This paper proposes a model predictive controller of robot manipulators using a genetic algorithm to secure the best performance by performing parameter optimization with the genetic algorithm. Genetic algorithm is a natural evolutionary process modeled as a computer algorithm and has excellent performance in global optimization, so it is useful for tuning control parameters. The sliding mode controller and inverse dynamics controller are included in the lower part of the model prediction controller to minimize the problems caused by non-linearity and uncertainty of the robot manipulator. The performance superiority of the proposed method as described above has been confirmed in detail through a simulation study.

This paper deals with the dynamic control of redundant robot manipulator. Traditionally, the kinematic control schemes for redundant robot manipulator were developed from the point of speed and used under the assumption that the dynamic control of manipulator is perfect. However, in reality, the precise control of redundant robot manipulator is very difficult due to their dynamics. Therefore, the kinematic controllers for redundant robot manipulator were employed in the acceleration dimension and may be combined with the computed torque method to achieve the accurate control performance. But their control performance is limited by the accuracy of the manipulator parameters such as the link mass, length, moment of inertia and varying payload. Hence in this paper, the proportional and derivative control gains of the computed torque controller are optimized by the genetic algorithm on the typical payloads, and the neural network is applied to obtain the proper control gains for arbitrary loads. The simulation results show that the proposed control method has better performance than the conventional control method for redundant robot manipulator.

Robot manipulators are highly nonlinear system with multi-inputs multi-outputs, and various control methods for the robot manipulators have been developed to acquire good trajectory tracking performance and improve the system stability lately. The computed torque controller has nonlinear feedforward control elements and so it is very effective to control robot manipulators. If the control gains of the computed torque controller is adjusted according the payload, then more precise control performance is attained. This paper extends the conventional computed torque controller in the joint space to the Cartesian space, and optimize the control gains for some specified payloads in both joint and Cartesian spaces using genetic algorithms. Also a neural network is employed to have proper control gains for arbitrary payloads using generalization properties of the neural network. Computer simulation results show that the proposed control system for robot manipulators has excellent performance in various conditions.

본 연구는 오이수확기의 매니퓰레이터 개발을 위한 기구학적 분석을 하는 것이다. 매니퓰레이터의 정방향 기구학 및 역방향 기구학 분석을 한 후 실제 장치의 반복오차 측정실험을 통해 이론 값을 검증하였다. 매니퓰레이터는 총 세 개의 링크로서 한 개의 수직링크와 두 개의 호전링크로 구성되어져 있으며, 세 개의 스테핑 모터가 각 관절에 장착되어 링크에 동력을 전달한다. 주요 연구결과를 요약하면 다음과 같다. D-H Parameter를 이용하여 정방향 기구학에 의한 매리퓰레이터의 변환 연산자를 얻었다. 역방향 기구학의 해는 두가지로 나타났으며 삼각함수를 이용하여 해를 구하였다. 매리퓰레이터의 반복오차를 측정한 검증 실험에서는 X, Y, Z축에 대하여 반복 오차가 최대 2.60mm, 2.05mm, 1.55mm로 나타났으며, 정방향 및 역방향 기구학에서 오차의 최대지점 및 최소지점의 실제 좌표는 일치하였다. 반복오차 측정 결과는 매리퓰레이터의 목표지점인 오이의 직경에 비해 비교적 작게 나타났다. 측정오차는 실험중 발생한 실험오차로 판단된다. 매니퓰레이터의 오차를 줄이고 작업능률의 향상을 위해서는 링크의 수를 줄이고 오이의 품종 및 재배환경을 고려하여야 하며, 경량이면서도 견고한 재료를 사용하여 하중을 줄여야 한다.

본 연구는 오이 수확기 개발을 위해 매니퓰레이터를 설계 제작하였다. 실험에 사용한 3축 매니퓰레이터는 견고성, 내구성, 모멘트를 줄이기 위해 모터 및 감속기의 하중이 실리지 않는 곳에 장착하였다. 주요 연구 결과를 요약하면 다음과 같다. 3차원 공간상의 좌표에 대하여 매니퓰레이터의 10회 반복 측정한 오차의 평균은 Z축에 관계없이 0.1 mm내외로 정확하게 작동하는 것으로 나타났다. 실내 실험에서 25개의 오이에 대한 실험 결과 22개의 절단하여 92%의 성공률을 보였으나, 원인은 오이가 기형과이며, 수확한 후 시간이 경과하여 오이 과병의 물성이 변한 것으로 판단된다. 실내 실험에서 오이 과병을 절단하지 못한 경우에도 매니퓰레이터는 오이 과병에 0.1 mm 내외로 엔드이펙터을 접근시켰다. 50개의 오이에 대하여 현장 실험을 한 결과 42개, 84%의 절단율을 보였다. 16%의 오차가 발생한 것은 수확적기가 지나서 오이의 과병이 짧고 뭉툭해서 나타난 것으로 판단된다.

This paper presents cable-hydraulic driven 3DoF (Degree-of-Freedom) manipulator for cooperative robot with high output/low inertia and enhancing lager workspace of hydraulic manipulator. Hydraulic actuation could be solution to design more higher output manipulator than the one of electric motor actuation due to install actuation source and robot joint separated. In spite of this advantage, the conventional hydraulic driven manipulator using cylinder or vane actuator is not suitable for the candidate of cooperative robot because smaller workspace owing to small RoM (Range of Motion) hydraulic actuator. In this paper, we propose 3DoF manipulator with cable-hydraulic actuation which is more larger ratio of payload-to-weight than the one of conventional cooperative manipulator and larger workspace than the one of existing hydraulic driven manipulator. The performance of proposed manipulator was demonstrated by the experiments for confirming overall workspace task, high payload operation task under worst situation and comparing repeatability between developed manipulator and existed cooperative robots. The results of experiments showed that the appropriate performance of proposed manipulator for cooperative robot.

This paper deals with the development and application of control algorithms for series elastic relief robots for rescue operations in harsh environment like disasters or battlefield. The joint controller applied in this paper has a cascade structure combining inner loop for torque control and outer loop for position control. The torque loop contains feedforward and feedback controller and disturbance observer for independent, decentralized joint control. The effect of the elastic component and motor dynamics are treated as the nonlinear disturbance and compensated with the disturbance observer of torque controller. For the collision detection, Band Designed Disturbance Observer is configured to recognize/respond to external disturbance robustly in the continuously changing environment. The controller is applied to a 7-dof series elastic manipulator to evaluate the torque tracking and collision detection/response performance.

In this paper, applications of neural networks to vibration control of flexible single link robot manipulator are ocnsidered. The architecture of neural networks is a hidden layer, which is comprised of self-recurrent one. Tow neural networks are utilized in a control system ; one as an identifier is called neuro identifier and the othe ra s a controller is called neuro controller. The neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by dynamic error-backpropagation algorithm(DEA). To guarantee concegence and to get faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. When a flexible manipulator is ratated by a motor through the fixed end, transverse vibration may occur. The motor torque should be controlle dinsuch as way, that the motor is rotated by a specified angle. while simulataneously stabilizing vibration of the flexible manipulators so that it is arrested as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large body motions, as well as the flexural vibrations. Therefore, dynamic models for a flexible single link manipulator is derived, and LQR controller and nerual networks controller are composed. The effectiveness of the proposed nerual networks control system is confirmed by experiments.