Polishing work of a freely-curved surface die demands simple and repetitive operations but requires a considerable amount of time for high precision. Some workers tend to gradually avoid the polishing work because of the poor environment caused by dust and noise. In order to reduce the polishing time and solve the problem of the shortage of skilled workers, an automatic polishing system should be developed. In this study, a user-friendly automatic polishing system was developed. It is composed of two different systems: a three-axis machining center and a two-axis polishing robot. The developed polishing system with five degrees of freedom is able to keep the polishing tool normal to the die surface. In order to operate the polishing system, the host computer should transmit the polishing data to the FANUC controller of the machining center and DSP controller of the polishing robot, respectively. A driving program (P-PROS: Pusan National University-polishing robot operating system) in the Windows environment was developed to easily operate the polishing robot system. A communication program was developed for transmitting the polishing data to each controller. To evaluate the performance of the P-PROS and the developed DSP (digital signal processing) controller, a polishing experiment on the shadow-mask die was done.
Proposes a self tuning fuzzy inference method based on genetic algorithms for the fuzzy-sliding mode control of a robot. Using this method the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method, and it is guaranteed that the selected solution becomes the global optimal solution by optimizing Akaike's information criterion. A trajectory tracking experiment with the automatic polishing robot system shows that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.
In spite of the nonlinear characteristic of cable pulley mechanism, surgical robot has adopted it for safety. The sliding mode control with sliding perturbation observer has been applied to surgical robot instrument because of the robustness of the control algorithm. In the previous study, it was found that the output of perturbation observer followed disturbance such as loaded on the tip very similarly. Thus, this paper confirms that the perturbation observer in SMCPSO controller is sufficient estimator which finds out the mount of loaded force on the robot end-effect. To prove the proposition, the dynamic of surgical robot instrument will be analyzed, and used to make simulation test which shows that the observer could overcome the nonlinearity in the system. The simulation results show that the sliding perturbation observer can substitute for tactile force sensor to build haptic system. It means that the perturbation observer contributes to build sensorless surgical robot haptic system.
The active magnetic bearing system has been studied for long period. Comparing with long research history, the AMB application into industrial field is shown slowly for various causes. One of primary factor is to make up exclusive controller which can generate fast linear current output. Thus, this paper developed the exclusive AMB controller mounted high speed DSP which can operate so fast control calculation that improve system response ability. Especially, to consider the fusion of AMB system and control software, the development is conducted in HILS system with dSPACE from the beginning. Although HILS system is adopted, the developed ABM controller simplified the whole system and could make up optimized control algorithm promptly by measuring and applying the system gain and characteristics of them monitored by the HILS system in real time.
This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that the selected solution become the global optimal solution by optimizing the Akaike's information criterion expressing the quality of the inference rules. The trajectory tracking simulation and experiment of the polishing robot show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.
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