• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.128-133
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• 2001

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C50) for robotic manipulators to achieve trajectory tracking angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide motion for robotic manipulators. In the proposed scheme, adaptation laws are derived from the improved second stability analysis based on the indirect adaptive control theory. The proposed control scheme is simple in structure, fast in computation, an suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by experimental results for a SCARA robot.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.673-678
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• 2001

This paper proposes a new type of piezoactuator-driven valve system. The piezoceramic actuator bonded to both sides of a flexible beam surface makes a movement required to control the pressure at the flapper-nozzle of a pneumatic valve system. After establishing a dynamic model, an appropriate size of the valve system is designed and manufactured. Subsequently, a robust $H_{\infty}$ control algorithm is formulated in order to achieve accurate tracking control of the desired pressure. The controller is experimentally realized and control performance for the sinusoidal pressure trajectory is presented in time domain. The control bandwidth of the valve system, which directly represents the fastness, is also evaluated in the frequency domain.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.1-6
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• 1992

A principle of "orthogonalization" is proposed as an extended notion of hybrid (force and position) control for robot manipulators under geometric endpoint constraints. The principle realizes the hybrid control in a strict sense by letting position and velocity feedback signals be orthogonal in joint space to the contact force vector whose components are exerted at corresponding joints. This orthogonalization is executed via a projection matrix computed in real-time from a gradient of the equation of the surface in joint coordinates and hence both projected position and velocity feedback signals become perpendicular to the force vector that is normal to the surface at the contact point in joint space. To show the important role of the principle in control of robot manipulators, three basic problems are analyzed, the first is a hybrid trajectory tracking problem by means of a "modified hybrid computed torque method", the second is a model-based adaptive control problem for robot manipulators under geometric endpoint constraints, and the third is an iterative learning control problem. It is shown that the passivity of residual error dynamics of robots follows from the orthogonalization principle and it plays a crucial role in convergence properties of both positional and force error signals.force error signals.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1132-1138
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• 2012

The MPC (Model Predictive Control) is one of the techniques that can be used to control an unmanned vehicle. It predicts the future vehicle trajectory using the dynamic characteristic of the vehicle and generate the control value to track the reference path. If some obstacles are detected on the reference paths, the MPC can generate control value to avoid the obstacles imposing the inequality constraints on the MPC cost function. In this paper, we propose an obstacle modeling algorithm for MPC with inequality constraints for obstacle avoidance and a method to set selective constraint on the MPC for stable obstacle avoidance. Simulations with the field test data show successful obstacle avoidance and way point tracking performance.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.142-147
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• 1999

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1804-1807
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• 1997

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learnign architecture. It is proposed a learning controller consisting of two neural networks-fuzzy based on independent reasoning and a connecton net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of Power System Engineering
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• v.7 no.1
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• pp.34-41
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• 2003

A method is presented for generating the path which significantly reduces residual vibration of a flexible robot manipulator and applying control theory to track the desired path. The desired path is optimally designed so that the system completes the required move with minimum residual vibration. A closed loop control theory is applied to track the planned path in the case of load variation. Specifically, it is desired that the optimally designed path has a better trajectory tracking capabilities during the residual vibration over the cycloidal path, in various cases of load. Perturbation adaptive control is used as closed loop control scheme. A planar two link manipulator is used to evaluate this method.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.886-891
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• 1987

In this paper, we present a effective method to control robotic systems by an iterative learning algorithm. This method is based on the concepts of the learning control law which is introduced in this paper, that is, avoidance of using derivative of system state and ignorance of high frequency influence in system performance. By means of the betterment of performance due to the improvement of estimated unknown information, the learning control algorithm compels the system to gradually approach in desired trajectory, and eventually the tracking error asymptotically converges upon zero. In order to verify its utility, one degree of freedom of manipulator has been used in the experiments and the results illustrate this control scheme is very effective.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.3
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• pp.157-166
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• 2015

In this paper, we proposed a new robust control scheme to implement stable control of robot manipulators including nonlinear perameters The proposed robust controller is composed of a nonlinear controller and linear compemsation controller. It shows a good robust performance in reaching mode which does not possess invariance property. Thus, the proposed nonlinear controller showed a good robust performance in the whole region, It was illustrated that the proposed control showed a good transient response and trajectory tracking performance for robot manipulator with four joint by experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.7-12
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• 1990

In this paper, we propose a new manipulator control scheme based on the CMAG neural network. The proposed control consists of two components. The feedforward component is an output of trained CMAC neural network and the feedback component is a modified sliding mode control. The CMAC accepts the position, velocity and acceleration of manipulator as input and outputs two values for the controller : One is the nominal torque used for feedforward compensation(M1 network) and the other is the inertia matrix related information used for the feedback component(M2 network). Since the used control algorithm guarantees the robust trajectory tracking in spite of modeling errors, the CMAC mapping errors due to the memory limitation are little worth consideration.