• Journal of Advanced Marine Engineering and Technology
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• v.28 no.6
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• pp.946-961
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• 2004

A new adaptive controller which combines a model reference adaptive controller (MRAC) and a fuzzy controller is developed for unstable nonlinear time-invariant systems. The fuzzy controller is used to analyze and to compensate the nonlinear time-invariant characteristics of the plant. The MRAC is applied to control the linear time-invariant subsystem of the unknown plant, where the nonlinear time-invariant plant is supposed to comprise a nonlinear time-invariant subsystem and a linear time-invariant subsystem. The stability analysis for the overall system is discussed in view of global asymptotic stability. In conclusion. the unknown nonlinear time-invariant plant can be controlled by the new adaptive control theory such that the output error of the given plant converges to zero asymptotically.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1087-1094
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• 2014

In general, a speaker generates nonlinear distortions owing to a design principle, manufacturing process, and so on, which decreases and changes a sound quality. So, if the speaker is linearized by compensating these nonlinear distortions, the sound quality and satisfaction can be increased. In this paper, a new adaptive controller was proposed to be applied for compensating nonlinear distortions of a speaker. Through computer simulations as well as the analytical analysis, it could be shown that it is possible for both conventional adaptive controller and proposed adaptive controller, to be applied for linearizing the speaker with nonlinear distortions. Also, the simulations results demonstrated that the proposed adaptive controller may have faster convergence speed and better capability of compensating the nonlinear distortion than the conventional adaptive controller with nearly equal computation complexity.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.17-20
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• 2001

In this paper, we design a nonlinear adaptive controller using wavelet neural network. The method proposed in this paper performs for a nonlinear system with unknown parameters, identification with using a wavelet neural network, and then a nonlinear adaptive controller is designed with those identified informations. The advantage of the proposed control method is simple to design a controller for unknown nonlinear systems, because we use the identified informations and design parameters are positioned within a negative real part of s-plane. The simulation results showed the effectiveness of proposed controller design method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.582-589
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• 1996

In this paper, an adaptive control problem of a hydraulic actuator for vehicle active suspension controller is divided into two parts: the inner loop controller and the outer loop controller. Inner loop controller, which is a nonlinear adaptive controller, is designed to control the force generated by the nonlinear hydraulic actuator acting under the effects of Coulomb friction. For simplicity of designing a nonlinear controller, the spool valve dynamics of a hydraulic actuator is reduced using a singular perturbation technique. The estimation error signal used to an indirect parameter adaptation is calculated without a regressor filtering. The absolute velocity of a sprung mass will be damped down by its negatively proportional term(sky-hook damper) adopted as an outer loop controller. Simulation results are presented to show the importance of controlling the actuator force and the validity of the proposed adaptive controller. (author). refs., figs. tab.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.314-317
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• 1996

In this paper, we present a nonlinear adaptive controller for position tracking of induction motors. In constructing the adaptive controller, a backstepping approach is used under the condition of full state information, while a nonlinear observer is adopted for rotor flux estimation. The adaptive controller is shown to drive the state variables of system to the desired ones asymptotically and whose effectiveness is also shown via computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.333-336
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• 1997

In this paper, we present a nonlinear dynamic controller for position tracking of brushless dc motors. In constructing the controller, a backstepping-type approach is used under the condition of full state information, while an adaptive controller is adopted for parameter uncertainty throughout the entire electromechanical system. The nonlinear dynamic controller using the adaptive learning technique approach is shown to drive the state variables of system to the desired ones asymptotically and whose effectiveness is also sown via computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.401-405
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• 1993

This paper presents a neuro adaptive control method for nonlinear dynamical systems based on artificial neural network systems. The proposed neuro adaptive controller consists of 3 layers artificial neural network system and parallel PD controller. At the early stage in learning or identification process of the system characteristics the PD controller works mainly in order to compensate for the inadequacy of the learning process and then gradually the neuro contrller begins to work instead of the PD controller after the learning process has proceeded. From the simulation studies the neuro adaptive controller is seen to be robust and works effectively for nonlinear dynamical systems from a practical applicational points of view.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.633-635
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• 1997

In this paper we present a scheme of adaptive backstepping controller for nonlinear system. Backstepping approach has recently been adopted as a design tool for nonlinear control and especially backstepping with modular design used to seperately design controller and identifier. In the modular design the nonlinear damping term is contained in controller for input-to-state stability (ISS). We compare the ISS controller, which used in general case, with the weak-ISS controller that attenuates the effect of nonlinear damping term and prove their advantages and disadvantages by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1292-1295
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• 1996

This paper proposes an adaptive control technique for the autopilot design of STT missile. Dynamics of the missile is highly nonlinear and the equilibrium point is vulnerable to change due to fast maneuvering. Therefore nonlinear control techniques are desirable for the autopilot design of the missile. The nonlinear controller requires the exact model to obtain satisfactory performance. Generally a look-up table is used for the dynamic coefficients of a missile, so there must be coefficients error during actual flight, and the performance of the nonlinear controller using these data can be degraded. The proposed adaptive control technique compensates the nonlinear controller with modeling error resulting from the error of aerodynamic data and disturbance. To investigate the usefulness, the proposed method is applied to autopilot design of STT missile through simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.