Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

主动电磁轴承智能积分型自适应模糊控制器

将传统的PID控制器或者传统复合型模糊控制器应用到主动电磁轴承系统中,其动、静态特性之间存在着一定的矛盾,较难达到理想的控制效果。将自适应模糊控制器和智能积分器相结合,提出了智能积分型自适应模糊控制器,以一个单自由度主动电磁轴承模型为例,比较了智能积分型自适应模糊控制器、传统模糊控制器和传统PID控制器的控制性能,并进行了仿真。仿真结果证明了智能积分型自适应模糊控制器的优越性。     

Effective fast-response pressure control for thin-wall gas-assisted injection molding

As compared with the traditional injection molding, gas-assisted injection molding (GAIM) exhibits more advantages such as requiring lower injection pressure, reducing part weight, minimizing residual stress, and improving part quality, etc. When GAIM is applied to thin-wall parts, the required filling time (usually less than 0.3 seconds) becomes a critical factor for successful molding. In this study, a new pressure control system that operates with much less response time than what most commercial gas-assisted units need was developed. The new control system consists of a fast interpolation device for fast approaching the set pressure and a fast-response pressure control valve incorporated with a PID controller to accelerate and fine-tune the steady output. To verify the effectiveness and performance of the proposed system, several experiments were conducted. The experimental results have shown that the proposed control system can effectively shorten the response time so that the filling time can be less than 0.3 s to provide good quality for the thin-wall injected products.     

基于PLC和WinCC的水箱液位模糊控制研究与应用

采用S7-300的CPU315-2DP设计了一个单容水箱的液位模糊控制器,该模糊控制器克服了传统PID控制对被控对象精确数学模型的依赖.通过查询预先计算出的模糊控制量表直接实现智能控制.通过在WinCC中的参数连接与设置,实现液位运行画面的监测.实验结果表明该控制系统运行可靠有效,其鲁棒性明显优于传统的PID控制方法.     

基于神经网络的多值模糊控制系统研究

对于难以用精确数学模型描述的多变量非线性复杂控制系统,靠传统控制理论难以获得理想的控制效果,基于模糊神经网络控制的技术不依赖于被控对象精确的数学模型,且能根据被控对象参数的变化自适应调节控制规则和隶属函数参数的特性本文介绍了模糊神经网络控制器的应用研究,经过仿真实验证明该控制器能够获得较理想的控制效果。     

Grey fuzzy PI control for packing pressure during injection molding process

This study presents a grey fuzzy PI (GFPI) controller for packing pressure control during the injection process. The novel controller was designed for solving the problems of large overshoot, static error, and long delay time on servo motor-driven injection molding machines. Based on reasonable assumptions, a mathematical model of injection system was established. According to the nonlinear, severe interferences, and long delay time characteristics in injection process, this paper integrates predictive grey system, robust fuzzy ratiocination, and PI control. Using these mathematical model and control algorithms, a GFPI controller is implemented into an MCU using C programming techniques. The integral discrete PID controller and solid fuzzy controller were realized for contrast experiments. The experimental results show that GFPI controller had better performance on reducing overshoot and static error, increasing both response rate and repeatable accuracy. Such a developed technology would provide helpful references for designing the controller of energy-saving servo motor-driven injection molding equipment.     

A Self-Organising Fuzzy Logic Controller for a Coordinate Machine

For a 3D coordinate measurement system, the dynamic accuracy of the moving table will influence the measuring accuracy directly. If a classical PID controller were designed for this measuring table without an accurate mathematical model, the gain parameters may need to be regulated frequently by trial-and-error to obtain the precise motion control objective, good adaptability, and robustness. In this paper, a model-free fuzzy controller and a self-organising fuzzy controller (SOFC) were employed to eliminate the above controller design problems and improve the tracking control accuracy. The control performances of these intelligent control strategies were compared, based on the experimental results. The SOFC has the best tracking accuracy and its learning ability significantly reduces the trial-and-error design effort of a traditional fuzzy controller.     

Effective fast-response pressure control for thin-wall gas-assisted injection molding

As compared with the traditional injection molding, gas-assisted injection molding (GAIM) exhibits more advantages such as requiring lower injection pressure, reducing part weight, minimizing residual stress, and improving part quality, etc. When GAIM is applied to thin-wall parts, the required filling time (usually less than 0.3 seconds) becomes a critical factor for successful molding. In this study, a new pressure control system that operates with much less response time than what most commercial gas-assisted units need was developed. The new control system consists of a fast interpolation device for fast approaching the set pressure and a fast-response pressure control valve incorporated with a PID controller to accelerate and fine-tune the steady output. To verify the effectiveness and performance of the proposed system, several experiments were conducted. The experimental results have shown that the proposed control system can effectively shorten the response time so that the filling time can be less than 0.3 s to provide good quality for the thin-wall injected products.     

New modifying self-organizing fuzzy controller for robotic motion control

The precise motion control of robotic manipulators is important in improving productivity and quality. However, robotic manipulators are multivariable nonlinear dynamic systems. Designing a model-based controller for robotic system control is difficult because its mathematical model is hard to accurately establish. This study proposed a self-organizing fuzzy controller (SOFC) to control a robotic system and evaluate its control performance. The SOFC continually updates the learning strategy in the form of fuzzy rules during the control process. The learning rate and the weighting distribution value of the controller are hard to regulate, so its fuzzy control rules may be modified to such an extent that the system response generally causes oscillatory phenomena. Two fuzzy logic controllers were designed according to the system output error and the error change, and introduced to the SOFC to determine the appropriate parameters of the learning rate and the weighting distribution, in order to eliminate this oscillation. This new modifying self-organizing fuzzy controller (NMSOFC) can effectively improve the control performance of the system, reduce the time consumed to establish a suitable fuzzy rule table, and support practical and convenient fuzzy controller applications. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old robot for a control system to evaluate the feasibility of motion control. Experiment results indicate the NMSOFC has better control performance in reducing the tracking errors of the joint-space trajectories and the positions, and requires less computational time than does the traditional fuzzy controller.     

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。     

基于单片机的注塑机温控系统设计

塑料注塑成型机的诸多输入、输出参数中,料筒温度是工作过程中的重要被控参数。论文针对传统注塑机温控系统存在的超调量大、调节时间长等诸多不足,提出了以单片机为控制核心,应用模糊控制理论,设计出模糊控制器,实现注塑机温度的实时控制,对于提高注塑机温度控制精度具有较好的意义。     

ARM7参数自整定模糊PID控制器的仿真及设计

常规PID控制器参数设定之后,运行环境改变时不能实现参数的在线整定,这样会影响系统的控制效果。本设计以误差e和误差变化率ec作为输入,经过一定的模糊推理规则,对PID控制器的参数进行自动整定。在Matlab环境下对系统进行了仿真,从仿真的结果可以看出,添加模糊控制环节后,系统的动静态性能得到了提高。同时基于ARM7处理器完成了该控制器的软硬件设计。     

气辅注气控制系统中的多模态智能PID调节器

气体辅助注射成型机的注气控制系统是一小延迟、小惯性系统。为使之能满足气体辅助注射成型工艺对气体压力的高精度控制要求，设计了一种多模态智能PID调节器。该调节器将PID控制方法和智能控制技术相结合，采用了改良型的位置式PID控制算法和归一参数在线自整定方法对控制规律和参数进行实时修正。实验证明，该PID调节器具有波动小、超调小、对环境及控制参数不敏感等优点。     

压缩空气智能节能系统的设计与实现

介绍了基于模糊PID控制的空气压缩机智能节能控制系统的设计与实现,详细阐述了该系统的模糊PID控制器、硬件以及软件的开发。实际应用表明,该系统具有智能化较高、运行稳定、节能效果显著等优点。     

智能模糊给水控制器的设计开发

给出一种智能模糊给水控制器的设计方法,讨论了该控制器的硬件电路结构和模糊控制方法,克服了传统PID控制中稳定性差、参数调整困难的问题。同时,介绍了控制器采用的无线数据传输模块和通信协议,可用于现有给水控制器的升级改造。     

Improvement in automatic generation control of two-area electric power systems via a new fuzzy aided optimal PIDN-FOI controller

Automatic generation control (AGC) executes a vital role to supply quality power in an interconnected power system. To cultivate good quality of power supply via preserving area frequency and tie-line power oscillations following consumer's load demand disturbances, the controller designed for AGC of power system should display excellent disturbance rejection expertise. Hence, in this paper, a maiden attempt is made to propose a fuzzy aided integer order proportional integral derivative with filter-fractional order integral (FPIDN-FOI) controller for AGC of multi-area power systems. A more recent intelligent optimization technique termed as imperialist competitive algorithm (ICA) is fruitfully employed for concurrent tuning of various parameters of the proposed controller. It is observed from the simulation results that the proposed FPIDN-FOI controller outperforms the various existing control strategies and PID/PIDN/FPIDN controller designed in the study for five different power system models. Effect of variation in fractional order value of integral on the system performance is analyzed. A sensitivity analysis is conducted to test the robustness of the designed controller under variations in the system parameters, load demands and existence of the system nonlinearities. It is perceived that the proposed controller is robust and executes adequately under variations in system parameters, random load disturbance patterns and nonlinearities.     

自主移动机器人巡线控制系统设计与实现

针对自主移动机器人传统巡线控制中存在的不足,主要完成了机器人控制系统的设计。在使用灰度传感器采集地面轨迹信息的同时,引入角度传感器对行进方向的角度信息进行采集;设计了PID控制加模糊控制的复合控制器,并给出复合控制器算法。在此基础上建立实验系统,仿真结果证明:该控制系统不仅克服了传统巡线控制中单一传感器采集信息不全的缺点,而且有效解决了机器人在遇到大信号时传统PID控制响应时间长、系统不稳定的问题。     

基于模糊PID的永磁同步电机矢量控制仿真

基于传统固定增益PID（Proportion—Integral—Derivative）的永磁同步电机矢量控制系统,存在着响应速度不快、稳态性能较差、转矩脉动较大的缺陷．针对这一问题,利用具有参数自整定功能的模糊PID控制器对矢量控制系统进行改进,并在MATLAB／Simulink环境下建立了系统仿真模型．仿真结果表明：模糊PID控制器可显著提高系统鲁棒性,很好地满足了永磁同步电机（PMSM）高精度、快响应的控制要求．     

High pressure common rail injection system modeling and control

In this paper modeling and common-rail pressure control of high pressure common rail injection system (HPCRIS) is presented. The proposed mathematical model of high pressure common rail injection system which contains three sub-systems: high pressure pump sub-model, common rail sub-model and injector sub-model is a relative complicated nonlinear system. The mathematical model is validated by the software Matlab and a virtual detailed simulation environment. For the considered HPCRIS, an effective model free controller which is called Extended State Observer – based intelligent Proportional Integral (ESO-based iPI) controller is designed. And this proposed method is composed mainly of the referred ESO observer, and a time delay estimation based iPI controller. Finally, to demonstrate the performances of the proposed controller, the proposed ESO-based iPI controller is compared with a conventional PID controller and ADRC.