车用摆销链式CVT速比控制参数优化策略研究

速比控制是变速器控制领域的关键技术之一。通过分析摆销链式CVT的结构及工作原理,采用模糊PID控制方法对汽车行驶循环工况进行仿真;搭建CVT传动系统数学模型和速比模糊控制器子模型,基于Simulink/Cruise联合仿真软件平台对模糊控制进行仿真研究;最后,引入遗传算法对速比控制器控制规则进行参数优化,分别对汽车起步工况和循环工况速比跟踪控制策略进行验证。结果表明,基于遗传算法的速比模糊控制对目标速比的跟随效果及误差变化均优于传统的模糊PID控制方法,可满足摆销链式CVT速比控制的要求。     

模糊控制在立推式铝板锭加热炉中的实现

加热炉的温度具有非线性、大滞后的特性,模糊控制是一种有效地针对该问题的解决方法。使用模糊控制策略来实现炉温的控制,设计了一个适用于现场温度控制的二阶模糊控制器,并基于PLC编程平台设计了模糊控制程序结构和算法,在中铝瑞闽公司立推式铝板锭加热炉生产应用中得到良好效果。     

中央空调冷冻水系统的模糊PID控制

将模糊控制和PID控制相结合,提出了一种模糊PID控制策略,并将其应用于中央空调冷冻水变流量调节。利用模糊控制在线自适应调整PID控制器的参数,从而使系统的动态性能和静态性能均得到提高。实验结果表明,基于模糊PID控制的冷冻水变流量系统具有响应速度快、超调小、稳态精度高和鲁棒性强等特点。     

自适应模糊PID在井式回火电加热炉中的应用

提出了利用模糊PID控制器控制的方法,并设计了模糊PID控制器应用于电加热炉中对炉温进行控制。介绍了模糊PID控制器结构,以及模糊控制规则的生成方法,并且利用MATLAB语言对该控制方案进行数字仿真。仿真结果表明：该方法稳态误差为零。动态响应快,超调量小,有较好的控制效果。     

基于熵测度的加工过程模糊控制的参数优化

设计了一种参数优化的模糊控制器,其以信息熵为优化的性能指标,根据模糊控制器输入变量的大小,寻找模糊控制器的最优参数。应用于切削条件变化的加工过程的参数自调整模糊控制表明,这种方法不仅可以提高模糊控制的动、静态性能,而且还具有信息补偿、鲁棒性强等特点。     

基于熵测度的加工过程模糊控制参数优化

设计了一种参数优化的模糊控制器，其以信息熵为优化的性能指标，根据模糊控制器输入变量的大小。寻找模糊控制器的最优参数。应用于切削务件变化的加工过程的参数自调整模糊控制表明，这种方法不仅可以提高模糊控制的动、静态性能，而且还具有信息补偿、鲁棒性强等特点。     

一种在线插值模糊控制器在炉温控制系统中的应用

针对电加热炉大惯性、纯滞后、参数时变的非线性对象的控制的特点,以及模糊控制本身不具备自学习的能力。本文提出了一种在线插值模糊控制方法,设计了在线插值模糊控制器,并在炉温控制系统中应用。实验结果表明,本文所提出的在线插值模糊控制,消除了系统的稳态误差,没有超调和振荡,鲁棒性较强,而且简单易行,具有一定的实用价值。     

基于优化模糊控制的清洗机研究

清洁度和流量控制是茎块状农产品清洗机运行的两个重要参数。为了实现优化对茎块状农产品清洁度和清洗过程中水流量的控制,提出了一种基于改进粒子群优化模糊控制的智能清洗机研究。通过粒子群算法在更新迭代中计算最优加权因子,将加权因子引入到模糊控制器中。研究表明,该控制方法将粒子群算法与模糊控制算法有效结合,提高了控制系统的稳定性,同时实现了以较少的水流量提高茎块状农产品的清洁度。     

数控机床进给伺服系统模糊自适应PID仿真

针对传统的PID控制策略不能满足数控进给伺服系统的对控制性能要求。结合模糊控制理论,在传统PID控制基础上设计出一种基于模糊决策的模糊自适应PID控制器。运用模糊控制理论,对PID参数进行实时修改,使系统具有较好的自适应能力和较强的鲁棒性。在MATLAB环境下对进给伺服系统进行了动态仿真;仿真结果表明,模糊自适应PID控制器的控制性能远优于常规PID控制器,即使在外界干扰和系统工况发生变化时,此控制器也具有很好的快速响应特性和较强的鲁棒性。     

基于模糊控制的电动助力转向系统控制方法研究

基于模糊控制的基本原理及模糊控制器的设计方法,对模糊＂PID参数整定的方法进行研究。针对电动助力转向系统,设计了一种模糊自整定PID参数控制策略,利用MATLAB软件对控制算法进行仿真。通过仿真实验,证明了所设计的电动助力转向系统控制算法将PID控制和模糊控制的简便性、灵活性以及鲁棒性相结合,提高了电动助力转向系统的助力控制性能。     

基于免疫遗传算法的模糊径向基函数神经网络在高速电主轴中的应用

为满足高速高性能电主轴系统快、稳、准的控制要求,结合免疫遗传算法寻优速度快及模糊神经网络控制不依赖主轴系统模型的优点,设计了一种将模糊逻辑控制、径向基函数(Radical basis function, RBF)神经网络和免疫遗传算法进行有机结合的高速电主轴系统全局优化的控制策略,并将该智能控制策略成功应用于高速电主轴系统双闭环矢量控制系统的转速控制器中。通过免疫遗传算法对该智能控制器三类参数的同步优化取得了最佳控制效果,从而实现了对主轴输出转速的精确控制。试验和仿真结果验证了所设计的控制器能够精确控制主轴的输出转速,而且当高速电主轴受到突加负载冲击时,具有很好的抗干扰性能及较强的鲁棒性,使主轴系统具有优良的动、静态性能,实现了高品质驱动。     

加热炉模糊控制的研究与应用

本文针对济钢集团的加热炉设计了一种复合模糊控制器,目的是实现加热炉的炉温自动控制。     

Intelligent semi-active vibration control of eleven degrees of freedom suspension system using magnetorheological dampers

A novel intelligent semi-active control system for an eleven degrees of freedom passenger car’s suspension system using magnetorheological (MR) damper with neuro-fuzzy (NF) control strategy to enhance desired suspension performance is proposed. In comparison with earlier studies, an improvement in problem modeling is made. The proposed method consists of two parts: a fuzzy control strategy to establish an efficient controller to improve ride comfort and road handling (RCH) and an inverse mapping model to estimate the force needed for a semi-active damper. The fuzzy logic rules are extracted based on Sugeno inference engine. The inverse mapping model is based on an artificial neural network and incorporated into the fuzzy controller to enhance RCH. To verify the performance of the NF controller (NFC), comparisons with existing semi-active techniques are made. The typical control strategy are linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) controllers with clipped optimal control algorithm, while inherent time-delay and non-linear properties of MR damper lie in these strategies. Simulation results demonstrated that the NFC has better control performance and less control effort than the optimal in improving the service life of the suspension system and the ride comfort of a car.     

Fuzzy model predictive control of non-linear processes using convolution models and foraging algorithms

In this paper, a fuzzy model predictive control (FMPC) approach is introduced to design a control system for nonlinear processes. The proposed control strategy has been successfully employed for representative, benchmark chemical processes. Each nonlinear process system is described by fuzzy convolution models, which comprise a number of quasi-linear fuzzy implications (FIs). Each FI is employed to describe a fuzzy-set based relation between control input and model output. A quadratic optimization problem is then formulated, which minimizes the difference between the model predictions and the desired trajectory over a predefined predictive horizon and the requirement of control energy over a shorter control horizon. The present work proposes to solve this optimization problem by employing a contemporary population-based evolutionary optimization strategy, called the Bacterial Foraging Optimization (BFO) algorithm. The solution of this optimization problem is utilized to determine optimal controller parameters. The utility of the proposed controller is demonstrated by applying it to two non-linear chemical processes, where this controller could achieve better performances than those achieved by similar competing controller, under various operating conditions and design considerations. Further comparisons between various stochastic optimization algorithms have been reported and the efficacy of the proposed approach over similar optimization based algorithms has been concluded employing suitable performance indices.     

基于改进蚁群算法的模糊控制器优化研究

作为智能控制的一种新技术,模糊控制不依赖于工业对象模型。它不是用数值变量而是用语言变量来描述系统特征。并依据系统的动态信息和模糊控制规则进行推理以获得合适的控制量。因而具有较强的鲁棒性。本文使用蚁群算法对模糊控制器进行优化实现模糊规则的自寻优。通过MATLAB进行仿真试验,系统阶跃响应表明经改进的蚁群算法寻优得到的模糊控制器具有良好的性能。     

System modeling and temperature control of reheating furnace walking hearth type in the setting up process

Reheating furnace walking hearth type (RFWHT) has been used for the iron and wire rods of the reheating process in Ratchasima Steel Products Co., Ltd. Factory (Nakhon Ratchasima, Thailand). Ratchasima Steel Products Co., Ltd. is among the companies with the highest energy consumption in Thailand. The company is confronted by problems related to performance improvement in terms of temperature control and reduction of fuel consumption. Since the reheating furnace was installed in 1964 and with the adjustment of some of its devices, the system has required an optimal controller for controlling the temperature inside each zone of the reheating furnace. This study aimed at investigating the mathematical model from the experimental data in the manufacturing process of rolling wire rod production and subsystem by using system identification with genetic algorithm. Results can be used to design a proportional-integral controller for temperature control in each zone and to reduce fuel.     

并联式混合动力汽车能量管理策略新分类与概述

针对混合动力汽车(hybrid electric vehicle,HEV)能量管理策略的分类与综述问题,对并联式HEV能量管理策略的研究现状与发展趋势进行了研究。根据能量管理策略是否使用人工智能控制方式或优化算法,对并联式HEV能量管理策略进行了横向分类和综述,纵向梳理了模糊逻辑、神经网络、动态规划与等效油耗最小原理的具体应用方式,分析了多智能体系统应用于HEV能量管理优化控制的可行性和优势。研究结果表明,智能优化控制型能量管理策略是解决HEV量管理问题的有效途径,而智能优化控制方法和驾驶工况预测技术的有机结合是未来的一个重要研究方向;另外,将多智能体技术应用于HEV能量管理优化控制也是一个值得研究的方向。     

模糊仿人智能控制器的分析与应用

提出将模糊控制与仿人智能控制相融合的模糊仿人智能控制技术,引入了模糊切换,给出了模糊仿人智能控制器的基本结构和控制算法。分析了二级倒立摆系统的运动特性,在MAT-LAB中进行了针对二级倒立摆的模糊仿人智能控制系统的建模与仿真。     

基于FPGA的模糊自整定PID控制器的研究

提出了一种基于VHDL描述、FPGA实现的模糊自整定PID控制器设计方法。首先，借助Matlab系统仿真工具，优化得出模糊自整定PID参数的模糊推理规则和控制器算法结构。然后，进行控制器的VHDL分层设汁，作为单一控制器芯片，重点编程实现和时序仿真：模糊逻辑推理、模糊自整定PID算法、数据缓存和I／O接口控制。最后，在一个具体的FPGA芯片上实现该控制器，并在此基础上进行系统实验。实验结果表明：FPGA作为单一控制器实现模糊自整定PID控制编程规范、时序验证方便、系统修改灵活，且基本无须改动硬件，是实现单片或小系统智能控制策略的一种新的有效途径。     

A novel optimized hybrid fuzzy logic intelligent PID controller for an interconnected multi-area power system with physical constraints and boiler dynamics

In the fast developing world nowadays, load frequency control (LFC) is considered to be a most significant role for providing the power supply with good quality in the power system. To deliver a reliable power, LFC system requires highly competent and intelligent control technique. Hence, in this article, a novel hybrid fuzzy logic intelligent proportional-integral-derivative (FLiPID) controller has been proposed for LFC of interconnected multi-area power systems. A four-area interconnected thermal power system incorporated with physical constraints and boiler dynamics is considered and the adjustable parameters of the FLiPID controller are optimized using particle swarm optimization (PSO) scheme employing an integral square error (ISE) criterion. The proposed method has been established to enhance the power system performances as well as to reduce the oscillations of uncertainties due to variations in the system parameters and load perturbations. The supremacy of the suggested method is demonstrated by comparing the simulation results with some recently reported heuristic methods such as fuzzy logic proportional-integral (FLPI) and intelligent proportional-integral-derivative (PID) controllers for the same electrical power system. the investigations showed that the FLiPID controller provides a better dynamic performance and outperform compared to the other approaches in terms of the settling time, and minimum undershoots of the frequency as well as tie-line power flow deviations following a perturbation, in addition to perform appropriate settlement of integral absolute error (IAE). Finally, the sensitivity analysis of the plant is inspected by varying the system parameters and operating load conditions from their nominal values. It is observed that the suggested controller based optimization algorithm is robust and perform satisfactorily with the variations in operating load condition, system parameters and load pattern.