Earticle

이동로봇팔의 역학 제어기와 신경회로망 보상기가 결합된 제어구조를 제안한다. 신경망 보상이 적응적이 고 추적오차와 파라미터 추정치가 유계가 되는 파라미터 동조 알고리듬과 안정도 증명을 제시한다. 신경망 비선형 보상기를 이동로봇팔에 시뮬레이션 함으로서 비선형성의 해로운 영향을 줄이는 효과를 보여준다.

A control structure that makes possible the integration of a kinematic controller and a neural network(NN) compensator for mobile manipulators is presented. The stability of the closed loop system and the boundness of tracking errors are proved using Lyapunov theory. The NN compensation scheme proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamic in the mobile manipulator. On-line NN parameter tuning algorithms do no require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.

In this study, a position and force simultaneous trajectory tracking control algorithm is proposed for a driving apparatus that consists of two pneumatic cylinders connected in series. The controller applied to the driving apparatus is composed of a non-interaction controller to compensate for interaction between cylinders and a disturbance observer aimed to reduce the effect of model discrepancy that cannot be compensated by the non-interaction controller. The effectiveness of the proposed control algorithm is proved by experimental results.

The study reported deals with investigating the feasibility of control strategy for a serial rigid link manipulator that applies impulsive torques at the joints. The strategy is illustrated for a planar three rigid link manipulator. An impulse-based concept which uses successive torque impulses on rigid link as the controller for motion correction was introduced. This control strategy was tested over the entire trajectory to demonstrate that the tracking error could be reduced effectively. The best condition for minimizing the tracking error with the least impulse input at each joint is investigated by considering one design and one operating parameter. The first was the damping in the system, and the second was the sampling time during operation. The results show that this approach can provide useful guidance for the design and control of robot manipulators that require minimum impulse feedback for accurate tracking.

Objective: The purpose of this study is to investigate the effects of eyeball tracking exercises with neuromuscular postural control exercise programs on chronic neck pain patients with tension headaches. Design: A randomized controlled trial Methods: Thirty-two patients with chronic neck pain and tension headaches participated in this study. Participants were randomly assigned to the experimental group (n=17) and the control group (n=15) using a random allocation program. Both groups received a neuromuscular postural control exercise program. Additionally, the experimental group received eyeball tracking exercises. Visual analogue scale (VAS), neck disability index (NDI), and quality of life (Euro Quality of Life 5 Dimension, EQ-5D), headache impact index test-6 (HIT-6), and SCM muscle thickness were compared to evaluate the effect on intervention. Results: Both groups showed significant changes in VAS, NDI, EQ-5D, HIT-6, and SCM muscle thickness before and after the intervention (p<0.05). Furthermore, there were significant differences in the magnitude of change in VAS, EQ-5D, and HIT-6 between the experimental and control groups (p<0.05). Conclusions: Eyeball tracking exercises with neuromuscular control exercises that are in chronic neck pain patients with tension headaches can promote relaxation of the muscles around the eyes and enhance the stability of the neck muscles, as well as improving postural control ability. Therefore, eyeball tracking exercises with neuromuscular control exercises for chronic neck pain patients with tension headaches are effective method with clinical significance in improving the pain, quality of life, and reducing the headache impact index.

This paper presents a Multiple Target Tracking (MTT) Adaptive Cruise Control (ACC) system which consists of three parts; a multi-model-based multi-target state estimator, a primary vehicular target determination algorithm, and a single-target adaptive cruise control algorithm. Three motion models, which are validated using simulated and experimental data, are adopted to distinguish large lateral motions from longitudinally excited motions. The improvement in the state estimation performance when using three models is verified in target tracking simulations. However, the performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. The MTT-ACC system is tested under lane changing situations to examine how much the system performance is improved when multiple models are incorporated. Simulation results show system response that is more realistic and reflective of actual human driving behavior.

A stable neural network control scheme for unknown non-linear systems is developed in this paper. While the control variable is optimised to minimize the performance index, convergence of the index is guaranteed asymptotically stable by a Lyapnov control law. The optimization is achieved using a gradient descent searching algorithm and is consequently slow. A fast convergence algorithm using an adaptive learning rate is employed to speed up the convergence. Application of the stable control to a single input single output (SISO) non-linear system is simulated. The satisfactory control performance is obtained.

The usage of Unmanned Aerial Vehicles (UAVs) has escalated widely in recent years. The mobility they provide and their capability to be used autonomously has made them applicable to a wide range of fields for both the civilians and the military. However, the flight safety is pivotal during UAV flights for any application and it is necessary that the recently ordained rules are complied with. Ensuring the compliance of such rules and regulations is especially difficult when using these UAVs are used in Autonomous mode. To establish the flight safety and compliance with the regulations, computer vision can be utilized while in autonomous mode. This is predominantly true when the flight is conducted in the proximity of crowds, animals, moving vehicles or when emergency and precision landings need to be performed. To that extent, we present a concept of drone flight control system which utilizes object detection and tracking algorithms.

수중운동체가 수중에서 진행할 때 외부 파도에 의한 불확실한 부하 저항을 받으므로 이에 대응하는 핀(조타) 구동부의 제어 문제를 고려한다. 본 논문에서 제시하는 제어기는 본체로부터의 지정 각도를 부여 받으면 이에 부응하여 핀의 각도와 각속도를 이용하여 제어기의 알고리즘을 구축하여 지정된 경로를 추적하게 한다. 또한 핀의 각속도 정보의 이용이 부득이 어려운 상황에 대처하기 위하여 핀의 각도만을 이용한 출력제어기나 추정기를 설계하여 주위 환경의 불확실성을 극복할 수 있는 제어기를 제안한다. DC서보 모터로 구성괸 핀 구동부에 대해 실제 데이터를 사용하여 제안된 제어기의 성능을 시뮬레이션을 통하여 검증한다.

In the development of intelligent vehicles, path tracking of unmanned vehicle is a basis of autonomous driving and automatic navigation. It is very important to find the exact position of a vehicle for the path tracking, and it is possible to get the position information from GPS. However the information of GPS is not the current position but the past position because a vehicle is moving and GPS has a time delay. In this paper, therefore, the moving distance of a vehicle is estimated using a direction sensor and a velocity sensor to compensate the position error of GPS. In the steering control, optimal fuzzy rules for the path tracking can be found through the simulation of Simulink. Real driving experiments show the fuzzy rules are good for the steering control and the position error of GPS is well compensated by the proposed estimation method.

A tracking control scheme on the XY ball-screw drive system in the presence of nonlinear dynamic friction is proposed. A nonlinear dynamic friction is regarded as the Lund-Grenoble friction model to compensate effects of friction. The conventional VSC method that often has been used as a non-model-based friction controller has poor tracking performance in high-precision position tracking application since it cannot compensate the friction effect below a certain precision level completely. Thus to improve the precise position tracking performance, we propose the integral type VSC method combined with the friction-model-based observer. Then this control scheme has the high precise tracking performance compared with the non-model-baked VSC method and the PID control method with a similar observer. This fact is shown through the experiment on the XY ball-screw drive system with the nonlinear dynamic friction.

An adaptive controller for trajectory tracking control of a pneumatic cylinder is proposed. The controller is directly derived by using Lyapunov function, and very simple and computationally efficient since it does not require the mathematical model or the parameter values of a pneumatic system. It is also shown that the system is bounded stable with the controller, and the size of tracking errors can be made arbitrarily small. The stability and the performance of the controller is also verified experimentally. The results of the experiments demonstrate that the proposed controller achieves more accurate trajectory tracking performance than a PD controller.

This paper presents a precise tracking control scheme for the proposed parallel robot using artificial neural network. This control scheme is composed of three feedback controllers and one feedforward controller. Conventional PD controller and artificial neural network are used as feedback and feedforward controller respectively. A backpropagation learning strategy is applied to the training of artificial neural network, and PD controller outputs are used as target outputs. The PD controllers are designed at the robot dynamics based on inter-relationship between active joints and moving platform. Feedback controllers insure the total stability of system, and feedforward controller generates the control signal for trajectory tracking. The precise tracking performance of proposed control scheme is proved by computer simulation.

본 연구에서는 ER 브레이크와 ER클러치를 피드백작동기로 사용하여 이송테이블의 위치 추적제어를 수행하였다. 이를 위해 먼저 아라빅 검(arabic gum)계통의 ER유체를 자체조성한 후 전기장에 대한 빙햄(Bingham)모델을 실험적으로 도출하였다. 빙햄모델에 근거하여 평판형의 ER브레이크와 실린더형의 ER클러치를 설계 제작하였으며, 계단입력(step input)전기장에 따른 출력토오크특성을 통하여 이들 작동기의 동적모델을 얻었다. 이들 작동기와 연계된 이송테이블시스템의 운동지배방정식을 유도한 후 위치추적제어를 위한 슬라이딩모드제어기를 설계하였다. 제어기 설계시 이송해이블의 부하질량 변화에 대한 시스템 불확실성과 마찰력을 고려하여 제어성능의 강건성을 보장하도록 하였다. 제안된 제어시스템의 제어영역(control bandwidth)을 주파수 영역에서 고찰한 후 여러 레적에 대한 위치추적제어 실험을 수행하였다.

This paper presents a robust force tracking control of a smart flexible gripper featured by a piezoceramic actuator characterizing its durability and quick response time. A mathematical governing equation for the proposed gripper structure is derived by employing Hamilton's principle and a state space control model is subsequently obtained through model analysis. Uncertain system parameters such as frequency variation are included in the control model. A sliding mode control theory which has inherent robustness to the sys- tem uncertainties is adopted to design a force tracking controller for the piezoceramic actuator. Using out- put information from the tip force sensor, a full-order observer is constructed to estimate state variables of the system. Force tracking performances for desired trajectories represented by sinusoidal and step func- tions are evaluated by undertaking both simulation and experimental works. In addition, in order to illustrate practical feasibility of the proposed method, a two-fingered gripper is constructed and its performance is demonstrated by showing a capability of holding an object.

In this paper, the control algorithm fur an autonomous vehicle is studied and applied to an actual 2 wheel-driven vehicle system. In order to control a nonholonomic system, the kinematic model for an autonomous vehicle is constructed by relative velocity relationship about the virtual point at distance from the vehicle's frame. And the optimal controller that based on the kinematic model is operated on purpose to track a reference vehicle's path. The actual system is designed with named 'HYAVI' and the system controller is applied. Because all the results of simulation don't satisfy the driving conditions of HYAVI, a reformed control algorithm that satisfies an actual autonomous vehicle is applied at HYAVI. At the results of actual experiments, the path tracking works very well by the reformed control algorithm. An autonomous vehicle that applied this control algorithm can be easily used for a path generation algorithm.

시스템의 모델링 과정에서 발생될 수 있는 불확실성(uncertainty) 혹은 미지의 가반중량을 비롯한 외란에 의해 발생되는 불확실성 등을 갖고있는 로봇의 강건 추적제어기 설계를 위해 가변구조시스템(variable structure system) 이론을 적용하였다. 시스템 방정식과 연계하여 슬라이딩 모드가 존재하기 위한 조건을 구했으며, 입력 에 대한 불확실성은 매칭조건(matching condition)을 가정하여 다루었다. 기존의 방법에 비해 제어기 설계과정이 간단 명료하며 요구되는 궤적에 대한 추적제어 효과 또한 매우 우수함을 컴퓨터 시뮬레이션을 통해 입증하였다.

This paper presents vibration and position tracking control of a smart structure using shape memory alloy(SMA) actuators. A governing equation of motion of the proposed structure is obtained via Hamilton's princeple. The dynamic characteristics of the SMA actuator are experimentally identified and incorporated with the governing equation to furnish a control system model. Subsequently, a sliding mode controller which has inherent robustness to external disturbances is formulated on the basis of the sliding mode conplacement, and also for the position tracking control of desired trajectories with low-frequency sine and square waves.

This paper discusses automation of a small-scale hydraulic shovel and its trajectory control. To move an end-effect (grinder) along a desired trajectory, the controller uses PID(proportional-integral- defferential) control and internal pressure of hydraulic cylinder. To apply PID control in the present hydraulic system, the system model is derived physically and its system parameters are obtained by actual measurement. To show the effectiveness of the PID controller and propriety of system model, the computer simulations and experiments are performed. These results of the simulations and experiments indicate that the PID trajectory control of robotic deburring by hydraulic shovel is very effective.

This paper describes a robust control scheme for high-speed and long stroke scanning motion of high precision linear motor system consisting of linear motor, air bearing guide and position measurement system using heterodyne interferometer. Nowadays, semiconductor process and inspection of wafer or LCD need high speed and long travel length for their high throughput and extremely small velocity fluctuations or tracking errors. In order to satisfy these conditions, linear motor system are widely used because they have large thrust force and do not need motion conversion mechanisms such as ball screw, rack & pinion or capstan with which the system are burdened. However linear motors have a problem called force ripple. Force ripple deteriorates the tracking performances and makes periodic position errors. So, force ripple must be compensated. To maximize the tracking performance of linear motor system, we propose the control scheme which is composed of a robust control method, Time Delay Controller (TDC) and a feedforward control method, Zero Phase Error Tracking Control (ZPETC) for accurate tracking a given trajectory and an adaptive force ripple compensation (AFC) algorithm fur estimating and compensating force ripple. The adaptive ripple compensation is continuously refined on the basis of tracking error. Computer simulation results based on modeled parameters verify the effectiveness of the proposed control scheme for high-speed, long stroke and high precision scanning motion and show that the proposed control scheme can achieve a sup error tracking performance in comparison to conventional TDC control.