• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11a
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• pp.235-240
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• 2001

A four degrees of freedom (dof) motion platform for bicycle simulator is developed. The motion platform, capable of the vertical linear and three angular motions, is designed based on analysis of the typical motion characteristics revealed by the existing six dof bicycle simulator. The platform essentially consists of two parts： the three dof parallel manipulator, consisting of a moving platform, a fixed base and three actuators, and the turntable to generate the yaw motion. The nonlinear kinematics and dynamics of the three dof parallel manipulator with multiple closed loop chains are analyzed for tracking control of the motion platform. The tracking performances of the three control schemes are experimentally compared： the computed torque method (CTM), the sliding mode control (SMC) and the PD control. The CTM and SMC, incorporated with the system dynamics model, are found to be equally better in performance than the PD controller, irrespective of the presence of external disturbance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.5
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• pp.46-55
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• 2000

This paper presents a direct adaptive control of robot system using chaotic neural networks and PD controller. The chaotic neural networks have robust nonlinear dynamic characteristics because of the sufficient nonlinearity in neuron itself, and the additional self-feedback and inter-connecting weights between neurons in same layer. Since the structure and the learning method are not appropriate for applying in control system, this neural networks have not been applied. In this paper, a modified chaotic neural networks is presented for dynamic control system. To evaluate the performance of the proposed neural networks, these networks are applied to the trajectory control of the three-axis PUMA robot. The structure of controller consists of PD controller and chaotic neural networks in parallel for conforming the stability in initial learning phase. Therefore, the chaotic neural network controller acts as a compensating controller of PD controller.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.265-270
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• 2005

This paper presents a new attitude stabilization and control of an unmanned helicopter based on neural network compensation. A systematic derivation on the dynamics of an unmanned small-scale helicopter is performed. Combined rotor-fuselage-tail dynamics is derived in body-fixed reference frame with its origin at the C.G. of the helicopter. And the resulting nonlinear equation of motion consists of 6-DOF air vehicle dynamics as well as the rotor flapping and engine torque equations. A simulation model was modified using the existing simulator for an unmanned helicopter dynamic model, which reflects the unmanned test helicopter(CNUHELI). The dynamic response of the refined model was compared with the flight test data. It can be shown that a good coincidence was accomplished between the real unmanned helicopter system and the mathematical model. This dynamic model was linearized for classical controller design using small perturbation method. A Neuro-PD control system was designed for both longitudinal and lateral flight modes, and the results were compared with the PD-only control response. Simulation results show that the proposed Neuro-PD control system demonstrates better performance.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.4
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• pp.154-166
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• 1992

A kinematic nonlinear multivariable launcher is modeled of which the azimuth and elevation axes are drived simultaneously and position control systems are designed for this system by the PD and LQG/LTR control methods. Also, the suitable command input fonction is suggested for the desired command following performance and the two control systems with disturbances and load variation are evaluated for the entire operating range by computer simulation. It is found that the two linear controllers can be used for the kinematic nonlinear multivariable launcher in the entire operating range and LQG/LTR controller is more effective for disturbance rejection.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.4
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• pp.335-341
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• 2010

To improve the performance of inner loop based on PD controller for a unmanned helicopter, neural networks are applied. The performance of PD controller designed on the response characteristics of error dynamics decreases because of uncertain nonlinearities of the system. The nonlinearities are decoupled to modified dynamic inversion model(MDIM) and are compensated by the neural networks. For the training of the neural networks, online weight adaptation laws which are derived from Lyapunov's direct method are used to guarantee the stability of the controller. The results of the improved performance of PD controller by neural networks are illustrated in the simulation of unmanned helicopter with nonlinearities,

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.351-354
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• 2006

In underwater environment, the control of AUV is difficult, because of the existence of parameter uncertainties and disturbances as well as highly nonlinear and coupled system dynamics. The requirement for the simple and robust controller which works satisfactorily in those dynamical uncertainties, call for a design using the PD or sliding mode controller. The PD controller is very popular controller in the industrial field and the sliding mode controller has been used successfully for the AUV controller design. In this paper, the two controllers arc designed for ISiMI(Integrated Submergible Intelligent Mission Implementation) AUV and the performances are compared by numerical simulation under the modeling uncertainty and disturbances. The design process of PD and sliding mode controller for ISiMI AUV and simulation results are included to compare the performances of the two controllers.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1256-1263
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• 2004

Output feedback passivation problem is studied when the given system is not minimum-phase or does not have relative degree one. Using a parallel connection with an additional dynamics, the authors provide a dynamic output feedback control law which renders the composite system passive. Sufficient conditions are presented under which the composite system is output feedback passive. As an application of the dynamic passivation scheme, a point-to-point control law for a flexible joint robot is presented when only the position measurements are available. This provides an alternative way of replacing the role of the velocity measurements for the proportional-derivative (PD) feedback law. The performance of the proposed control law is illustrated in the simulation studies of a manipulator with three revolute elastic joints.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.76-83
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• 2004

The purpose of this paper is to find an effective control system for a hydraulic motor-load system using matlab. The Hydraulic control system consists of a hydraulic pump, a hydraulic proportional control valve, hydraulic pipelines, a hydraulic motor and a load system. The simulation models were verified by comparing the simulation results with measured data from the real hydraulic proportional position control system. In order to compensate the nonlinear friction characteristics in a hydraulic motor-load system, a discrete time PD controller and Friction torque observer has been applied.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.458-462
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• 2004

The hydraulic system for discharging compressed gas is composed of compressor tank, proportional flow control servo valve, expulsion spool valve and discharging tube. Purpose of this study is to control of expulsion spool valve. First, we analyzed the hydraulic system. The flow control servo valve is modeled as a 2nd order transfer function and friction force of the expulsion spool valve is modeled as nonlinear model with stribeck effect. However, it is difficult to include the flow reaction force in modeling. So, we exchanged from the simplified flow reaction force of the compressed gas affection into the flow analysis code written in FORTRAN code. Our simulation of the oil pressure system for discharging gas used MATLAB/Simulink. So, we realized ＆apos;Level -2 S-Function Fortran＆apos; to cooperate for MATLAB/Simulink and FORTRAN code. PD controller is selected to control in this system. Simulation results show that with given conditions the controllers give a good tracking performance.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.768-773
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• 2013

In this paper, decoupled course, depth and roll controller design for an Autonomous Underwater Vehicle (AUV) and its performance test results are presented. Control system design is done using the PD control scheme based on a mathematical model of the AUV. Details of system implementation are given and the results of simulations and experiments using the prototype vehicle model are discussed. The designed controller was successfully applied to the nonlinear and coupled system under non-ideal actuator conditions.