Intelligent Parameter Identification for Robot Servo Controller Based on Improved Integration Method

With the rise of smart robots in the field of industrial automation, the motion control theory of the robot servo controller has become a research hotspot. The parameter mismatch of the controller will reduce the efficiency of the equipment and damage the equipment in serious cases. Compared to other parameters of servo controllers, the moment of inertia and friction viscous coefficient have a significant effect on the dynamic performance in motion control; furthermore, accurate real-time identification is essential for servo controller design. An improved integration method is proposed that increases the sampling period by redefining the update condition in this paper; it then expands the applied range of the classical method that is more suitable for the working characteristics of a robot servo controller and reducesthe speed quantization error generated by the encoder. Then, an optimization approach using the incremental probabilistic neural network with improved Gravitational Search Algorithm (IGSA-IPNN) is proposed to filter the speed error by a nonlinear process and provide more precise input for parameter identification. The identified inertia and friction coefficient areused for the PI parameter self-tuning of the speed loop. The experiments prove that the validity of the proposed method and, compared to the classical method, it is more accurate, stable and suitable for the robot servo controller.

The ROS: Kinetic Kame for Humanoid Robot BarelangFC

A collaborative robot such as humanoid robot which able to play soccer consist tons of software framework such as servo controller, vision system, strategy receiver and transmitter, sensors, and coordination system. All these frameworks needed to be integrated to simplify the command of creating the complexity of the robot behaviors. To overcome these problems, the Robot Operating System (ROS) can be implemented on each robot. This paper presented the implementation of the ROS: Kinetic Kame in order to integrated the whole framework which is existed in the robot. To verify the performance of this system, some experiments has been done in real-time application. From the experimental results, the ROS: Kinetic Kame able to integrate each software framework of the robot in very good response.

An investigation on discharge servo parameters and machining servo mode optimization of MS-WEDM

Increasing the machining efficiency of middle speed wire electrical discharge machining (MS-WEDM) is usually through optimizing process parameters, but the discharge servo parameters of the servo controller can also be optimized to achieve the same purpose. This paper develops a multi-mode servo controller for MS-WEDM to investigate discharge servo parameters. Firstly, the structure and control principle of the servo controller are introduced, and the core functions are described in detail. Secondly, it introduces the discharge servo parameters that can be optimized: gap state threshold and feedback period. At the same time, the platform specifications and parameters for optimization experiments are introduced. Thirdly, the experimental scheme and result analysis of parameter optimization are described, and the optimized parameters are obtained. Finally, different machining experiments are carried out for the multi-mode of the servo controller to investigate the effectiveness and practicability of the different machining servo modes. In the parameter optimization and machining experiments, the recording function of the servo controller was used to draw the speed curve, and the servo response effects of different servo modes was obtained.