• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1179-1181
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• 1996

In this paper, method for direct adaptive control of discrete nonlinear systems using neural network is presented. Also, the stability problems are investigated in sense of the Lyapunov stability conditions. Through extensive simulation, the SOON is shown to be effective for indirect adaptive control of nonlinear dynamic systems.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.693-695
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• 1997

This paper deals with the tracking control problem of robotic manipulators with unknown or changing dynamics. The torque input applied to the joint actuators is determined at every instance by the identification model that best approximates the robot dynamics. The best of the identified model is chosen by the proposed switching mechanism with fuzzy inference of the manipulator in an indirect adaptive controller architecture. Simulation results are also included to demonstrate the improvement in the tracking performance when the proposed method is used.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.47-52
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• 2012

This paper focuses on discrete-time sliding mode control with SIIM fuzzy adaptive switching gain. The adaptive switching gain is calculated using the simplified indirect inference fuzzy logic. Two fuzzy inputs are the normal distance from the present state trajectory to the switching function and the distance from the present state trajectory to the equilibrium state. The fuzzy output $f_{out}$(k) out f k is used to adjust the speed the adaptation law depending on the location of the state trajectory. The simulation results showed that the proposed method had no chattering in case of uncertain parameter without disturbance. Moreover the convergent rate of the switching gain was faster and more stable even in case of disturbance.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2135-2137
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• 2002

In this paper to overcome drawback of FLC an adaptive fuzzy sliding mode controller is proposed. The fuzzy basis function to describe the fuzzy system is introduced. The system parameter in sliding mode are estimated by the indirect adaptive fuzzy control. Adaptive laws for fuzzy parameters and fuzzy rule structure are established so that the whole system is suable in the sense of Lyapunov stability. The computer simulation results for inverted pendulum system show the performance of the proposed fuzzy sliding mode controller.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.1
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• pp.78-89
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• 1991

This Paper presents an application example of the Adaptive Robust Servocontrol (ARSC) scheme, which is an explicit (or indirect) pole-assignment adaptive algorithm with the property of "robustness". The ARSC scheme is applied to an end-milling process for cutting force regulation. It is shown that the federate of an end-milling process can be maximized by the adaptive regulation of the peak cutting force through the ARSC scheme. The results of simulation study and real cutting experiment are presented. It has been verified that asymptotic regulation can be achieved with robustness against the slowly time-varying perturbations to the process model parameters, which are caused by nonlinear cutting dynamics. dynamics.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.11
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• pp.9-21
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• 2009

This paper proposes the alternative observer and controller design scheme based on T-S fuzzy model. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given unknown nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. The proposed controller is based on a simple output feedback method. Therefore, it solves the singularity problem, without any additional algorithm, which occurs in the inverse dynamics based on the feedback linearization method. The adaptive fuzzy scheme estimates the parameters and the feedback gain comprising the fuzzy model representing the observation system. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observer and controller, they are applied to an inverted pendulum on a cart.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2374-2376
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• 2000

In this paper, we propose an indirect vector control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) parameter estimator. It estimates the rotor time constant when the indirect vector control of induction motor is applied. We use the stator current error that is difference between the current command and estimated current calculated from terminal voltage and current. And two induced current estimate equations are used in training ANFIS.The estimator is trained by the hybrid learning algorithm. Simulation results shows good performance under load disturbance and motor parameter variations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.52-57
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• 2006

In this paper, we have proposed a new adaptive fuzzy control algorithm based on Takagi-Sugeno fuzzy model. The regulation problem for the uncertain SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the parameter approximation error is proved. Unlike the existing feedback linearization based methods, the proposed algorithm can guarantee the global stability in the presence of the singularity in the inverse dynamics of the plant. The performance of the proposed algorithm is demonstrated through the problem of balancing and swing-up of an inverted pendulum on a cart.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.771-776
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• 1991

In this paper. an indirect adaptive controller for manipulator which is composed of two controller structure is considered. One is feedforward controller in which the dynamics equation solved and the other is feedback controller in which the output error compensated. This controller has a good performance, but the computation burden of the feed forward controller keep from real time control. At this point, we proposed the two time adaptive controller where the sampling time of the feedforward controller is quite longer than that of the feedback controller. By the computer simulation, this proposed two time adaptive controller shows good performance in the view of accuracy in spite of decreasing computational burden.

• Bulletin of the Korean Mathematical Society
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• v.38 no.1
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• pp.29-53
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• 2001

This note presents a an indirect adaptive control scheme for first-order continuous-time systems. The estimated plant model is controllable and then the adaptive scheme is free from singularities. The singularities are avoided through a modification of the estimated plant parameter vector so that its associated Sylvester matrix is guaranteed to be nonsingular. That properties is achieved by ensuring that the absolute value of its determinant does not lie below a positive threshold. A modification scheme based on the achievement of a modified diagonally dominant Sylvester matrix of the parameter estimates is also given as an alternative method. This diagonal dominance is achieved through estimates modification as a way to guarantee the controllability of the modified estimated model when a controllability measure of the ‘a priori’ estimated model fails. In both schemes, the use of a hysteresis switching function for the modification of the estimates is not required to ensure the nonsingularity of the Sylvester matrix of the estimates.