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.     

电液比例方向控制系统理论与实践研究

针对大惯量、大负荷、低速工况液压系统的滞环及爬行现象,研究了一种基于高速开关阀控制的方向比例控制系统,建立了该系统的数学模型,并进行了计算机仿真研究,在此基础上,提出了以脉冲宽度调制(PWM)技术为核心的电液数字控制方法.     

Design of a two-dimensional proportional solenoid for miniature directional control pneumatic valves

In this paper, a new proportional solenoid invented for pneumatic directional control valves is introduced. The new proportional solenoid has two-dimensional structure and a pivoting armature on which the friction force is inherently negligible. Another advantageous feature of this solenoid is that its mechanical parts can be easily manufactured and assembled. The working principle and design example of the now proportional solenoid, its application to the activation of a 4/3-way directional control valve, and the evaluation of its control performance in a position control loop are reported.     

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.     

Anti-sway position control of an automated transfer crane based on neural network predictive PID controller

In this paper, we develop an anti-sway control in proposed techniques for an ATC system The developed algorithm is to build the optimal path of container motion and to calculate an anti-collision path for collision avoidance in its movement to the finial coordinate Moreover, in order to show the effectiveness in this research, we compared NNP PID controller to be tuning parameters of controller using NN with 2 DOF PID controller The simulation and experimental results show that the proposed control scheme guarantees performances, trolley position, sway angle and settling time in NNP PID controller than other controller As the results in this paper, the application of NNP PID controller is analyzed to have robustness about disturbance which is wind of fixed pattern in the yard. Accordingly, the proposed algorithm in this study can be readily used for industrial applications