Intelligent phase plane switching control of a pneumatic muscle robot arm with Magneto-Rheological Brake

Pneumatic cylinders are one kind of low cost actuation sources which have been applied in industrial and robotics field, since they have a high power/weight ratio, a high-tension force and a long durability. To overcome the shortcomings of conventional pneumatic cylinders, a number of newer pneumatic actuators have been developed such as McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle (PAM) Manipulators. 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. In addition, the nonlinearities in the PAM manipulator still limit the controllability. Therefore, it is not easy to realize motion with high accuracy and high speed and with respect to various external inertia loads. To overcome these problems, a novel controller which harmonizes a phase plane switching control method (PPSC) with conventional PID controller and the adaptabilities of neural network is newly proposed. In order to realize satisfactory control performance a variable damper, Magneto-Rheological Brake (MRB), is equipped to the joint of the robot. The mixture of conventional PID controller and an intelligent phase plane switching control using neural network (IPPSC) brings us a novel controller. The experiments were carried out in a robot arm, which is driven by two PAM actuators, 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 1PPSC and without regard for the changes of external inertia loads.     

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.     

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.     

Improvement of the control performance of pneumatic artificial muscle manipulators using an intelligent switching control method

Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are factors that could be potentially exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external inertia load in the pneumatic artificial muscle manipulator. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is newly proposed. This estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.     

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     

基于PID+ESO控制器的气动机械手控制仿真分析及试验研究

针对现实中的气动机械手存在的停滞及控制精度不高等缺点,为了使气动机械手具有更好的运动轨迹跟踪特性,降低非线性,通过分析PID+ESO控制器原理,对采用PID控制和扩张状态观测器PID控制下的气动机械手进行了理论研究。通过对控制器的设计,在MATLAB/Simulink软件上分别对两种不同的控制方法进行仿真,得到各轴的目标跟踪曲线和跟踪误差曲线。将此控制器原理应用于气动试验台,得出基于扩张状态观测器PID控制下的误差较低。应用于直角坐标气动机械手响应速度快、控制精度高。     

基于nRF905的气动肌肉机械手远程遥控实现

为了实现气动肌肉机械手远距离遥感控制,设计了一种基于nRF905无线收发芯片和C8051F020单片机的远程遥控系统,根据人体手臂结构和运动方式特点搭建了一种多自由度气动肌肉机械手样机,并将远程遥控系统作用于气动肌肉机械手样机上,最后对远程遥控系统的控制区域和气动肌肉机械手关节运动进行了实验研究。实验结果表明,该远程遥控系统在室外700 m和室内100 m区域内能够较好的对气动肌肉机械手进行远程控制,并且运行可靠。     

气动肌肉并联关节的位姿轨迹跟踪控制

针对多输入多输出的气动肌肉并联关节,建立包含任务空间负载动态方程、容腔压力动态方程和高速开关阀平均流量方程的多阶动态系统数学模型。为保证气动肌肉并联关节系统良好动态特性的同时具有高精度的位姿轨迹跟踪,采用基于非连续投影算法的自适应鲁棒控制策略。该策略通过自适应参数估计来消除因气动肌肉并联关节系统动态数学模型的参数未知而引起的较大参数不确定,通过鲁棒反馈来消除因气动肌肉的伸缩力模型误差、摩擦力时变和关节系统的不可知干扰等引起的严重非线性不确定,且控制器基于反推设计,对多输入多输出的多阶耦合动态系统具有很好的适用性。试验结果表明：所研究的气动肌肉并联关节阶跃响应的静态误差小于0.09°,连续轨迹跟踪的标准误差小于0.15°,且具有较强的自适应性和鲁棒性。     

空间柔性伸杆机构振动抑制的半物理仿真试验

研究空间柔性伸杆机构的振动抑制问题,提出一种基于伸杆机构自适应指令整形(Adaptive input shaper, AIS)与航天器本体输出反馈控制相结合的复合控制策略,基于经验传递函数估计方法(Empirical transfer function estimate, ETFE),建立最优指令整形器(Optimal arbitrary input shaper, OAIS)的自调整机制,得到改进的自适应指令整形器。以金字塔构型的单框架控制力矩陀螺簇(Single gimbal control moment gyros, SGCMGs)为本体执行机构,详细设计基于实时仿真机、PMAC实时运动控制板卡、SGCMGs等实物的半物理仿真试验平台。在考虑环境干扰力矩以及执行机构控制受限的情况下对控制方法进行半物理仿真试验验证。结果表明：与基于OAIS 及无指令整形前馈控制的复合控制方法相比,提出的控制方法可有效抑制伸杆机构的低频振动,实现航天器本体姿态的精确跟踪和伸杆机构的高精度指向控制。     

Development of a piezoelectric high speed on/off valve and its application to pneumatic closed-loop position control system

The use of smart materials-based high speed on/off valve has exhibited the potential to replace traditional servo and proportional valves in fluid power systems. In this paper, a novel pneumatic high speed on/off valve driven by a piezoelectric stack actuator is developed and its dynamics are studied. For an upstream gauge pressure of 0.5 MPa, the valve exhibited a large flow rate of up to 86 L/min. Furthermore, to study the ability of this novel high speed on/off valve to be applied in closed-loop control systems, a real-time position control system is realized using a PID controller. In order to prove its feasibility and effectiveness, a number of closed-loop trajectory tracking experiments are conducted on a pneumatic cylinder. The system is proved feasible and the results demonstrate good tracking performance.

     

气动肌肉及其构成的关节模型研究

气动肌肉是一种具有重量轻、功率—重量比高的新型气动柔性执行器。用变刚度弹簧模型来近似气动肌肉的特性 ,研究了气动肌肉的刚度特性 ,并分析了由一对气动肌肉构成的关节模型 ,最后通过实验对模型进行了验证。     

STUDY ON THE CONTROL OF SINGLE PNEUMATIC MUSCLE ACTUATOR

The control of single pneumatic muscle actuator is studied, as one basic part of research onthe parellel-robot arthrosis actuated by pneumatic muscle actuators. Experiments show that aself-modified fuzzy-PID controller is obviously effective for its position servo and a simple PID con-troller is good for its force track.     

气动人工肌肉驱动灵巧手的设计与研究

针对气动人工肌肉推广应用的问题,提出了一种轻小型气动人工肌肉的制作方案,并对其不同压力下的力学性能进行了试验和测试。试验结果表明,该气动人工肌肉具有高收缩率和大输出力的特性。基于该气动人工肌肉的运动特性,设计了一款欠驱动的多自由度灵巧手,并进行了多项抓取实验,验证了其多种状况下的工作能力。基于气动人工肌肉的灵巧手具有适应性强和抓取力大的特点,对气动人工肌肉的应用和机器人末端执行器的开发具有参考价值。     

灰色预测模糊PID控制在调节阀智能定位系统中的应用

针对气动调节阀系统大惯性、纯滞后和时变性的特点,建立了调节阀执行机构的二阶加纯滞后模型,在此基础上设计了基于灰色预测模糊PID算法的复合控制器。PID模块作为该复合算法的基础,用来减小系统偏差,提升动态性能;模糊控制模块对系统偏差及其变化率进行模糊处理,输出值作为PID参数的调整量,实现在线调整控制参数,克服时变性对系统的影响;灰色预测模块对系统输出数据序列进行提取并预测未来发展趋势以进行系统的“超前控制”,进一步解决气动系统滞后和稳态性能差的问题。基于灰色预测模糊PID算法的复合控制器将灰色预测模型、模糊控制和比例微积分算法相结合,可实现阀门位置的高精度调节与智能控制。仿真结果表明,灰色预测模糊PID控制算法相比常规PID以及模糊PID具有更快的响应速度、更高的调节精度以及更好的稳态性能。     

一种混合驱动式多关节双机械臂的设计与实验

基于机器入学和仿生学,设计了一种基于气动人工肌肉和步进电机混合驱动的多关节双机械臂,结构上兼具柔顺性和紧凑性,完成了多关节双机械臂气动回路和控制系统硬件的设计.基于VC+ +编程,开发了相应的控制系统软件和实现了用计算机对多关节双机械臂的位置协调控制.样机实验验证了多关节机械臂具有良好的位置精度和协调运动能力.     

气动肌肉主动悬架系统的仿真研究

以气动肌肉为新型执行器构建车用主动悬架系统，依据1／4悬架模型搭建实验平台，研究气动肌肉主动悬架系统的减振性能及其控制特点。主要在建立该系统数学模型的基础上，运用：Matlab Simulink软件，采用PID控制算法，对不同的激励信号作用下的系统性能进行了仿真研究，以验证气动肌肉主动悬架系统的可行性与有效性。     

直线电磁驱动串并联阵列人工肌肉设计研究

在分析动物骨骼肌的结构和运动形式的基础上,设计了基于电磁力的类肌肉肌纤维多肌小节串并联构成的阵列式人工肌肉。首先对构成人工肌肉的驱动器进行了性能对比,而后对类肌小节驱动器电磁场进行了有限元仿真分析,并完成了样机制作、不同负载及不同控制策略的多组开闭环实验测试。通过分析样机实验数据可知,该方案具有响应速度高、体积小、质量小、加速度高等特点。     

Active vibration control based on modal controller considering structure-actuator interaction

Active vibration control to suppress structural vibration of the flexible structure is investigated based on a new control strategy considering structure-actuator interaction. The experimental system consists of a clamped-free rectangular plate, a controller based on modal control switching, and a magnetostrictive actuator utilized for suppressing the vibrations induced by external excitation. For the flexible structure, its deformation caused by the external actuator will affect the active control effect. Thus interaction between structure and actuator is considered, and the interaction model based on magnetomechanical coupling is incorporated into the control system. Vibration reduction strategy has been performed resorting to the actuator in optimal position to suppress the specified modes using LQR (linear quadratic regulator) based on modal control switching. The experimental results demonstrate the effectiveness of the proposed methodology. Considering structure-actuator interaction (SAI) is a key procedure in controller design especially for flexible structures.     

Multimodel PD-control of a pneumatic actuator under variable loads

In this paper, a multimodel controller is designed and implemented to a variably loaded pneumatic actuator. A particular design of the pneumatic system with one PWM-driven switching valve is utilized which has a quasilinear input–output behavior under constant load conditions. Therefore, under a constant load, position control is properly accomplished by a linear PD-controller. In order to use the system in variable load applications such as robot actuators, a multimodel controller is designed based on the PD-controllers. For this purpose, a number of constant loads are considered and corresponding to each load, a linear model is identified and a PD-controller is tuned for the system. Then, a switching algorithm is applied which determines the best model and selects the corresponding controller in any load condition. Also, for the realization of the D-action in PD-controllers, a Kalman filter is designed to observe the velocity instead of direct differentiation of the output position. Experimental results indicate the high performances of the multimodel controller under variable load conditions.     

广义执行器馈能主动悬架研究

为实现车辆悬架的主动控制以及悬架振动能量的回收,提出一种基于广义执行器的馈能主动悬架。研究电磁式、液电式、气压式三种常见馈能主动悬架的特点,忽略馈能主动悬架模型的物理意义,从数学角度对广义执行器的广义功/能以及广义力进行分析,建立统一的简洁的数学模型。结合LQR和遗传算法(GA)设计控制器,实现振动能量回收,改善车辆平顺性。仿真结果表明,基于广义执行器模型的馈能主动悬架,能够改善车辆的平顺性,并且能获得15%以上的馈能效率。