Intelligent switching control of pneumatic cylinders by learning vector quantization neural network

School of Mechanical and Automotive Engineering, University of Ulsan, San 29, Muger2-dong, Nam-gu, Uhan 680-749, Korea The development of a fast, accuiate, and inexpensive position-controlled pneumatic actuator that may be applied to various practical positioning applications with various external loads is described in this paper A novel modified pulse-width modulation (MPWM) valve pulsing algorithm allows on/off solenoid valves to be used in place of costly servo valves A comparison between the system response of the standardPWM technique and that of the modified PWM technique shows that the performance of the proposed technique was significantly increased A state-feedback controller with position, velocity and acceleration feedback was successfully implemented as a continuous controllei A switching algorithm foi control parameters using a learning vector quantization neural network (LVQNN) has newly proposed, which classifies the external load of the pneumatic actuator The effectiveness of this proposed control algorithm with smooth switching control has been demonstrated thiough experiments with various external loads     

Performance improvement of pneumatic artificial muscle manipulators using magneto-rheological brake

A novel pneumatic artificial muscle actuator (PAM actuator), which has achieved increased popularity to provide the advantages such as high strength and high power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available and cheap power source, inherent safety and mobility assistance to humans performing tasks, has been regarded during the recent decades as an interesting alternative to hydraulic and electric actuators However, some limitations still exist, such as the air compressibility and the lack of damping ability of the actuator bring the dynamic delay of the pressure response and cause the oscillatory motion Then it is not easy to realize the performance of transient response of pneumatic artificial muscle manipulator (PAM manipulator) due to the changes in the external inertia load with high speed In order to realize satisfactory control performance, a variable damper — Magneto-Rheological Brake (MRB), is equipped to the joint of the manipulator Superb mixture of conventional PID controller and a phase plane switching control method brings us a novel controller This proposed controller is appropriate for a kind of plants with nonhnearity, uncertainties and disturbances. The experiments were carried out in practical PAM manipulator and the effectiveness, of the proposed control algorithm was demonstrated through experiments, which had proved that the stability of the manipulator can be improved greatly in a high gain control by using MRB with phase plane switching control method and without regard for the changes of external inertia loads     

气动机械手控制回路设计

介绍一种全气动控制机械手的控制回路,克服了信号-动作(X-D)状态图法、卡诺图图解法等较繁琐的缺点.     

Control of two-axis pneumatic artificial muscle manipulator with a new phase plane switching control method

The use of robots in rehabilitation has become an issue of increasing importance because of the requirement of functional recovery therapy for limbs. A novel pneumatic artificial muscle (PAM) actuator — which has achieved increased popularity for providing safety and mobility assistance to humans performing tasks, as well as providing another advantages such as high strength and power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available, cheap power source, and so on — has been considered during the recent decades for use in a therapy robot, which in particular requires a high level of safety. However, some limitations still exist, such as air compressibility and the lack of damping ability of the actuator to bring the dynamic delay of the pressure response and cause the oscillatory motion. In addition, to aid rehabilitation more efficiently, the robot should adjust its impedance parameters according to the physical condition of the patient For this purpose, the manipulator join is equipped with a Magneto-Rheological Brake (MRB). A new phase plane switching control method using MRB is proposed for tracking sinusoidal waveforms. The effectiveness of the proposed algorithm is demonstrated through an experiment using a fabricated two-axis PAM manipulator. The experiment proves that the stability of the manipulator could be greatly improved using a high gain control without regard to the change of the frequencies of the reference input and the external load condition, and without decreasing the response speed or lowering the stiffness of PAM manipulator.     

A method of accurate position control with a pneumatic cylinder driving apparatus

In this paper, a method of accurate position control using a pneumatic cylinder driving apparatus is presented. To overcome the effect of friction force and transmission line, low friction type cylinder applied externally pressurized air bearing structure is used and two control valves attached both side of the cylinder directly. To compensate nonlinear characteristics of control valves, linearized control input derived from the relation between control input and effective area of control valve, and dither signal are applied to the valve. The controller applied to the pneumatic cylinder driving apparatus is composed of a state feedback controller and a disturbance observer. Experimental results show that the effectiveness of the proposed method and position control error of 5 μm accuracy could be obtained easily.     

Nonlinear PID control to improve the control performance of the pneumatic artificial muscle manipulator using neural network

A novel actuator system which has achieved increased popularity to provide these advantages such as high strength and power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available, cheap power source, inherent safety and mobility assistance to humans performing tasks has been the utilization of the pneumatic artificial muscle (PAM) manipulator, in recent times. However, the complex nonlinear dynamics of the PAM manipulator makes it a challenging and appealing system for modeling and control design. The problems with the time variance, compliance, high hysteresis and nonlinearity of pneumatic systems have made it difficult to realize precise position control with high speed. In order to realize satisfactory control performance, the effect of nonlinear factors contained in thePAM manipulator must be considered. The purpose of this study is to improve the control performance of thePAM manipulator using a nonlinearPID controller. Superb mixture of conventionalPID controller and the neural network, which has powerful capability of learning, adaptation and tackling nonlinearity, brings us a novel nonlinearPID controller using neural network. This proposed controller is appropriate for a kind of plants with nonlinearity uncertainties and disturbances. The experiments were carried out in practicalPAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through the experiments, which suggests its superior performance and disturbance rejection.     

Empirical modeling of dynamic behaviors of pneumatic artificial muscle actuators

Pneumatic Artificial Muscle (PAM) actuators yield muscle-like mechanical actuation with high force to weight ratio, soft and flexible structure, and adaptable compliance for rehabilitation and prosthetic appliances to the disabled as well as humanoid robots or machines. The present study is to develop empirical models of the PAM actuators, that is, a PAM coupled with pneumatic control valves, in order to describe their dynamic behaviors for practical control design and usage. Empirical modeling is an efficient approach to computer-based modeling with observations of real behaviors. Different characteristics of dynamic behaviors of each PAM actuator are due not only to the structures of the PAM actuators themselves, but also to the variations of their material properties in manufacturing processes. To overcome the difficulties, the proposed empirical models are experimentally derived from real physical behaviors of the PAM actuators, which are being implemented. In case studies, the simulated results with good agreement to experimental results, show that the proposed methodology can be applied to describe the dynamic behaviors of the real PAM actuators.     

A QP Artificial Neural Network inverse kinematic solution for accurate robot path control

In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attention in many disciplines, including robot control, where they can be used to solve nonlinear control problems. One of these ANNs applications is that of the inverse kinematic problem, which is important in robot path planning. In this paper, a neural network is employed to analyse of inverse kinematics of PUMA 560 type robot. The neural network is designed to find exact kinematics of the robot. The neural network is a feedforward neural network (FNN). The FNN is trained with different types of learning algorithm for designing exact inverse model of the robot. The Unimation PUMA 560 is a robot with six degrees of freedom and rotational joints. Inverse neural network model of the robot is trained with different learning algorithms for finding exact model of the robot. From the simulation results, the proposed neural network has superior performance for modelling complex robot’s kinematics.     

智能控制分拣系统的研制

利用计算机技术对生产线上的分拣设备进行智能化控制是一种既能节约成本又能在短时间内提升企业生产效率的好方法.引入计算机图像处理技术对工件进行颜色分辨,能够有效地减少人为原因造成的判断失误.     

产品质量智能预测控制研究

介绍了质量预测控制的研究现状,提出了基于质量特征参数序列的质量智能预测控制系统模型,分析了系统的稳定性.根据提出的系统模型利用动态神经网络对加工过程质量特征参数的变化进行了跟踪实验,效果良好.     

Comparative study of modeling and identification of the pneumatic artificial muscle (PAM) manipulator using recurrent neural networks

The paper deals with the PAM manipulator modeling and identification based on autoregressive recurrent neural networks. For the first time, the most powerful types of neural-network-based nonlinear autoregressive models, namely, NNARMAX, NNOE and NNARX models, will be applied comparatively to the PAM manipulator identification. Furthermore, the evaluation of different nonlinear neural network auto-regressive models of the PAM manipulator with different number of neurons in hidden layer is completely discussed. On this basis, the merits of each identified model of the highly nonlinear PAM manipulator have been analyzed and compared. The results show that the nonlinear NNARX model yields better performance and higher accuracy than the other nonlinear NNARMAX and NNOE model schemes. These results can be applied to model and identify not only the PAM manipulator but also to control other nonlinear and time-varying industrial systems.     

三坐标气动机械手气动控制回路设计

以三坐标气动机械手为例子来介绍一种简便、可靠的多气缸顺序动作气动控制白路的设计方法。     

Automatic assembly task of electric line using 6-link electro-hydraulic manipulators

Uninterrupted power supply has become indispensable during the maintenance task of active electric power lines as a result of today’s highly information-oriented society and increasing demand of electric utilities. The maintenance task has the risk of electric shock and the danger of falling from high place. Therefore it is necessary to realize an autonomous robot system using electro-hydraulic manipulator because hydraulic manipulators have the advantage of electric insulation. Meanwhile it is relatively difficult to realize autonomous assembly tasks particularly in the case of manipulating flexible objects such as electric lines. In this report, a discrete event control system is introduced for automatic assembly task of electric lines into sleeves as one of the typical task of active electric power lines. In the implementation of a discrete event control system, LVQNN (linear vector quantization neural network) is applied to the insertion task of electric lines to sleeves. In order to apply these proposed control system to the unknown environment, virtual learning data for LVQNN is generated by fuzzy inference. By the experimental results of two types of electric lines and sleeves, these proposed discrete event control and neural network learning algorithm are confirmed very effective to the insertion tasks of electric lines to sleeves as a typical task of active electric power maintenance tasks.     

基于神经网络的交流伺服控制系统设计

针对PID控制交流伺服系统无法同时获得稳定性和快速性,提出了一种基于神经网络的交流伺服控制系统设计方法.在交流伺服控制系统中,采用改进的BP神经网络作为系统控制器,利用其极强的非线性动态跟踪能力和自适应学习能力,及对控制对象的数学模型无依赖性,实现控制对象快速、准确定位.系统仿真表明,BP网络控制器具有快速跟踪性和较好的控制精度等优点.     

The use of models in the motion control of pneumatic and electric drives

With the aim of achieving improved motion control, modelling and control methods for pneumatic and electric drives are considered. The use of both conceptual and analytic models for controlling single-degree-of-freedom pneumatic and electric modules is reported, supported by experimental results. The approaches described relate to a research programme which has evolved a novel family of modular machine elements that can be aggregated to form a wide variety of types of manufacturing machine.     

A study on the speed control performance of a servo-pneumatic motor and the application to pneumatic tools

In this study, the performance of the rotational speed control of a proportional-valve-controlled pneumatic motor is investigated. The main application field of the proposed prototype is the pneumatic tools, which are currently very important manufacturing devices. Generally speaking, it is usually quite difficult to obtain a satisfactory and precise speed control of a servo-pneumatic motor at a low rotational speed because of the nonlinear deadband and stick-slip friction inside the proportional valve. Therefore, the fuzzy-sliding mode controller is proposed. Experiment results show that the fuzzy-sliding mode controller is superior to the traditional PID controller, especially when the command input is set at a low rotational speed. This newly designed prototype may be utilised to develop advanced pneumatic tools.     

Real-Time Simulator based hybrid control of DFIG-WES

This paper presents the analysis and design of recurrent neural network (RNN) and proportional and integral(PI) controller based hybrid control(HC) of doubly fed induction generator (DFIG). The proposed HC shows the quick dynamic and good transient response during the sudden changes in wind speed as well as generator speed. Further performance of proposed hybrid controller has been analyzed independently with RNN and PI control as its components. The HC along with RNN gives effective performance compared to conventional DTC (CDTC) and PI DTC in terms of flux ripples,torque ripples and settling time. The proposed technique is implemented in real-time simulator (RTS) based OPAL-RT and MATLAB/SIMULINK environment at a rating of 5.5 KW system under steeply and randomly change in wind velocity.     

主烘腔温控网络系统的研发

基于LonWorks现场总线技术，提出建立控制网络系统(CNS)，对它的实现策略进行了探讨，并以散热器芯子烘焊自动线控制系统中主烘腔的温度控制为应用对象，建立了基于LonWorks节点的主烘腔温度模糊控制系统，分散的节点不仅有效地实现了温度模糊控制的功能，而且使系统整体性能得到了提高。     

基于PLC的智能PID算法在延伸率控制中的应用

文中主要对铝带拉弯矫直机设备及其延伸率控制原理进行了介绍，详细论述了智能PID控制的控制规则以及使用PLC完成其控制的硬件配置和软件实现。通过实际延伸率智能PID控制的实践，说明所述智能PID控制规则及方法是可行的，且效果是显著的。     

人造金刚石合成工艺的智能控制

采用计算机控制技术对Y-500型六面顶压机生产人造金刚石的整个生产工艺过程进行全方位控制和监测,大大提高了压机运行的可靠性和生产效率.根据工艺要求采用智能PID控制和模糊PID控制策略对人造金刚石生产中的工艺参数加热功率和加压压力实现有效控制,大大提高人造金刚石产品的质量和品位.