• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.697-700
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• 2005

An adaptive fuzzy sliding-mode controller (SMC) for uncertain or ill-defined single-input single-output (SISO) nonaffine nonlinear systems is proposed. By using the universal approximation property of the fuzzy logic system (FLS), it is tuned on-line to cancel the unknown system nonlinearity. We adopt a self-structuring FLS to guarantee global stability of the closed-loop system rather than semi=global boundedness. The control and adaptive laws are derived so that the estimated fuzzy parameters are bounded and the sliding condition is satisfied.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.148-153
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• 1999

In this paper, it is proposed the adaptive-sliding mode control technique which is new approach to implement the robust control of industrial robot manipulator with external disturbances and parameter uncertainties. Over the past decade, the design of advanced control systems for industrial robotic manipulators has been a very active area of research and two major design categories have emerged. Sliding mode control is a well-known technique for robust control of uncertain nonlinear systems. The robustness of sliding model controllers can be shown in continuous time, but digital implementation may not preserve robustness properties because the sampling process limits the existence of a true sliding mode. Adaptive control algorithm is designed by using the principle of the model reference adaptive control method based upon the hyperstability theory. The proposed control scheme has a simple structure is computationally fast and does not require knowledge of the complex dynamic model or the parameter values of the manipulator or the payload. Simulation results how that the proposed method not only improves the performance of the system but also reduces the chattering problem of sliding mode control. Consequently, it is expected that the new adaptive sliding mode control algorithm will be suited for various practical applications of industrial robot control system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.7
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• pp.1146-1151
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• 2003

In this paper, results of an analysis of driving behavior characteristics and a driver-adaptive control algorithm for adaptive cruise control systems have been described. The analysis has been performed based on real-world driving data. The vehicle longitudinal control algorithm developed in our previous research has been extended based on the analysis to incorporate the driving characteristics of the human drivers into the control algorithm and to achieve natural vehicle behavior of the adaptive cruise controlled vehicle that would feel comfortable to the human driver. A driving characteristic parameters estimation algorithm has been developed. The driving characteristics parameters of a human driver have been estimated during manual driving using the recursive least-square algorithm and then the estimated ones have been used in the controller adaptation. The vehicle following characteristics of the adaptive cruise control vehicles with and without the driving behavior parameter estimation algorithm have been compared to those of the manual driving. It has been shown that the vehicle following behavior of the controlled vehicle with the adaptive control algorithm is quite close to that of the human controlled vehicles. Therefore, it can be expected that the more natural and more comfortable vehicle behavior would be achieved by the use of the driver adaptive cruise control algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.183-187
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• 1989

This paper presents on approach to the position control of a robot manipulator by using a decentralized adaptive control scheme. The large scale system is regarded as the system which consists of many subsystems having interconnection. In each subsystem, a local control system is composed by feedforward and feedback component, one computes the nominal torque from the Newton-Euler equation, the other computes the perturbation equation which reduce the position error of the manipulator along the nominal trajectory. A computer simulation studies was conducted to evaluate and compare the performances of the proposed manipulator control scheme with those of the PD control and centralized control schemes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.236-241
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• 2007

This paper presents an adaptive controller using chaotic dynamic neural networks(CDNN) for nonlinear dynamic system. A new dynamic backpropagation learning method of the proposed chaotic dynamic neural networks is developed for efficient learning, and this learning method includes the convergence for improving the stability of chaotic neural networks. The proposed CDNN is applied to the system identification of chaotic system and the adaptive controller. The simulation results show good performances in the identification of Lorenz equation and the adaptive control of nonlinear system, since the CDNN has the fast learning characteristics and the robust adaptability to nonlinear dynamic system.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1182-1187
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• 2014

This paper proposes an energy management strategy and adaptive control for superconducting magnetic energy storage (SMES) in a distribution power system with a grid-connected photovoltaic (PV) farm. Application of the SMES system can decrease the output power fluctuations of PV system effectively. Also, it can control the real and reactive powers corresponding to the scheduled reference values with adequate converter capacity, which are required at a steady-state operating point. Therefore, the adaptive control strategy for SMES plays a key role in improving the system stability when the PV generation causes uncertain variations due to weather conditions. The performance of proposed energy management strategy and control method for the SMES is then evaluated with several case studies based on the PSCAD/EMTDC$^{(R)}$ simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.717-722
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• 2003

A driving simulator is a computer-controlled tool to study an interface between a driver and vehicle response by enabling the driver to participate in judging vehicle characteristics. Using the driving simulator, human factor study, vehicle system development and other research can be effectively done under controllable, reproducible and non-dangerous conditions. An Adaptive Cruise Control (ACC) system is generally regarded as a system that can be achieved in the near future without the demanding infrastructure components and technologies. ACC system is an automatic vehicle following system with no human engagement in the longitudinal vehicle direction. And the influence of the driver is substantial in developing the system. Driving characteristic is very different according to the accident riskiness, gender, age and so on. In this research, experiments have been carried out to investigate driving characteristics with the ACC system, using a driving simulator. Participants are 21 male and 19 female. Driving characteristics such as preferred headway-time, lane keeping ability, eye direction, and head movement have been observed and compared between the driving with ACC and the driving without ACC.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1103-1109
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• 2008

Since the flexible joint robots with motor dynamics are represented by the fifth-order nonlinear sγstem, it is difficult and complex to design the controller for electrically driven flexible-joint (EDFJ) robots. In this paper, we propose a simple adaptive control method to solve this problem. It is assumed that the model uncertainties of the robots dynamics, joint flexibility, and motor dynamics are unknown. For the simple control design, the dynamic surface design method is applied, and all uncertainties in the robot and motor dynamics are compensated by using the adaptive function approximation technique. It is proved that all signals in the controlled closed-loop system are uniformly ultimately bounded. Simulation results for three-link EDFJ manipulators are provided to validate the effectiveness of the proposed control system.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.42-47
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• 2014

This paper investigates an adaptive approximation design problem for the tracking control of output-constrained non-affine pure-feedback systems. To satisfy the desired performance without constraint violation, we employ a barrier Lyapunov function which grows to infinity whenever its argument approaches some limits. The main difficulty in dealing with pure-feedback systems considering output constraints is that the system has a non-affine appearance of the constrained variable to be used as a virtual control. To overcome this difficulty, the implicit function theorem and mean value theorem are exploited to assert the existence of the desired virtual and actual controls. The function approximation technique based on adaptive neural networks is used to estimate the desired control inputs. It is shown that all signals in the closed-loop system are uniformly ultimately bounded.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.315-322
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• 2004

A direct adaptive state-feedback controller for highly nonlinear systems is proposed. This paper considers uncertain or ill-defined nonaffine nonlinear systems and employs a static fuzzy logic system (FLS). The employed FLS estimates. and adaptively cancels an unknown plant nonlinearity using its proved universal approximation property. A control law and adaptive laws for unknown fuzzy parameters and bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. The tracking error is guaranteed to be uniformly asymptotically stable rather than uniformly ultimately bounded with the aid of an additional robustifying control term. No a priori knowledge of an upper bound on an lumped uncertainty is required.