• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.11
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• pp.945-954
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• 1990

In this paper, we presents a robust adaptive control system design method in the work coordinate of the robot arm for conveyor tracking. In the design, if the weighting function $L_K$ is smaller than the design parameter then the transient characteristics of system becomes stable, if $L_K$ is larger than then the system becomes unstable. Proposed design method presented here is based on model referenece adaptive control and Popov stability theorem. By the utiliza/tion of an auxilary input, it is improved the transent characteristics of the system in comparison with the conventional model reference adptive control, since the rate of V and V(t) is large. The usefulness of a proposed design method has been confirmed by computer simulations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.697-702
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• 2016

To control permanent magnet synchronous motors smoothly, it is important to know the exact parameter values of the stator resistance, various inductances, and the flux linkage of the permanent magnet. In practice, these parameters vary due to a variable operating point, temperature change, or a fault. This paper proposes a MRAS (Model Reference Adaptive System) based parameter estimator and adaptive control scheme. Owing to the non-linearity of the system equation with respect to these parameters, although many schemes proposed previously assumed that some parameters are known, all the parameters were assumed to be unknown. The simulation results revealed the effectiveness of the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.14-21
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• 2001

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. In indirect adaptive fuzzy control, based on the proved approximation capability of fuzzy systems, they are used to capture the unknown nonlinearities of the plant. Until now, most of the papers in the field of controller design for nonlinear system considers the affine system using fuzzy systems which have fixed grid-rule structure. We proposes a dynamic fuzzy rule insertion scheme where fuzzy rule-base grows as time goes on. With this method, the dynamic order of the controller reduces dramatically and an appropriate number of fuzzy rules are found on-line. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed-loop system is guaranteed.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.621-627
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• 2001

In this paper, a novel and efficient global intelligent digital redesign technique for a Takagi-Sugeno (TS) fuzzy system is addressed. The proposed method should be notably discriminated from the previous works in that in allows us to globally match the states of the closed-loop TS fuzzy system with the pre-designed continuous-time fuzzy-model-based controller and those with the digitally redesigned fuzzy-model-based controller, and further to guarantee the stabilizability by the redesigned controller in the sense of Lyapunov. Sufficient conditions for the global state-matching and the stability of the digitally controller system are formulated in terns of linear matrix inequalities (LMIs). The Duffing-like chaotic oscillator is simulated and demonstrated, to validate the effectiveness of the proposed digital redesign technique, which implies the safe applicability to the digital control system.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.77-88
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• 2018

This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.

• 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.

• Journal of Ocean Engineering and Technology
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• v.23 no.2
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• pp.8-17
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• 2009

This paper presents an adaptive tracking control method for a welding mobile manipulator with several unknown parameters such as the last length of the manipulator, the wheel radius and the distance from the center to the wheel. The mobile manipulator consisted of the manipulator and the mobile-platform. Kinematic modelings for the manipulator and the mobile-platform with several unknown parameters were produced. The tracking error vectors for the manipulator and the mobile-platform were defined. These adaptive controllers were designed based on the Lyapunov function to guarantee the stability of the whole system when the mobile manipulator performs a welding task. Update laws were also designed to estimate the unknown dimensional parameters. To implement the designed controllers, a control system integrated with PIC16F877 microprocessors and a TMS320C32 DSP was developed. Simulation and experimental results are presented to show the effectiveness of the proposed controllers.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.240-248
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• 2009

An approach to control the position for an interior permanent magnet synchronous motor (IPMSM) based on an adaptive integral binary observer is described. The binary controller with a binary observer is composed of a main loop regulator and an auxiliary loop regulator. One of its key features is that it alleviates chatter in the constant boundary layer. However, steady state estimation accuracy and robustness are dependent upon the thickness of the constant boundary layer. In order to improve the steady state performance of the binary observer and eliminate the chattering problem of the constant boundary layer, a new binary observer is formed by adding extra integral dynamics to the existing switching hyperplane equation. Also, the proposed adaptive integral binary observer applies an adaptive scheme because the parameters of the dynamic equations such as the machine inertia and the viscosity friction coefficient are not well known. Furthermore, these values can typically be easily changed during normal operation. However, the proposed observer can overcome the problems caused by using the dynamic equations, and the rotor position estimation is constructed by integrating the rotor speed estimated with a Lyapunov function. Experimental results obtained using the proposed algorithm are presented to demonstrate the effectiveness of the approach.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2365-2377
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• 2017

This paper addresses two interrelated problems concerning the tracking control of pod propulsion unmanned surface vessel (USV), namely, the modeling of pod propulsion USV, and tracking controller design. First, based on MMG modeling theory, the model of pod propulsion USV is derived. Furthermore, a practical adaptive neural tracking controller is proposed by backstepping technique, neural network approximation and adaptive method. Meanwhile, unlike some existing tracking methods for surface vessel whose control algorithms suffer from "explosion of complexity", a novel neural shunting model is introduced to solve the problem. Using a Lyapunov functional, it is proven that all error signals in the system are uniformly ultimately bounded. The advantages of the paper are that first, the underactuated characteristic of pod propulsion USV is proved; second, the neural shunting model is used to solve the problem of "explosion of complexity", and this is a combination of knowledge in the field of biology and engineering; third, the developed controller is able to capture the uncertainties without the exact information of hydrodynamic damping structure and the sea disturbances. Numerical examples have been given to illustrate the performance and effectiveness of the proposed scheme.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.6
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• pp.142-155
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• 2000

A MIMO model reference control scheme incorporating the variable structure theory for a vehicle four wheel steering system(4WS) is proposed and evaluated for a class of continuous-time nonlinear dynamics with known or unknown uncertainties. The scheme employs an neural network to identify the plant systems, where the neural network estimates the nonlinear dynamics of the plant. By the Lyapunov direct method, the algorithm is proven to be globally stable, with tracking errors converging to the neighborhood of zero. The merits of this scheme is that the global system stability is guaranteed and it is not necessary to know the exact structure of the system. With the resulting identification model which contains the neural networks, it does not need higher degrees of freedom vehicle model than 3 degree of freedom model. Th proposed scheme is applied to the active four wheel system and shows the validity is used to investigate vehicle handing performances. In simulation of the J-turn maneuver, the reduction of yaw rate overshoot of a typical mid-size car improved by 30% compared to a two wheel steering system(2WS) case, resulting that the proposed scheme gives faster yaw rate response and smaller side angle than the 2WS case.