Adaptive Air-Fuel Ratio Control with MLP Network

This paper presents an application of adaptive neural network model-based predictive control (MPC) to the air-fuel ratio of an engine simulation. A multi-layer perceptron (MLP) neural network is trained using two on-line training algorithms: a back propagation algorithm and a recursive least squares (RLS) algorithm. It is used to model parameter uncertainties in the nonlinear dynamics of internal combustion (IC) engines. Based on the adaptive model, an MPC strategy for controlling air-fuel ratio is realized, and its control performance compared with that of a traditional PI controller. A reduced Hessian method, a newly developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up nonlinear optimization in the MPC. Keywords Air-fuel ratio control - IC engine - adaptive neural networks - nonlinear programming - model predictive control Shi-Wei Wang PhD student, Liverpool John Moores University; MSc in Control Systems, University of Sheffield, 2003; BEng in Automatic Technology, Jilin University, 2000; Current research interests automotive engine control, model predictive control, sliding mode control, neural networks.Ding-Li Yu obtained B.Eng from Harbin Civil Engineering College, Harbin, China in 1981, M.Sc from Jilin University of Technology, Changchun, China in 1986 and PhD from Coventry University, U.K. in 1995, all in control engineering. He is currently a Reader in Process Control at Liverpool John Moores University, U.K. His current research interests are in process control, engine control, fault detection and adaptive neural nets. He is a member of SAFEPROCESS TC in IFAC and an associate editor of the IJMIC and the IJISS.

Approximate Dynamic Programming for Self-Learning Control

This paper introduces a self-learning control approach based on approximate dynamic programming. Dynamic programming was introduced by Bellman in the 1950's for solving optimal control problems of nonlinear dynamical systems. Due to its high computational complexity, the applications of dynamic programming have been limited to simple and small problems. The key step in finding approximate solutions to dynamic programming is to estimate the performance index in dynamic programming. The optimal control signal can then be determined by minimizing (or maximizing) the performance index. Artificial neural networks are very efficient tools in representing the performance index in dynamic programming. This paper assumes the use of neural networks for estimating the performance index in dynamic programming and for generating optimal control signals, thus to achieve optimal control through self-learning.

Design and Implementation of a Wireless Networked Water Level Control System

The design of a wireless water level control system is introduced and discussed in detail. In this system, the wireless Proportional Integral (PI) controller is developed using the LabVIEW graphical user programming language. Zigbee wireless technology is chosen for the wireless data transfer system. The experimental testbed was built and the system software and hardware were implemented. In order to compare the performance of the wired and wireless system, a corresponding wired water level control system was built. Experimental results show that under the same PI parameters, the settling time of the wired system is 3.3 times faster than the wireless system. However, the percent overshoot using the wireless controller is 4% smaller.

Attractor-Based Simultaneous Design of the Minimum Set of Control Nodes and Controllers in Boolean Networks

Design of control strategies for gene regulatory networks is a challenging and important topic in systems biology. In this paper, the problem of finding both a minimum set of control nodes (control inputs) and a controller is studied. A control node corresponds to a gene that expression can be controlled. Here, a Boolean network is used as a model of gene regulatory networks, and control specifications on attractors, which represent cell types or states of cells, are imposed. It is important to design a gene regulatory network that has desired attractors and has no undesired attractors. Using a matrix-based representation of BNs, this problem can be rewritten as an integer linear programming problem. Finally, the proposed method is demonstrated by a numerical example on a WNT5A network, which is related to melanoma.

考虑节点度紧致化的最优主动解列模型

为解决主动解列模型的连通性和解列模型趋于复杂化导致计算效率下降的问题,本文提出了考虑节点度紧致化的主动解列模型。首先基于单商品流的思想,提出保证孤岛连通性的解析约束;再结合主动解列自身特点和复杂网络中度的概念,提出一种模型紧致化方法以压缩混合整数规划模型的可行域;最后以切负荷与最小潮流中断之和最小为目标求取最优的解列断面。所提模型能保证孤岛内的连通性并且能够较快地求解大型系统的解列问题。3个测试系统的仿真实验表明了该模型的有效性和快速性。

基于图神经网络的最大化代数连通度算法

随着智能体数量的增加,多智能体系统中潜在的通信链路数量呈指数级增长.过多冗余链路的存在给系统带来了大量的能源浪费和维护成本,而盲目地去除链路又会降低系统的稳定性和安全性.代数连通度是衡量图连通性的重要指标之一.然而,传统的半正定规划(SDP)方法和启发式算法在求解大规模场景下的最大化代数连通度问题时非常耗时.在本文中,我们提出了一种监督式的图神经网络模型来优化多智能体系统的代数连通度.我们将传统的SDP方法应用于小规模任务场景中,得到足够丰富的训练样本和标签.在此基础上,我们训练了一个图神经网络模型,该模型可用于更大规模的任务场景中.实验结果表明,当需要去除15条边时,我们的模型的平均性能达到了传统SDP方法的98.39%.此外,我们的模型计算时间极其有限,可以推广到实时场景中去.

程控交换与现代通信组网的优化发展研究

随着社会的发展,传统的通信技术已经无法满足社会需求,因此必须根据市场需求对其进行创新、优化。而现代通信组网是现代通信技术中不可或缺的重要组成部分,将其与程控交换相结合,能够显著提升通信信息传输效率、质量,为用户提供更加优质的通信服务。鉴于此,文章将对现代化通信组网技术与程控交换进行简单的分析和概述,尝试分析两者的优化改造方式,提出3个发展建议,希望能够起到一定的参考作用。

基于故障观测器的机器人多关节手臂最优容错控制

针对机器人多关节手臂容易出现卡死、饱和及损伤等故障影响控制精度的问题,利用设计的故障观测器提出了一种最优容错控制律。首先根据机器人多关节手臂末端的空间位置与各关节转动角度的关系,建立了机器人多关节手臂的故障模型;然后定义了多关节手臂的转角和接触力的跟踪误差,并设计了故障观测器,通过引入径向基函数(Radial Basis Function,RBF)神经网络估计出模型参数;最后根据动态规划的思想,建立了综合位置/接触力跟踪误差和故障的机器人多关节手臂性能指标矩阵,并设计了包含Hamiltonian方程的容错控制律,利用RBF神经网络求解出最优的性能指标,从而得到最优的容错控制律。仿真结果表明,设计的最优容错控制律能够克服各类故障带来的影响,故障估计的最大误差仅为0.09 N·m,位置跟踪的最大误差仅为0.14 cm,接触力控制的最大误差仅为0.18 N,验证了设计方法的可行性。六自由度多关节手臂测试结果表明,在8个空间坐标上定位的最大误差仅为0.17 cm,接触力的最大误差仅为0.22 N,从而验证了设计的最优容错控制律具有优越的工程实用性。

统一通信技术与程控交换混合组网方式的应用

近年来,随着通信技术的快速发展,用户对于通信服务的需求日益多样化与个性化,同时对技术标准提出更高的要求。通过程控交换与通信技术的组合,形成统一的组网方式,能够满足技术发展需求。文章主要探讨统一通信技术与程控交换混合组网方式的应用,基于其功能丰富、配置简单、灵活性高等优势,以软交换为例,总结混合组网方式的基本情况和存在的问题等,在此基础上对其发展方向进行展望。

电力通信程控交换系统组网技术方案研究

程控交换技术具有稳定性高、支持多种通信协议以及自动接通等功能,将其用于电力通信网络,能够提升电力通信稳定性和并网效率。文章基于通信程控交换技术,结合某电力企业要求,设计程控交换系统接入方式、服务器、网络及并网安全防护,实现稳定高效通信,以期为相关工作者提供参考。

程控交换技术下混合组网方式的应用研究

程控交换技术以其高效、精准的数据传输能力,成为现代通信网络组网的核心力量。基于此,概述程控交换技术与现代通信组网技术,分别探究模拟环路中继对接组网方案、典型数字程控交换机对接接入方案以及一台程控交换机加脉冲编码调制(Pulse Code Modulation,PCM)传输复用器的组网方案,并分析程控交换技术在星型结构、树型结构以及混合组网中的应用,为现代混合组网提供参考。

基于Socket编程的实时网络通信系统设计与实现

在电力通信系统中,实时网络通信是确保数据传输高效和稳定的重要环节。Socket编程作为一种底层网络通信技术,广泛应用于各种实时通信场景。文章详细分析传输控制协议(Transmission Control Protocol,TCP)和用户数据报协议(User Datagram Protocol,UDP)在Socket通信中的应用,并设计了基于Socket编程的实时网络通信系统,介绍了系统的架构设计、多线程并发处理、网络延迟优化等关键技术。经过对比实验,验证了该系统在带宽、延迟、丢包率以及并发处理能力方面的优越性能。

未知不匹配互联系统的非对称输入约束分散控制器设计

基于自适应动态规划算法研究了具有未知不匹配互联和非对称输入约束的连续时间非线性系统分散控制问题.首先,根据孤立子系统的局部状态和耦合子系统的参考状态,采用径向基函数神经网络近似未知互连项,从而消除了互联项满足匹配条件且存在上界的常见假设.然后,基于自适应评判框架,将分散最优控制器设计问题转化为一系列子系统非对称约束下局部最优控制器设计问题.利用Lyapunov稳定性定理,证明了不对称输入约束控制器能够迅速地镇定大规模分散系统.其中,引入状态观测器估计大规模非线性互联子系统状态并保证了观测误差满足一致最终有界.另外,利用评判神经网络近似改进后的代价函数,以近似求解Hamilton–Jacobi–Bellman方程,获得满足非对称输入约束的最优分散控制策略.基于评判网络权值更新规则,通过选择合适的Lyapunov函数保证了权值近似误差满足一致最终有界.最后,通过仿真实例验证了该算法的有效性,并通过与未改进代价函数的传统方法对比,体现了该方法的先进性.

配电自动化终端自动验收系统的研制

针对配电自动化终端人工验收方式的耗时长、易出错、效率低等问题,提出一种能够实现信息自动验收系统,通过调度主站控制现场设备,实现程控加量、结果反馈、自动比对等功能,大大缩短配电自动化终端信息验收时间,提高配电网供电可靠性,具有一定的推广应用价值。

A Novel Distributed Optimal Adaptive Control Algorithm for Nonlinear Multi-Agent Differential Graphical Games

In this paper, an online optimal distributed learning algorithm is proposed to solve leader-synchronization problem of nonlinear multi-agent differential graphical games. Each player approximates its optimal control policy using a single-network approximate dynamic programming(ADP) where only one critic neural network(NN) is employed instead of typical actorcritic structure composed of two NNs. The proposed distributed weight tuning laws for critic NNs guarantee stability in the sense of uniform ultimate boundedness(UUB) and convergence of control policies to the Nash equilibrium. In this paper, by introducing novel distributed local operators in weight tuning laws, there is no more requirement for initial stabilizing control policies. Furthermore, the overall closed-loop system stability is guaranteed by Lyapunov stability analysis. Finally, Simulation results show the effectiveness of the proposed algorithm.

民用多旋翼无人机运行风险控制网络模型

为降低民用多旋翼无人机运行风险,从无人机操作人员的角度建立考虑控制效果和成本的风险控制模型。首先,基于贝叶斯网络灵敏度分析确定网络中的关键节点,并提出相应的风险控制策略;然后,运用图论和目标规划理论构建风险控制网络,将单一致因因素的风险控制问题转化为风险组合控制问题;最后,建立民用多旋翼无人机运行风险控制网络模型和贝叶斯控制网络模型,优选出控制效果最好、成本最低的组合控制策略并进行分析。研究结果表明:贝叶斯网络中有9个需要重点防控的致因节点;基于贝叶斯网络的风险控制模型能够确定最优组合控制策略,组合控制策略作用后,失踪、空中碰撞、坠落和人员伤亡事故发生概率相比未采取控制策略时分别下降44.12%,66.86%,44.33%,92.98%。

Extraction of Robot Primitive Control Rules from Natural Language Instructions

A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural language instructions, and motion information condensation with the aid of support vector machine （SVM） theory. Self-organizing fuzzy neural networks are utilized for the collection of control rules, from which support vector rules are extracted to form a final controller to achieve any given control accuracy. In this way, the number of control rules is reduced, and the structure of the controller tidied, making a controller constructed using natural language training more appropriate in practice, and providing a fundamental rule base for high-level robot behavior control. Simulations and experiments on a wheeled robot are carried out to illustrate the effectiveness of the method.

基于自适应动态规划机械臂运动策略仿真研究

针对机械臂在复杂、恶劣的环境下难以实现高精度运动跟踪的问题,提出了基于自适应动态规划(adaptive dynamic programming,ADP)与滑模导纳控制相结合的策略方法。将未知环境建模为线性模型,基于准滑动模态推导出滑模导纳控制器,用以抵抗扰动干扰;提出ADP与滑模导纳控制器相结合的最优控制方法,并对价值函数中R矩阵的定义进行了优化改进,进一步提升了跟踪精度;运用基于ADP的神经网络来逼近最优价值函数的解,提高了收敛速度,从而快速获得哈密顿-雅可比-贝尔曼(Hamilton-Jacobi-Behrmanequation,HJB)方程的近似最优策略。实验结果表明:该方法有效降低了机械臂运动的控制成本以及保证了最优轨迹跟踪。