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.     

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     

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。     

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.     

非对称泵缸系统模型跟踪控制研究

用神经网络逼近非对称泵缸系统的非线性逆模型，通过神经网络模型参考控制实现电液斜盘位置和流量的控制，使系统的不确定性和非线性等得到补偿。仿真和实验表明，所开发的神经网络控制器能够较好地实现模型跟踪控制，有较好的自适应性和鲁棒性，跟踪性能有较大改善。     

焦化鼓风机系统智能控制策略研究及应用

针对焦化鼓风机系统具有非线性时变、多变量、强耦合及存在随机干扰的特点,通过采用基于最近邻聚类方法的RBF神经网络快速学习算法,实时在线辨识,建立被控对象的精确逆模型并用于控制,实现了将具有强耦合特性的多输入多输出(MIMO)系统解耦成单个独立的伪线性对象,并提出一种基于RBF神经网络逆控制与非线性比例积分微分(PID)控制相结合的智能控制策略,保证了系统稳定的同时改善了控制系统性能.仿真和应用结果证实了该控制策略具有快速适应对象和过程变化的能力及较强的鲁棒性.     

基于多传感器的工业机械臂精细化操作远程控制方法

为实现远程控制工业机械臂时的精细化操作,使其关节轨迹具有连续、平稳、光滑的控制效果,提出基于多传感器的工业机械臂精细化操作远程控制方法。优化传感器的布局,以便采集信息;利用新息变化野值检测方法消除工业机械臂精细化操作中存在的野值,提高关节角度与速度信息的完整性和精度;将滑模控制与神经网络结合,消除因非线性、摩擦非线性和未知参数等不确定性因素对机械臂精细化操作的影响,构建工业机械臂操作远程控制器,实现工业机械臂精细化操作的远程控制。实验结果表明,所提方法可精准控制工业机械臂的关节角度与速度,具有较高的灵活性和高效性。     

Robust control of robot manipulator with actuators

A Robust controller is designed for cascaded nonlinear uncertain systems that can be decomposed into two subsystems; that is, a series connection of two nonlinear subsystems, such as a robot manipulator with actuators. For such systems, a recursive design is used to include the second subsystem in the robust control. The recursive design procedure contains two steps. First, a fictitious robust controller for the first subsystem is designed as if the subsystem had an independent control. As the fictitious control, a nonlinearH∞ control using energy dissipation is designed in the sense ofL ₂-gain attenuation from the disturbance caused by system uncertainties to performance vector. Second, the actual robust control is designed recursively by Lyapunov’s second method. The designed robust control is applied to a robotic system with actuators, in which the physical control inputs are not the joint torques, but electrical signals to the actuators.     

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.     

用于气动伺服系统的自适应神经模糊控制器

研究了一种基于压力比例阀的气动伺服系统自适应神经模糊控制器。其中的神经网络辨识器（NNI）通过高线训练可以充分逼近非线性动态系统的模型,并能够在线调整模糊控制器的控制规则。系统的位置控制精度和伺服特性有了很大改善。试验结果表明,所提出的控制器对该气动伺服系统具有很好的控制特性以及很强的自适应能力。     

气动人工肌肉驱动单关节机械臂的自抗扰控制

针对气动人工肌肉驱动单关节机械臂存在严重的非线性问题,提出一种自抗扰控制策略,来改善单关节机械臂的控制效果。对于给出的不精确系统模型,首先利用跟踪微分器安排输入信号的过渡过程,从而有效地解决了系统的快速性和超调之间的矛盾;其次利用扩张状态观测器观估计出系统状态以及系统的非线性和外部扰动,并对其进行补偿;最后设计了带扩张状态补偿的非线性误差反馈控制器来保证系统的闭环响应性能。实验结果表明,该控制方法在气动单关节机械臂关节关节角度控制方面具有良好的控制效果。     

基于神经网络控制算法的气动伺服系统运动分析研究

提出了一种神经网络控制方法并通过对气动伺服系统的无杆气缸运动控制,探究此控制方法的控制精度。由于受空气可压缩性、摩擦力以及启动系统的扰动等非线性因素的影响,气动伺服系统很难去建立精确的数学模型。根据系统的非线性特点及PID控制不足,基于BP神经网络控制,设计神经网络PID控制器,并进行实验。通过实验,对无杆气缸的运动特性分析,表明这种控制策略可以更好控制气动伺服系统的运动精度。     

基于FPGA的气动柔性手指的神经网络控制器设计

研究了气动柔性手指智能控制策略的硬件实现，设计了气动柔性手指的神经网络控制器。气动柔性手指神经网络控制器是在已经对气动柔性手指进行运动学和动力学分析的前提下，使用MATLAB对神经网络进行试训，依据训练所得的权值和阈值，在现场可编程门阵列集成环境下，基于超高速集成电路硬件描述语言完成的。该控制器适用于具有复杂智能控制策略且对实时性要求高的多手指控制系统。     

Neural network controller for robotic motion control

Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations.     

Machining force control with intelligent compensation

Force control is an effective means of improving the quality and efficiency of machining operations, so various approaches for force control have been proposed. However, due to the nonlinear, time-varying and uncertain characteristics of machining processes, it is difficult to develop force control systems that are stable and robust over the full range of operating conditions. This study proposed two control schemes to address such difficulties in the field of nonlinear force control by using a linear feedback proportional-derivate (PD) controller respectively with two different nonlinear intelligent compensators: fuzzy logic compensator (FLC) and neural network compensator (NNC). The PD controller is used to improve the transient response while maintaining the stability of the process system, and the FLC or NNC is employed to eliminate the steady-state error and compensate for the system nonlinearity (or uncertainty). The applications of the proposed schemes in machining processes show that the controllers adapt well to nonlinearity under time-varying cutting conditions in comparison to PID, PD, and FLC. The online updating of the NNC parameters through the Feedback-Error Learning can further improve the system performance.     

基于神经网络的超磁致伸缩智能构件滑模控制

提出了一种利用超磁致伸缩材料（giant magnetostrictive material GMM）智能构件精密加工活塞异形孔方法。 为了消除GMM智能构件迟滞非线性影响,提出一种神经网络前馈复合离散滑模变结构控制策略,实现GMM智能构件的精密位移控制。将智能构件的输出位移及其变化率作为小脑模型神经网络（CMAC）输入,构件的输入电流作为网络输出,利用CMAC在线自学习能力建立GMM智能构件的迟滞逆模型,神经网络的建模近似误差以及外界干扰通过离散滑模变结构控制器来消除。仿真结果表明此控制策略能在线建立智能构件的迟滞逆模型,消除迟滞非线性的影响,可实现智能构件的精密位移控制。     

Trajectory tracking control of a pneumatic X-Y table using neural network based PID control

This paper deals with the use of Neural Network based PID control scheme in order to assure good tracking performance of a pneumatic X-Y table. Pneumatic servo systems have inherent nonlinearities such as compressibility of air and nonlinear frictions present in cylinder. The conventional PID controller is limited in some applications where the affection of nonlinear factor is dominant. In order to track the reference model output, the primary control function is provided by the PID control and then the auxiliary control function is given by neural network for learning and compensating the inherent nonlinearities, A self-excited oscillation method is applied to derive the dynamic design parameters of a linear model. The experiment using the proposed control scheme has been performed and a significant reduction in tracking error is achieved.     

气动机械手稳定运动的控制策略综述

气动机械手由于具有驱动介质来源方便、成本低、工作环境要求低等优点,已经在自动搬运、自动上下料等场合中有着广泛的应用;但是由于自身非线性特性,其工作速度稳定性较差。从模糊控制、神经网络控制以及鲁棒控制3个方向分析了气动机械手稳定运动控制策略的研究及应用,发现模糊控制和神经网络控制发展较为迅速,与其他控制理论融合的效果良好且实际应用比较成熟,而鲁棒控制发展较为缓慢且应用较少。根据现有研究成果推测,气动机械手稳定运动控制策略在未来将继续沿着控制理论融合与补充的趋势发展,为气动机械手在复杂动态环境的应用提供更精准、更稳定的控制方案。     

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     

重载大惯性液压驱动系统的神经网络近似逆控制

针对重载大惯性液压驱动系统,考虑系统的强非线性、模型不确定性和工作点的变化,设计了系统的神经网络近似逆控制器.该系统逆控制器可以直接从辨识所得的神经网络模型中得到,因而只需要训练一个神经网络.对大惯性重载非线性液压驱动系统的控制仿真研究表明,与传统PID控制器相比,神经网络近似逆控制器具有更好的动态控制性能,对模型不确...