• Proceedings of the KIEE Conference
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• 대한전기학회 1990년도 하계학술대회 논문집
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• pp.51-54
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• 1990

A parametrization for a linear system is presented to design a direct model reference adaptive pole placement controler. This parametrized model is one of the structured nonminimal models. The exponentially weighted least-squres algorithm is employed to estimate the control parameters. The direct adaptive controller has the exponential weighting properties by the proposed method of selecting the characteristic polynomials of the sensitivity function filters in connection with the reference models.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.489-493
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• 1989

In this paper a new approach to adaptive control using a combination of both direct and indirect methods has been proposed. Based on the estimates of the plant parameters and the current values of the control parameters, closed-loop estimation errors .epsilon.$_{\theta}$(t) and .epsilon.$_{k}$(t) are defined. These in turn are used in the adaptive laws for updating both identification as well as control parameters. The global uniform stability of the overall system is shown by constructing a Lyapunov function.n.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.872-877
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• 1990

Robustness of a model refernece direct adaptive pole placement control for not necessarily minimum phase systems is studied subject to unmodeled dynamics and bounded disturbances. The adaptive control scheme involves two estimators for the system and the controller parameter estimation, respectively. The robustness is obtaind under some weak assumptions and by using both a normalized least-squares algorithm with dead zone and an appropriate nonlinear feedback.

• Proceedings of the KIEE Conference
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• 대한전기학회 1989년도 추계학술대회 논문집 학회본부
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• pp.376-379
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• 1989

In this paper a direct adaptive control scheme which minimizes N-step cost function is proposed. It is derived via GPC control algorithm under the assumption that we know N-step unit step response of the given system. The proposed adaptive scheme is shown to have the same stability properties with the RHTC control, and require very small amount of calculation compared with other adaptive algorithms which minimize N-step cost function. And it is shown through simulations that the proposed adaptive algorithm is robust to the variation of the unit step response which is assumed to be known.

• Journal of Institute of Control, Robotics and Systems
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• 제9권2호
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• pp.99-106
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• 2003

A new reconfigurable model following flight control method based on direct adaptive scheme is presented. Using the timescale separation principle, both the inner-loop and the outer-loop states are controlled simultaneously. For the timescale separation assumption to be satisfied, the inner-loop model dynamics is set to be fast whereas the outer-loop model dynamics is set to be relatively slow. The stability and convergence of the proposed control law is proved by Lyapunov theorem. One of the merits of the proposed reconfigurable controller is that the FDI process and the persistent input excitation are not necessary, which is suitable for the flight control system. To evaluate the reconfiguration performance of the proposed control method, numerical simulation is performed using six degree-of-freedom nonlinear dynamics.

• International Journal of Control, Automation, and Systems
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• 제5권6호
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• pp.630-642
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• 2007

In this paper, we propose a direct model reference adaptive fuzzy control (MRAFC) for MIMO nonlinear systems whose structure is represented by the Takagi-Sugeno fuzzy model. The adaptive law of the MRAFC estimates the approximation error of the fuzzy logic system so that it provides asymptotic tracking of the reference signal for the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. To verify the validity and effectiveness of the MRAFC scheme, the suggested analysis and design techniques are applied to the tracking control of robot manipulator and simulation studies are carried out. In the control design, the MRAFC is combined with feedforward PD control to make the actual joint trajectories of the robot manipulator with system uncertainties track the desired reference joint position trajectories asymptotically stably.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.260-265
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• 1992

This paper presents an adaptive control scheme for flexible joint robot manipulators. This control scheme is based on the Lyapunov direct method with the arm energy-based Lyapunov function. The proposed adaptive control scheme uses only the position and velocity feedback of link and motor shaft. The adaptive control system of flexible joint robots is asymptotically stable regardless of the joint flexibility value. Therefore, the assumption of weak joint ealsticity is not needed. Also, joint flexibility value is unknown. Simulation results are presented to show the feasibility of the proposed adaptive control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.640-645
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• 2004

This paper proposes a direct adaptive control method using self-recurrent wavelet neural network (SRWNN) for stable path tracking of mobile robots. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). Unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. For this ability of the SRWNN, the SRWNN is used as a controller with simpler structure than the WNN in our on-line control process. The gradient-descent method with adaptive learning rates (ALR) is applied to train the parameters of the SRWNN. The ALR are derived from discrete Lyapunov stability theorem, which are used to guarantee the stable path tracking of mobile robots. Finally, through computer simulations, we demonstrate the effectiveness and stability of the proposed controller.

• International Journal of Precision Engineering and Manufacturing
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• 제4권2호
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• pp.13-22
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• 2003

To coordinate the robot manipulator along the desired trajectory, the exact model of the dynamics is required. The added mass and added moment of inertia, buoyancy, drag force, and friction mainly affect the dynamics of the undersea robot manipulator, and they are quite complex and unknown. In this reason. the exact model of the undersea robot manipulator is difficult to obtain. In this paper, instead of having efforts to get the exact model of the robot dynamics, a control-based approach was performed. We modeled the dynamics of the undersea robot manipulator whose parameters are unknown, and then applied a proposed direct adaptive and robust control, which is different from previous studies. The unknown added mass, and added moment of inertia, drag force and friction are estimated by the direct adaptive control scheme, and the drag force which is dominant disturbance is compensated by the robust control. Also, stability of the proposed control scheme is analyzed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.14-19
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• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.