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。     

模糊复合控制方法在焦炉控制系统中的应用研究

针对焦炉温度的大惯性、纯滞后、非线性和时变性等特点。提出了一种新的模糊复合控制方法．它将常规的前馈控制、反馈控制与具有人工智能的模糊控制相结合。吸取了前馈控制改善系统动态响应特性、反馈控制消除稳态误差以及模糊控制能够较好地解决系统难以建立精确数学模型的优点。解决了焦炉温度控制问题．通过模糊复合控制的理论分析和仿真试验。证明了该控制方法的可行性和有效性．     

神经网络在控制系统中的应用现状及展望

本文分析了神经网络在控制系统中的应用，并对目前几种较重要的神经网络控制现状进行了评述，最后对神经网络控制的发展作了展望。     

模糊神经控制系统

神经网络和模糊逻辑可以解决传统系统无可奈何的问题。这里，我们将要介绍它们是如何工作，并且使诸如高速图形处理这类的应用受益。[编者按]     

新的多代理签名和多代理多签名方案

涉及多个签名人的代理签名体制有三类：第一类称为代理多签名体制;第二类称为多代理签名体制;第三类称为多代理多签名体制。针对Lee等人的强代理签名方案和代理多签名方案中的安全缺陷,给出了新的改进方案,新方案能有效地防止原始签名人的伪造攻击,在改进方案的基础上给出了安全的多代理签名方案和多代理多签名方案。     

神经网络在控制系统中的应用现状及展望

本文分析了神经网络在控制系统中的应用,并对目前几种较重要的神经网络控制现状进行了评述,最后对神经网络控制的发展作了展望。     

多代理信息融合算法

提出了一种基于模糊数的多代理信息融合算法。多代理系统中,自身代理为实现自身利益,有时提供非真实的诱导信息影响决策。通过分析信息融合算子对诱导信息的响应,扩充了简洁OWA(n-OWA)为信息融合算子以消除诱导信息的影响。同时,引进后续惩罚因子,降低该代理在后续阶段的作用来惩罚提供诱导信息代理。     

神经网络与模糊逻辑的集成及其在列车控制系统中的应用

针对神经网络的学习算法存在的缺陷,将模糊逻辑集成进神经网络的学习过程中,提出了一种Ｆ－ＢＰ算法,大大地加快了神经网络的学习速度。在此基础上,提出了一种在学习算法,在线的调整神经网络的参数,使神经网络能动态适应环境。     

紧偶合多回路模糊控制系统

本文介绍了一个适用于紧偶合多回路目标控制的控制系统。该控制系统的硬件以单片机为核心，以一套控制器对一个控制目标的多个控制回路分别进行调控。由于各控制回路之间存在着严重的偶合影响，故而采用常规的控制方式很难达到控制要求。因此尝试采用模糊控制方式来解决这一难题。该系统经过在一座55kW的三回路火炉上的试验，运行、表现出较好的调控效果。     

基于PLC的多模式交通灯控制系统的设计

为保障交通安全有序,减少交通事故的发生,需要在交通路口设计交通灯控制系统。由于PLC控制系统具有修改方便、操作简单、易上手的特点,本文综合运用PLC定时器指令、计数器指令、转换指令、比较指令等实现交通信号灯的控制程序设计。结果表明,该程序能够稳定可靠地实现十字路口交通灯的多模式控制。     

列车运行过程的多Agent 集成探讨

分析了铁路列车运动过程的特点，讨论了其运行过程中智能控制的现状及存在的问题，针对采用单一智能模式控制的缺点及多目标运营要求，提出采用多Agent集成技术，建立铁路列车运行过程自动化的综合智能控制。讨论了建立列车运行过程控制的多Agent集成系统的必要性和可行性，并给出该系统的结构和实现方法。     

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton–Jacobi–Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.     

Survey of Intelligent Control Techniques for Humanoid Robots

This paper focusses on the application of intelligent control techniques (neural networks, fuzzy logic and genetic algorithms) and their hybrid forms (neuro-fuzzy networks, neuro-genetic and fuzzy-genetic algorithms) in the area of humanoid robotic systems. It represents an attempt to cover the basic principles and concepts of intelligent control in humanoid robotics, with an outline of a number of recent algorithms used in advanced control of humanoid robots. Overall, this survey covers a broad selection of examples that will serve to demonstrate the advantages and disadvantages of the application of intelligent control techniques.     

Distributed containment control for nonlinear multiagent systems in pure‐feedback form

In this paper, the problem of distributed containment control for pure‐feedback nonlinear multiagent systems under a directed graph topology is investigated. The dynamics of each agent are molded by high‐order nonaffine pure‐feedback form. Neural networks are employed to identify unknown nonlinear functions, and dynamic surface control technique is used to avoid the problem of explosion of complexity inherent in backstepping design procedure. The Frobenius norm of the ideal neural network weighting matrices is estimated, which is helpful to reduce the number of the adaptive tuning law and alleviate the networked communication burden. The proposed distributed containment controllers guarantee that all signals in the closed‐loop systems are cooperatively semiglobally uniformly ultimately bounded, and the outputs of followers are driven into a convex hull spanned by the multiple dynamic leaders. Finally, the effectiveness of the developed method is demonstrated by simulation examples.     

Distributed control of uncertain multiagent systems for tracking a leader with unknown fractional‐order dynamics

This paper proposes distributed adaptive cooperative control algorithms for second‐order agents to track a leader with unknown dynamics. The models of the followers and the leader are composed of uncertain nonlinear components. The order of the leader's dynamics is unknown and can be fractional. Only the single output information is shared among neighbored agents. To simplify the control design, linearly parameterized neural networks are used to approximate the unknown functions. We first present an adaptive control for leaderless consensus and then extend the method to the tracking problem. Thorough theoretical proofs as well as numerical simulation are included to verify the results. Compared with relevant literature, the new approach applies to a larger variety of systems because (i) knowledge about the structure of leader's model is unnecessary; (ii) the unknown functions in different agents' dynamics can be diverse and arbitrary, in other words, the algorithms apply to heterogeneous agents; (iii) the results can be simply used without parameter calculations.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Neuroadaptive containment control of nonlinear multiagent systems with input saturations

This paper investigates the output containment tracking problem of nonlinear multiagent systems with mismatched uncertain dynamics and input saturations. A neural network–based distributed adaptive command filtered backstepping (CFB) scheme is given, which can guarantee that the containment tracking errors reach to the desired neighborhood of origin and all signals in the closed‐loop system are bounded. Note that error compensation system and virtual control laws established in CFB only use local information, so the given scheme is completely distributed. Moreover, the applied sliding mode differentiator (SMD) can make the outputs of SMD fast approximate the virtual signal and its derivative at each step of backstepping, which can further improve the control quality. Finally, a simulation example is given to show the effectiveness of the proposed scheme.     

基于遗传算法的模糊神经网络控制器设计及其稳定性分析

首先根据联结主义思想模糊控制器设计问题转化为对模糊神经网络参数的设计和优化,然后通过遗传算法对模糊神经网络的参数进行集中优化,得到了被控对象的一个最优或次优的控制器－模糊神经控制器,稳定性分析为此设计理论依据。     

A neural network approach to freeway network traffic control

The paper investigates the application of a feedforward neural network approach to freeway network control via variable direction recommendations at bifurcation locations. A nonlinear control problem is formulated and solved first by use of computationally expensive nonlinear optimization techniques. A feedforward neural network is then trained by optimally adjusting its weights so as to reproduce the optimal control law for a limited number of traffic scenarios. Generalisation properties of the neural network are investigated and a discussion of advantages and disadvantages compared with alternative control approaches is provided.