基于模糊神经网络的多变量解耦控制

针对焦炉集气管压力这类多变量非线性系统，提出了一种基于PID神经网络和RBF模糊神经网络的多变量解耦控制方案，RBF模糊神经网络对多变量对象解耦，PID神经网络控制器控制过程的动态特性。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。     

多座不对称焦炉集气管压力模糊解耦控制

某钢铁企业多座不对称焦炉具有容量不同、管道布局不同等特点, 使得基于传统解耦控制方法的集气管压力控制效果欠佳. 为此本文提出了一种多座不对称焦炉集气管压力模糊解耦控制方法. 从降低解耦控制器输入维数的角度出发, 采用动态耦合度方法可以实时确定多座不对称焦炉集气管的耦合关系; 并采用模糊解耦控制器对动态耦合度分析后的集气管压力进行实时解耦控制, 该方法从理论意义上证明了模糊控制的解耦原理, 实现了焦炉集 气管压力的解耦. 对于具有不对称特性的被控对象提供了一定的理论和实际应用价值.     

焦炉集气管压力Fuzzy-PID控制系统设计

针对焦炉集气管压力这类多变量非线性系统，提出了一种基于Fuzzy—PID控制的智能分段控制方案．构成了焦炉集气管压力智能控制系统。采用Siemens公司的S7-200PLC进行控制．采用WinCC组态软件实现监控功能。实际应用表明，提出的控制策略有效解决了集气管压力控制过程中的多扰动、强耦合的非线性问题，具有良好的动态和静态特性，系统主要技术指标优于原指标。     

焦炉集气管压力综合控制算法研究与应用

集气管压力是焦炉操作的重要参数之一，稳定集气管压力对延长焦炉寿命、稳定操作、减少煤气放散、减轻环境污染及节约能源具有重要作用。针对焦炉集气管压力系统具有多回路、强耦合、非线性的特点，提出了一种综合控制算法。该算法将变积分PI控制与运用相关性分析法的解耦控制有机结合。通过改变积分系数．保证单座焦炉的稳定，通过相关性分析及补偿，消除焦炉之间集气管压力的耦合影响。解决了具有耦合特性的多座焦炉的集气管压力稳定问题。实际运行结果表明，该方法简单实用，可靠有效。     

多焦炉集气管压力控制系统

焦炉集气管压力系统是一个具有非线性、多耦合、强扰动的多变量时变系统,通过采用北京亚控公司工控软件Kingview和KingACT,运用智能协调最优控制及变参数PID控制相结合的方法,成功实现了对多焦炉集气管压力系统的精确控制,多座焦炉压力稳定在设定值10Pa内。     

焦炉集气管压力模糊神经网络控制系统

针对焦炉集气管压力这类多变量非线性系统,提出一种基于模糊神经网络的智能协调控制方案。应用遗传算法对模糊神经网络结构和参数进行优化,并采用PLC的逻辑梯形图语言编程实现智能协调运算。工程应用表明了系统设计的有效性。     

相关性分析解耦控制算法的研究与应用

针对焦炉集气管压力系统具有多回路、强耦合、非线性的特点，提出了一种基于相关性分析的解耦控制算法，并将该算法与变积分PI控制有机结合，解决了具有相互耦合的多座焦炉的集气管压力稳定问题。实际运行结果表明该方法简单实用，可靠有效。     

DCS在焦炉集气管压力调节上的优化控制

针对焦炉集气管压力这类多变量非线性系统,在分析控制对象的基础上,采用逻辑判断,去耦合,去扰动、校正控制参数。在总管控制级采用总管吸力监督控制,达到敏感性和鲁棒性的统一:从而达到焦炉集气管压力的优化控制,在工况稳定的前提下,误差控制可达到±10Pa。     

焦炉集气管压力的变结构模糊控制研究

提出了集气管的智能控制方法．详细介绍了新2#焦炉集气管压力控制器的设计方法．针对该焦炉集气管压力波动幅值大、压力振荡剧烈的情况，提出了变结构模糊控制算法；该方法对集气管压力的不同波动范围，通过采用不同的控制规则以及量化因子设计了两个模糊控制器．仿真试验以及现场实际运行结果表明，根据变结构模糊控制算法建立的控制系统能够迅速调节新2#蝶阀开度使压力在15s内达到稳定，实际效果证明了该算法的有效性和可靠性．     

集气管压力控制系统建模与解耦

焦炉集气管压力系统是一个强干扰、时变、多参数、严重耦合的系统.针对影响焦炉集气管压力系统的主要参数进行了研究,运用机理建模法建立了控制系统的数学模型;并运用前馈补偿法进行解耦,构建阀门问的影响关系模型,实现基于阀门联动关系的集气管压力的自动控制.该控制模型建立了影响集气管压力稳定的主要参数与控制阀门开度之间的映射关系,在压力控制系统中的应用效果良好.     

Hybrid intelligent control of gas collectors of coke ovens

Stable pressure of gas collectors is beneficial to prolonging coke oven's life-time, consuming less energy and decreasing pollution. However, it is difficult to stabilize the pressure with conventional control methods because the collectors’ pressure system is a time-varying, nonlinear multi-variable system that is strongly inter-coupled and disturbed. In this paper, a hybrid control approach that incorporates PID control with feedforward control and expert control is presented. It integrates simplicity, reliability, flexibility and promptness in restraining disturbance by making full use of each method's advantages. The hybrid intelligent control system has been built to control the pressure of gas collectors. The results of actual runs show the effectiveness.     

专家优化控制在焦炉集气管压力调节上的应用

我国的焦炉大多采用多集气管并联,共用一套风机、冷凝系统。本文针对集气管压力被控过程的多样性、时变性、非线性的特点,通过分析影响集气管压力控制的多种因素,建立了一种新形式的焦炉集气管压力系统的数学模型。本文详细介绍了自适应专家智能控制系统程序的编制以及具体实现方法,通过专家控制算法和解耦相结合控制集气管压力,结果令人满意。经过两年的实际应用,表明了所建模型和控制方案的合理性和实用性。     

Cooperative, hybrid agent architecture for real-time traffic signal control

This paper presents a new hybrid, synergistic approach in applying computational intelligence concepts to implement a cooperative, hierarchical, multiagent system for real-time traffic signal control of a complex traffic network. The large-scale traffic signal control problem is divided into various subproblems, and each subproblem is handled by an intelligent agent with a fuzzy neural decision-making module. The decisions made by lower-level agents are mediated by their respective higher-level agents. Through adopting a cooperative distributed problem solving approach, coordinated control by the agents is achieved. In order for the multiagent architecture to adapt itself continuously to the dynamically changing problem domain, a multistage online learning process for each agent is implemented involving reinforcement learning, learning rate and weight adjustment as well as dynamic update of fuzzy relations using an evolutionary algorithm. The test bed used for this research is a section of the Central Business District of Singapore. The performance of the proposed multiagent architecture is evaluated against the set of signal plans used by the current real-time adaptive traffic control system. The multiagent architecture produces significant improvements in the conditions of the traffic network, reducing the total mean delay by 40% and total vehicle stoppage time by 50%.     

Optimal control of distributed multiagent systems with finite-time group flocking

The flocking of multiple intelligent agents, inspired by the swarm behavior of natural phenomena, has been widely used in the engineering fields such as in unmanned aerial vehicle (UAV) and robots system. However, the performance of the system (such as response time, network throughput, and resource utilization) may be greatly affected while the intelligent agents are engaged in cooperative work. Therefore, it is concerned to accomplish the distributed cooperation while ensuring the optimal performance of the intelligent system. In this paper, we investigated the optimal control problem of distributed multiagent systems (MASs) with finite-time group flocking movement. Specifically, we propose two optimal group flocking algorithms of MASs with single-integrator model and double-integrator model. Then, we study the group consensus of distributed MASs by using modern control theory and finite-time convergence theory, where the proposed optimal control algorithms can drive MASs to achieve the group convergence in finite-time while minimizing the performance index of the intelligence system. Finally, experimental simulation shows that MASs can keep the minimum energy function under the effect of optimal control algorithm, while the intelligent agents can follow the optimal trajectory to achieve group flocking in finite time.     

Chattering-free model free adaptive sliding mode control for gas collection process with data dropout

Stable pressure control of coke oven gas collectors is difficult due to the problems of nonlinearity, couplings, time-variation, disturbances and data-dropout. This paper proposes a novel chattering-free model free adaptive sliding mode control scheme for the gas collection process of coke ovens. Unlike the conventional data-driven sliding mode control approaches, the proposed controller is based on the data-dropout compensation scheme and predictive control strategy, whereby a novel data-driven sliding surface estimator and a new data-driven sliding mode prediction model are developed to facilitate the controller design. On one hand, the effect of data dropout could be attenuated by applying the proposed estimator. On the other hand, a chattering-free smooth control law with strong robustness to couplings, time-variation and disturbances is obtained owing to the application of the predictive control strategy and the sliding mode control technique. In addition, the convergence of the proposed algorithm could be guaranteed theoretically. Finally, experimental results soundly confirm the efficacy and superiority of the developed approach.     

Multivariable Intelligent Decoupling Control System and its Application

Many industrial processes have compositive complexities including multivariable, strong coupling, nonlinearity, time-variant and operating condition variations. Combining multivariable adaptive decoupling control with neural networks, this paper presents a multivariable neural network-based decoupling control algorithm. This control algorithm is integrated with distributed control technique and intelligent control technique, and a three-leveled intelligent decoupling control system consisting of basic control level, coordinating control level, and management and decision level is developed. The configuration and function of the control system are discussed in detail. This system has been successfully applied in ball mill pulverizing systems of 200MW power units, and remarkable benefits have been obtained.     

Multivariable Intelligent Decoupling Control System and its Application

Many industrial processes have compositive complexities including multivariable, strong coupling, nonlinearity, time-variant and operating condition variations. Combining multivariable adaptive decoupling control with neural networks, this paper presents a multivariable neural network-based decoupling control algorithm. This control algorithm is integrated with distributed control technique and intelligent control technique, and a three-leveled intelligent decoupling control system consisting of basic control level, coordinating control level, and management and decision level is developed. The configuration and function of the control system are discussed in detail. This system has been successfully applied in ball mill pulverizing systems of 200MW power units, and remarkable benefits have been obtained.     

一种多变量模糊神经网络解耦控制器的设计

为提高多变量、非线性和强耦合系统的动态特性和解耦能力,根据解耦原理和神经网络思想,提出一种两级串联结构的自适应模糊神经网络解耦控制器.前级是基于智能权函数规则的自调整模糊控制器,后级是基于动态耦合特性的自适应神经网络解耦控制器.同时从理论上证明了学习算法的收敛性.仿真实例表明,所提出的解耦控制器具有良好的鲁棒性和自适应解耦能力,是解决多变量、非线性和强耦合问题的一种简便有效的控制算法.