火电厂Multiagent控制系统中的优化任务分解Agents

提出了一个火电厂多代理控制系统（MACS）,并洋细介绍了其中优化任务分解代理子系统（OTDAS）的目标、决策和运行,OTDAS通过一个优化代理和一个分解代理对火电厂多代理控制系统的任务进行了优化分解。优化代理的决策采用了遗传算法（GA）,OTDAS的运行结果表明GA是Agent决策的有效工具。     

基于多Agent的智能行政审批系统

本文主要研究和开发了基于多代理(Multi-agent System、MAS)的行政审批系统。多代理系统理论是设计和实现复杂软件系统和控制系统的新途径,Agent智能体能通过角色分配协调、协作、智能地完成某些并发的、复杂的任务,使得整个系统成为一个性能优越的整体。多代理技术的使用,可以优化行政审批系统,使得系统的审批过程更加快捷。论文对agent和行政审批系统进行详细研究,在行政审批系统的建模、运行、管理方面,应用agent新技术设计开发出一个全新行政审批系统模型和软件。     

基于自适应分解的多任务协作型昂贵多目标优化算法

现实世界的工程优化问题通常需要同时优化多个冲突的目标,且这些目标函数的评估由于依赖仿真、物理实验而十分昂贵,这类问题被称为昂贵多目标优化问题.使用机器学习方法建立代理模型用于估计候选解的目标函数值是求解此类问题的一种有效手段.高斯代理模型适用于训练样本数较少的中小规模问题,且能提供评估的不确定性,因此常作为代理模型被应用于昂贵优化.分解是处理多目标优化问题的一种有效手段.一个多目标优化问题可被分解为多个单目标优化子问题,且多个子问题可被进一步划分为代理模型学习的一个目标任务.现有基于分解的昂贵多目标优化算法大多将固定数量的子问题静态地划分到同一任务,从而构造多个固定任务并对其建立多任务高斯代理模型进行求解.这未能充分利用数据的相关信息动态反映出任务间的相关性,限制了多任务高斯过程模型的预测精度以及优化算法的最终性能.为此,本文提出了一种自适应多任务多种群协作搜索算法(AMMCS).AMMCS使用相似性指标实时度量已评估的解集,获得子问题间的相关性,从而自适应地划分任务,提升多任务模型的预测质量.此外,AMMCS使用一个解集(种群)优化一个任务,并通过多种群的协作搜索实现多任务高斯模型的批量优化,提高了采样效率,提升了算法的收敛效率.通过AMMCS与六个代理辅助进化算法进行多组实验对比和分析,显示了AMMCS具有良好的性能.我们同时也设计实验验证了算法中自适应分解以及多种群协作搜索的有效性.     

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。     

基于模糊控制的拉式策略在装配生产控制中的应用

利用模糊控制鲁棒性强的特点, 将其引入单产品装配线的生产控制系统中, 构造新的拉式控制策略. 在建立控制系统周期审查模型和完成控制器设计的基础上, 通过缩小变异范围改进遗传算法求解以极小化在制品量和投放波动为目标, 顾客满意率为约束条件的多目标规划, 从而达到优化控制系统参数的目的. 为考察模糊控制系统性能, 通过实例与多阶段定量在制品法(Constant work-in-process, CONWIP)、Kanban和一般拉式策略 (Generic pull, GP) 系统进行比较, 结果表明模糊生产控制系统不仅能维持较低的在制品水平, 更重要的是能维持较低的订单投放波动水平.     

大型火电机组协调控制方案探讨

介绍了大型火电厂协调控制方案的原理,列举和利时自动化有限公司的HOLLIAS-MACS分散控制系统(DCS)在300MW火电厂机组运行控制中协调方案的实际应用,反映了协调控制系统在电厂运行中的重要性并对机炉负荷协调模式进行了探讨.     

基于遗传算法的振动主动控制优化方法研究

对多岛遗传算法和非支配排序遗传算法进行了研究;以结构振动系统的结构振动能量指标和系统控制能量指标作为多目标优化函数,建立了振动主动控制系统的传感器和作动器位置、数量、长度及控制增益的多目标优化配置数学模型,首次采用非支配排序遗传算法作为优化策略进行多目标优化,同时,以多岛遗传算法作为优化策略,以系统存留能量指标作为单目标优化函数,并以悬臂梁作为算例进行了对比仿真,验证了二者的一致性。     

一种混合遗传算法在异步电机直接转矩调速系统中的应用

针对煤矿电机车异步电动机直接转矩控制系统中数学模型参数的不确定问题提出了一种基于混合遗传算法的模糊控制器,讨论了遗传算法中禁忌搜索(TabuSearch)的变异算子,并对它进行了一些改进.理论分析和仿真结果表明,采用遗传算法可以对模糊控制器的加权因子进行优化,有助于改善系统的动态性能.     

考虑CO2排放量的能量枢纽运行配置优化

多能源系统通过电、热、气等不同形式能源在生产、传输、消费等多个环节进行协同优化为解决能源与环境问题提供了新方案.能量枢纽(EH)作为多能源系统的耦合环节,其配置方案对多能源系统的优化运行至关重要.在此背景下,本文提出了一种考虑CO2排放量的含电力、热能、天然气等不同形式能源的能量枢纽优化运行配置方案.在考虑CO2排放量的基础上,提出一个多目标优化问题,并采用遗传算法(GA)求解整体优化问题,从而实现社会效益最大化,CO2排放量最小化的目标.最后通过不同配置算例的分析比较验证所提方法的有效性,为能量枢纽的建设和运行提供理论和技术支撑.     

工业大系统多目标优化问题的分解协调方法

本文针对工业大系统多目标优化问题的特点,提出"一致单调性"概念、"目标诱导性决策" 方法以及与分解协调方法有关的两个定理,使分解协调问题得以简化.     

Modeling Uncertainty and its Implications to Sophisticated Control in Tæms Agents

Open environments are characterized by their uncertainty and non-determinism. Agents need to adapt their task processing to available resources, deadlines, the goal criteria specified by the clients as well their current problem solving context in order to survive in these environments. If there were no resource constraints, then an optimal Markov Decision Process based policy would obviously be the best way for complex problem solving agents to make scheduling decisions. However in many agent systems, these scheduling decisions have to be made on-line or in soft real-time, making the off-line policy computationally infeasible in open environments. The hybrid planner/scheduler used to control Task Analysis, Environment Modeling, and Simulation (TÆMS) agents is the Design-to-Criteria (DTC) agent scheduler. Design-to-Criteria scheduling is the soft real-time process of custom building a plan/schedule to meet an agent’s current objectives which are expressed as dynamic goal criteria (including real-time deadlines), using task models that describe alternate ways to achieve tasks and subtasks. Recent advances in Design-to-Criteria control include the addition of uncertainty to the TÆMS computational task models analyzed by the scheduler and the incorporation of uncertainty in the scheduling process. As we show, the use of uncertainty in TÆMS and Design-to-Criteria enables agents to make better control decisions in uncertain environments. Design-to-Criteria uses a heuristic approach for on-line scheduling of medium granularity tasks.It approximates the analysis used to generate an optimal policy by heuristically reasoning about the implications of uncertainty in task execution. The addition of uncertainty has also spawned a post-scheduling contingency analysis step for situations in which an agent must produce a result by a given deadline (deadline critical situations) and where the added computational cost is worth the expense. We describe the uncertainty representation in TÆMS and how it improves task models and the scheduling process, and provide empirical examples of reasoning about uncertainty in action. We also evaluate the performance of our heuristic-based approach to agent control using the performance of the policy generated by an optimal controller as the benchmark.     

基于MAS的分布式焦炉集气管压力解耦控制

针对焦炉集气管压力这类多变量非线性耦合系统,提出了一种基于multi-agent system(MAS)的焦炉集气管压力智能协调控制系统方案,给出了agent的分层组织结构和演化机制．在控制agent中,采用TS模糊神经回归网络对被控对象进行分布式建模,运用分布式智能协调解耦算法进行解耦控制,监督学习与强化学习相结合,采用遗传协进化算法对多个agent协调优化．通过agent模态变迁进行模式切换,以适应快速突变环境．工程应用表明,提出的控制策略有效地解决了集气管压力这类复杂对象的过程控制问题．     

A robust integrator algorithm with genetic based fuzzy controller feedback for direct vector control

The voltage model used for direct vector control has in the flux calculation process an open integration problem, which is generally solved with a feedback loop. In this paper, a new design method is developed for the feedback loop of the integrator. The method, as apart from standards in the literature, uses a fuzzy controller. Fuzzy controllers are knowledge-based systems that include fuzzy rules and fuzzy membership functions to incorporate human knowledge into their knowledge base. The determination of these rules and membership functions is one the key problems when designing fuzzy controllers, and is generally affected by subjective decisions. In this study, a fuzzy controller with rules and membership functions determined by genetic algorithms (GAs) in this study is designed and tested on various motors of different power ratings. The proposed method is simulated by using MATLAB/SIMULINK and implemented on an experimental system using a TMS320C31 digital signal processor.     

Multi-Agent System-Based Fuzzy Controller Design with Genetic Tuning for a Mobile Manipulator Robot in the Hand Over Task

This paper presents an application of the multi-agent system approach to a service mobile manipulator robot that interacts with a human during an object delivery and hand-over task in two dimensions. The base, elbow and shoulder of the robot are identified as three different agents, and are controlled using fuzzy control. The control variables of the controllers are linear velocity of the base, angular velocity of the elbow, and angular velocity of the shoulder. Main inputs to the system are the horizontal and vertical distances between the human and robot hands. These are input to all three agents. In developing the fuzzy control rules, effective delivery and avoidance of contact with humans, not to cause physical damage, are considered. The membership functions of the fuzzy controllers are tuned by using genetic algorithms. In tuning, the performance is calculated considering the distance deviation from the direct path, time spent to reach the human hand and energy consumed by the actuators. The proposed multi-agent system structure based on fuzzy control for the object delivery task succeeded in both effective and safe delivery.     

A real-time decision-making of maintenance using fuzzy agent

In many manufacturing processes, real-time information can be obtained from process control computers and other monitoring devices. However, production control problems are frequently accompanied by certain and uncertain conditions. Problems with uncertainty conditions generally include difficulty in identifying an optimal solution in real-time using conventional mathematical approaches. This study presents a fuzzy logic approach for decision-making of maintenance. Some linguistic variables and rules-of-thumb are used to form the fuzzy logic models, based on the domain experts’ experiences in production line and maintenance department. The historical production data are used to train and tune the fuzzy models. The tuned fuzzy models are then embedded into an internet-based and event-oriented information system as fuzzy agent. The production controller can easily make suitable production control decisions based on the inference results of fuzzy agents to satisfy the quick response requirement.     

Optimal two-degree-of-freedom fuzzy control for locomotion control of a hydraulically actuated hexapod robot

Locomotion control of legged robots is a very challenging task because very accurate foot trajectory tracking control is necessary for stable walking. An electro-hydraulically actuated walking robot has sufficient power to walk on rough terrain and carry a heavier payload. However, electro-hydraulic servo systems suffer from various shortcomings such as a high degree of nonlinearity, uncertainty due to changing hydraulic properties, delay due to oil flow and dead-zone of the proportional electromagnetic control valves. These shortcomings lead to inaccurate analytical system model, therefore, application of classical control techniques result into large tracking error. Fuzzy logic is capable of modeling mathematically complex or ill-defined systems. Therefore, fuzzy logic is becoming popular for synthesis of control systems for complex and nonlinear plants. In this investigation, a two-degree-of-freedom fuzzy controller, consisting of a one-step-ahead fuzzy prefilter in the feed-forward loop and a PI-like fuzzy controller in the feedback loop, has been proposed for foot trajectory tracking control of a hydraulically actuated hexapod robot. The fuzzy prefilter has been designed by a genetic algorithm (GA) based optimization. The prefilter overcomes the flattery delay caused by the hydraulic dead-zone of the electromagnetic proportional control valve and thus helps to achieve better tracking. The feedback fuzzy controller ensures the stability of the overall system in the face of model uncertainty associated with hydraulically actuated robotic mechanisms. Experimental results exhibit that the proposed controller manifests better foot trajectory tracking performance compared to single-degree-of-freedom (SDF) fuzzy controller or optimal classical controller like state feedback LQR controller.     

A framework for meta-level control in multi-agent systems

Sophisticated agents operating in open environments must make decisions that efficiently trade off the use of their limited resources between dynamic deliberative actions and domain actions. This is the meta-level control problem for agents operating in resource-bounded multi-agent environments. Control activities involve decisions on when to invoke and the amount to effort to put into scheduling and coordination of domain activities. The focus of this paper is how to make effective meta-level control decisions. We show that meta-level control with bounded computational overhead allows complex agents to solve problems more efficiently than current approaches in dynamic open multi-agent environments. The meta-level control approach that we present is based on the decision-theoretic use of an abstract representation of the agent state. This abstraction concisely captures critical information necessary for decision making while bounding the cost of meta-level control and is appropriate for use in automatically learning the meta-level control policies.     

Fuzzy‐Neuron Intelligent Coordination Control Fora Unit Power Plant

A novel fuzzy‐neuron intelligent coordination control method for a unit power plant is proposed in this paper. Based on the complementarity between a fuzzy controller and a neuron model‐free controller, a fuzzy‐neuron compound control method for Single‐In‐Single‐Out (SISO) systems is presented to enhance the robustness and precision of the control system. In this new intelligent control system, the fuzzy logic controller is used to speed up the transient response, and the adaptive neuron controller is used to eliminate the steady state error of the system. For the multivariable control system, the multivariable controlled plant is decoupled statically, and then the fuzzy‐neuron intelligent controller is used in each input‐output path of the decoupled plant. To the complex unit power plant, the structure of this new intelligent coordination controller is very simple and the simulation test results show that good performances such as strong robustness and adaptability, etc. are obtained. One of the outstanding advantages is that the proposed method can separate the controller design procedure and control signals from the plant model. It can be used in practice very conveniently.     

Fuzzy logic control of a solar power plant

This paper presents an application of fuzzy logic control to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A special subclass of fuzzy inference systems, the TP (triangular partition) and TPE (triangular partition with evenly spaced midpoints) systems, is used to obtain adequate control signals in the whole range of possible operating conditions. The fuzzy logic controller has been tested in the real plant and results obtained are shown. A comparison with other control approaches widely used in the plant is performed using a nonlinear computer model of the field