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.

This paper proposes a fusion controller combing an anti-windup PID controller and BELBIC (Brain Emotional Learning Based Intelligent Controller) for controlling the position and vibration of a robot system having a single flexible manipulator. A finite element model of the flexible manipulator is developed. The reliability of it is verified by comparing the natural frequencies computed using the finite-element method with the experimentally measured ones. An MSC.ADAMS computational model of the robot system is interfaced with the proposed controller in MATLAB/Simulink, for carrying out a simulation. The simulation is performed with various references inputs and endpoint masses. The effectiveness and robustness of the proposed controller for control of the position and vibration of the flexible manipulator is shown through the simulation.

원자력시설 유지보수 및 해체시 다관절 매니퓰레이터 이상동작에 대한 작업자의 반응특성과 안전속도 수준을 다루고 있다. 매니퓰레이터 팔의 속도, 오류 가능성, 오류 형태와 같은 여러 가지 작업 조건에 따 라 작업자의 반응시간, 고장 경보(false alarm), 실패(miss) 횟수 등에 대한 작업자의 반응 특성을 분석하 였다. 매니퓰레이터 팔의 속도와 이상동작 형태는 반응시간에 영향을 주지만, 이상동작 가능성에 영향을 미치지 않았고, 두 요인 이상의 교호작용은 대체로 영향이 없었다. 매니퓰레이터 팔의 속도변화에 따른 반응시간 특성은 이상동작 형태에 따라 다르지만 대체로 약간 증가하는 추세를 보였다.

Recently, many MEMS manipulator or mechanisms have been developed for application in nanotechnology and optical sensors. In this paper, the method that is measurement and analysis of the motion-ability is introduced for 1 degree of freedom MEMS Manipulator. To do this, the MEMS manipulator is fabricated on the SOI wafer. It is comprised of a parallel bi-lever flexure mechanism and a bent-beam thermal actuator. The flexure mechanism is comprised of an actuator input stage, four lever arms, ten circular flexure hinges, and an output stage. Each flexure hinge provides a point of compliance and acts similar to a rotational joint with an attached rotational spring. These components are significantly stiffer than the flexure hinges, so that they act as rigid bodies in the plane of motion. The static and dynamic parameters of fabricated manipulator are measured and analysis by 3D optical profiling system. It contains a CCD camera, a field-of-view (FOV) lens, a filter assembly, and an illumination source. Light from the illuminator travels through the system and is reflected down to the objective by a beamsplitter. Once the light reaches the objective, another beamsplitter separates the light into two beams. One beam, the reference beam, reflects form a super smooth reference mirror in the objective, while the other reflects form the surface of the sample and back to the objective. With this system, we can measure and analyze the resolution, accuracy, precision, stability and responsibility of MEMS manipulator and it is very useful for fabrication or application of improved manipulator without surface damage.

본 논문에서는 핫셀에서의 원격 운전 및 유지보수 작업을 위해 개발한 천정이동 서보 조작기시스템에 대해 소개한다. 조작기 시스템은 텔레스코픽형 이송장치, 슬레이브, 마스터, 그리고 제어시스템으로 구성되어 있다. 개발한 시스템에 대해 위치 추종, 하중 취급, 신뢰성, 및 조작성에 대한 테스트를 수행하였으며 이에 대한 테스트 결과를 제시한다. 이러한 테스트 결과를 바탕으로 개선된 시스템 이 설계되었으며 이 개선된 시스템 이 차세대 공정의 실증에 적용될 예정이다.