for ATS （active trailer steering） systems to improve high-speed stability of AHVs （articulated heavy vehicles）. An AHV consists of a towing unit, namely tractor or truck, and one or more towed units which called trailers. Individual units are connected to one another at articulated joints by mechanical couplings. Due to the multi-unit configurations, AHVs exhibit unique unstable motion modes, including jack-knifing, trailer swing and rollover. These unstable motion modes are the leading cause of highway accidents. To prevent these unstable motion modes, the preview controller, namely the LPDP （lateral position deviation preview） controller, is proposed. For a truck/full-trailer combination, the LPDP controller is designed to control the steering of the front and rear axle wheels of the trailing unit. The calculation of the corrective steering angle of the trailer front axle wheels is based on the preview information of the lateral position deviation of the trajectory of the axle center from that of the truck front axle center. Similarly, the steering angle of the trailer rear axle wheels is calculated by using the lateral position deviation of the trajectory of the axle center from that of the truck front axle. To perform closed-loop dynamic simulations and evaluate the vehicle performance measure, a driver model is introduced and it ＇derives＇ the AHV model based on well-defined testing specifications. The proposed preview control scheme in the continuous time domain is developed by using the LQR （linear quadratic regular） technique. The closed-loop simulation results indicate that the performance of the AHV with the LPDP controller is improved by decreasing rearward amplification ratio from the baseline value of 1.28 to 0.98 and reducing transient off-tracking by 95.03%. The proposed LPDP control algorithm provides an alternative method for the design optimization of AHVs with ATS systems.

基于虚拟方波注入的五相永磁同步电机开路故障下直接转矩控制

在传统直接转矩控制(DTC)中,参考磁链往往是一个固定值,这将导致五相永磁同步电机(PMSM)在正常及容错情况下运行时效率不高。为解决该问题,该文提出一种基于虚拟方波注入的DTC(VSWI-DTC)策略。首先,建立五相PMSM在正常和开路故障情况下的数学模型,在同步旋转坐标系中推导出恒定转矩下电流幅值与定子磁链幅值的关系。其次,设计最优定子磁链探测器。通过向定子磁链幅值中注入虚拟方波信号,根据电流幅值的变化情况补偿定子磁链幅值,在线探寻最优定子磁链幅值。最后,与无差拍DTC相结合,实现电机高效率DTC运行。实验结果表明,所提策略不但能保持DTC良好的动态性能,抑制开路故障引起的转矩脉动,而且提高了电机在正常和开路故障情况下的效率。

露天矿山采矿工程中的能源消耗优化与节约策略

当前采矿工程中的能源消耗优化与节约结构一般设定为独立形式,能源消耗优化的效率相对较低,能耗节约比率大幅度下降,为此提出露天矿山采矿工程中的能源消耗优化与节约策略的设计与验证研究。根据当前的测试需求及标准,先进行单元周期能耗均值计算,采用多层级的方式,提升能源消耗优化的效率,设计多层级能耗优化结构。以此为基础,构建AP-SVR采矿工程能源消耗优化模型,采用再生能源替换,实现能耗节约。测试结果表明:对比于传统进化算法采矿工程能源消耗优化与节约测试组、传统优化AP-SVR模型采矿工程能源消耗优化与节约测试组,此次所设计的AP-SVR采矿工程能源消耗优化与节约测试组最终得出的能耗节约比率均达到5.5%以上,说明此次设计的方法能耗优化、节约方法的效果更佳,针对性更强,具有实际的应用价值。

Research on production optimal control techniques for smart oilfield

Production optimal control technologies have become important tools for efficiently developing oil and gas reservoirs in recent years.This paper presents an overview of the research and application of these technologies in smart oilfield,including reservoir data matching and prediction,well production optimization,and automatic well monitoring and control technologies.With the support of the National Natural Science Foundation of China,we made years of effort and finally derived a novel data—driven reservoir data matching and prediction methods.Besides,the new automatic optimization technologies and flow monitoring and control devices were also presented.The proposed technologies helped improve the computational efficiency by hundreds of times compared to traditional technologies.The real-time optimization and control of the injection and production parameters was realized using the proposed technologies,which have been widely applied in actual reservoirs at home and abroad,achieving significant economic benefits.

A genetic algorithm based lookup table approach for optimal stepping sequence of open-loop stepper motor systems

Stepper motor driven systems are widely used in industrial applications. They are mainly used for their low cost open-loop high performance. However, as dynamic systems need to be increasingly faster and their motion more precise, it is important to have an open-loop system which is accurate and reliable. In this paper, we present a novel technique in which a genetic algorithm （GA） based lookup table approach is used to find the optimal stepping sequence of an open-loop stepper motor system. The optimal sequence objective is to minimize residual vibration and to accurately follow trajectory. A genetic algorithm is used to find the best stepping sequence which minimizes the error and improves the system performance. Numerical simulation has showed the effectiveness of our approach to improve the system performance for both position and velocity. The optimized system reduced the residual vibration and was able to follow the trajectory with minimal error.

Artificial intelligence for closed-loop ventilation therapy with hemodynamic control using the open lung concept

Purpose–The purpose of this paper is to develop an automatic control system for mechanical ventilation therapy based on the open lung concept(OLC)using artificial intelligence.In addition,mean arterial blood pressure(MAP)is stabilized by means of a decoupling controller with automated noradrenaline(NA)dosage to ensure adequate systemic perfusion during ventilation therapy for patients with acute respiratory distress syndrome(ARDS).Design/methodology/approach–The aim is to develop an automatic control system for mechanical ventilation therapy based on the OLC using artificial intelligence.In addition,MAP is stabilized by means of a decoupling controller with automated NA dosage to ensure adequate systemic perfusion during ventilation therapy for patients with ARDS.Findings–Thisinnovativeclosed-loop mechanicalventilation system leadsto a significant improvement in oxygenation,regulates end-tidal carbon dioxide for appropriate gas exchange and stabilizes MAP to guarantee proper systemic perfusion during the ventilation therapy.Research limitations/implications–Currently,this automatic ventilation system based on the OLC can only be applied in animal trials;for clinical use,such a system generally requires a mechanical ventilator and sensors with medical approval for humans.Practical implications–For implementation of a closed-loop ventilation system,reliable signals from the sensors are a prerequisite for successful application.Originality/value–Theexperiment with porcine dynamics demonstrates thefeasibility and usefulness of this automatic closed-loop ventilation therapy,with hemodynamic control for severe ARDS.Moreover,this pilot study validated a new algorithm for implementation of the OLC,whereby all control objectives are fulfilled during the ventilation therapy with adequate hemodynamic control of patients with ARDS.

开放式基金流动性风险的最优控制

在证券价格服从离散时间算术布朗运动的假设下 ,得到资产流动性风险最优控制策略 ,并对该策略进行有关参数的敏感性分析。研究结果表明 ,流动性系数较大时 ,最优控制策略接近于线性策略 ;流动性系数较小时 ,资产管理者会迅速将资产头寸降至理想水平 ,并在大部分时间内保持这种状态 ,直到变现期末达到资产目标头寸。最优策略对管理者的风险厌恶程度、资产波动率和流动性系数较为敏感 ,而对证券超额收益率敏感程度较低。

露天金属矿山边坡缓冲爆破技术优化研究与应用

边坡缓冲爆破可以有效减少超欠挖,降低爆破对保留区岩体的损伤破坏,维护边坡稳定,减少不必要的支护费用。此外,在相同条件下,采用缓冲爆破时边坡角可以适当增大,从而减少剥岩量,降低开采成本。因此,其对露天矿山生产具有非常重要的意义。在现有研究的基础上进一步阐述和分析了边坡缓冲爆破技术机理,对传统的缓冲爆破技术进行了优化,并在经山寺铁矿进行了应用研究。结果表明:该优化后的边坡缓冲爆破技术简单易行,经济效益显著。通过多次爆破施工观测,边坡坡比度、超欠挖度等指标完全达标。

直流配电网运行控制关键技术研究综述

以柔性直流为基础的配电网柔性互联与交直流混合配电网结构将给传统的配电系统运行方式带来巨大变革和挑战。首先简要介绍了一个含大规模新能源接入、集中式储能和负荷,以及融合交、直流微电网的典型多端直流配电网系统,并阐述了基本的分层运行控制框架;然后针对底层稳定控制,分析了直流配电网电压基本控制策略;接着从直流配电网电压波动抑制、直流配电网稳定与阻尼控制,以及直流配电网多运行模式切换控制3个方面综述了提升直流配电网稳定性的关键控制技术。针对直流配电网优化运行关键技术,首先介绍了基于日前调度和实时优化控制的分层优化框架,然后从基于智能软开关(soft open point,SOP)的柔性互联配电网和复杂交直流配电网2类主要场景讨论了直流配电网长时间尺度下的优化调度问题。最后展望了直流配电网运行控制关键技术发展趋势。研究指出:就地控制需要更加柔性化以适应未来交直流配电网灵活功率控制的需求;优化运行控制将深度融合先进信息通讯、量测技术以及大数据、人工智能等技术。论文研究可为直流配电网的运行控制提供参考。

融合张角拥挤控制策略的高维多目标优化

对于高维多目标优化问题,随着目标维数的增加,种群中非被支配解的比例剧增,严重降低了种群的进化压力.为了对数量众多的非被支配解进行有效的拥挤控制并提升种群的多样性,本文在提出张角概念的基础上设计了一种新的拥挤控制策略(Congestion control strategy based on open angle,CCSOA),它的时间复杂度并不会随着目标维数的增加而增大.与目前优秀的进化多目标优化(Evolutionary multiobjective optimization,EMO)算法IBEA(Indicator-based evolutionary algorithm)、NSGAIII(Nondominated sorting genetic algorithm III)和Gr EA(Grid-based evolutionary algorithm)的比较结果表明,融合了CCSOA的高维多目标优化算法在收敛效果和解集分布的均匀性两个方面均有较大的优势.

五相感应电机缺相容错运行的全转矩范围效率优化控制策略

多相电机的缺相容错性是其相比于传统三相电机的一个重要优势,现有的基于带载能力最大化或铜耗最小化策略的缺相容错控制方法在带载能力和驱动效率方面存在一定局限性。针对五相感应电机的几种典型缺相故障,提出一种全转矩范围效率优化容错控制策略,根据实际负载对各相电流间的幅值和相位的相对关系进行实时在线调节,以保证电机在全转矩范围内的任意负载工况均实现定子铜耗最小化,在保证带载能力最大化的同时提高了电机的运行效率。在此基础上,提出一种适用于正常与多种缺相工况的统一容错控制方法。通过阶梯加载和稳态性能对比等试验,证明了所提效率优化控制策略的有效性。

倒立单摆摆起开环控制律的最优化算法

基于最优控制理论,研究了倒立单摆非线性系统过程控制问题·建立了带惩罚条件的摆起过程开环控制律计算的最优化算法·同一般数值方法相比,这种算法在每步优化计算中,以最优步长取代了固定步长,因此可以确定每个时间段上的精确控制量·编制了倒立单摆摆起过程控制律的计算程序,并进行倒立单摆摆起过程仿真分析·结果表明这种算法可以获得高精度的控制律,它将为非线性系统过程精确控制提供条件·

减少氮流失的田间地表控制排水措施研究

针对南方丘陵地区地表排水方式,通过设计和使用自动开启闸门、土工布沙袋和捆扎秸秆作为排水口控制措施,控制降雨初期氮浓度较高的地表水流出农田。通过田间排水试验,研究自动开启闸门、土工布沙袋、捆扎秸秆控制和非控制排水中氮浓度的变化规律,分析了3种控制排水措施减少农田氮流失的机理和氮浓度随时间的变化。对比非控制排水,3种控制排水措施分别减少了53.2%、44%和39.1%的农田氮流失。以控制排水氮浓度和土壤入渗为目标函数,建立了既减少氮流失又防止涝渍的多目标控制排水模型,探讨了最优的地表控制排水时间。

基于二次型最优控制的起重机开环消摆方法

起重机载荷摆振主要是由载荷水平运动引起的。由于这类运动是确定性的,可以事先唯一地求出,由此设计出一种最优控制器,按照二次型最优消摆规律消除载荷摆振。这种方法是开环的,不需要角度传感器或角加速度传感器,降低了消摆控制系统的造价,提高了可靠性。但这种方法只能消除确定性扰动造成的载荷摆振,对随机干扰造成的摆振无效。

基于有限元数值模型和进给速度优化的薄壁件侧铣变形在线控制

为实现在加工过程中对薄壁件侧铣产生的较大切削变形进行在线控制,提出基于有限元数值模型和进给速度优化的在线控制策略。根据薄壁件切削过程的有限元仿真结果,建立数控机床进给速度、切削力、工件切削变形间的数值模型,进而确定用于控制变形的最优目标切削力。在具有开放式模块化的数控系统平台上开发了切削力信号实时采集、滤波功能和基于Brent-Dekker算法的进给速度在线优化策略,并根据滤波后的切削力及相应算法在加工过程中实时调整机床进给速度,保证切削力逐渐接近最优控制目标而实现切削变形的在线控制。试验结果表明,经过进给速度在线优化后的切削过程可将薄壁件侧铣变形控制在规定范围内,同时提高了切削效率。

基于MPC的电厂输煤皮带机系统能源效率优化

针对发电厂输煤皮带机系统能源效率较低的问题,提出了提高皮带机运行效率的优化控制方案.首先分析确立了皮带机功率的数学模型,然后利用基于有限元模型的动态参数分析法获得精确的优化参数,采用开环最优控制削峰填谷策略优化处理.最后根据系统中存在干扰产生控制误差的问题,引入模型预测控制算法(MPC)进行反馈校正和滚动优化.结果表明在分时电价下,模型预测控制策略节能显著,使生产成本大大降低,增加了发电厂的生产效益,具有良好的实际应用价值.

快速汽门的开闭环相结合的控制系统研究

基于ＬＱＲ理论设计的最优快关控制由于下列原因使其效果受到限制：（１）故障初期的严重模型失配；（２）过高的反馈增益使得控制量受到严重限幅。该文提出将线性最优快关控制和开环快关控制相结合，克服了前述缺点。