• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.533-538
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• 2002

We propose a direct adaptive controller for a class of nonlinearly parametrized systems. A normalized output error is added to the previous control law to construct the proposed adaptive controller. The transient performance of the resulting closed-loop system can be guaranteed by suitably choosing a novel kind of Lyapunov function while the tracking performance of the adaptive control system is improved using the proposed algorithm. Finally, the effectiveness of the proposed algorithm is illustrated with computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.18-18
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• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

• 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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• 2001.10a
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• pp.54.4-54
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• 2001

This paper proposes an adaptive control scheme for an autopilot design of Relaxed-Static-Stability(RSS) Missiles with saturating actuator. The feedback linearization controller eliminates nonlinear terms in RSS missile dynamics and makes the entire system linear. But modeling errors, disturbances and the nonlinear mismatch due to input constraints exert a bad influence on the performance of the feedback linearization controller Thus, first, we derive a parametric affine uncertainty model with modeling errors and disturbances. Then an adaptive control law with anti-windup scheme is developed, where the bounds of uncertainties are estimated with adaptive laws. The proposed adaptive controller can remove the bad effects of uncertainties, of disturbances, and of saturating actuator ...

• Journal of Power System Engineering
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• v.21 no.4
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• pp.42-50
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• 2017

A velocity controller using a modified model reference adaptive controller (M-MRAC) and a projection operator for a fish sorting belt conveyor system with uncertainty parameters, input saturation and bounded disturbances is proposed in this paper. To improve the tracking performance and robustness of the proposed controller in the presence of bounded disturbances, the followings are done. Firstly, the reference model for the conventional model reference adaptive controller (CMRAC) is replaced by a modified reference model for a M-MRAC to reduce unexpected high frequency oscillation in control input signal when the adaptation rate is increased. Secondly, estimated parameters in an adaptive law are varied smoothly under bounded external disturbances and a projection operator is utilized in an adaptive law for the proposed M-MRAC controller to be robust. Thirdly, an auxiliary error vector is introduced for compensating the error dynamics of the system when the saturation input occurs. Finally, the experimental results are shown to verify the better effectiveness and performance of the proposed controller under the bounded disturbance and saturated input than that of a CMRAC.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.647-655
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• 2001

In adaptive neuro-control, neural networks are used to approximate unknown plant nonlinearities. Until now, most of the studies in the field of controller design for nonlinear system using neural network considers the affine system with fixed number of neurons. This paper considers nonaffine nonlinear systems and on-line variation of the number of neurons. A control law and adaptive laws for neural network weights are established so that the whole system is stable in the sense of Lyapunov. In addition, at the expense of th input, tracking error converges to the arbitrary small neighborhood of the origin. The efficiency of the proposed scheme is shown through simulations ofa simple nonaffine nonlinear system.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.100-107
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• 1997

A pneumatic artificial muscle type of actuator, which acts similar to human muscle, is developed recently. In this paper, an adaptive controller is presented for the trajectory tracking problem of a two-degree- of-freedom manipulator using two pairs of pneumatic artificial muscle actuators. Due to the nonlinearity and the uncertainty on the dynamics of the actuator, it is difficult to make the effective control schemes of this system. By the adaptive control law which inclueds a nonlinear "feedforward" term compensating paramet- ric uncertainties in addition to P.I.D. scheme, both golbal stability of the system and convergence of the tracking error are guaranted. The effectiveness of the proposed control method for the manipulator using this actuator is illustrated through experiments.periments.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.624-629
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• 1993

In this paper, a model reference adaptive control scheme is applied to the normal acceleration controller for missiles with nonminimum-phase characteristics. The proposed scheme has an auxiliary compensator, an identifier of plant parameters and a feedback control law. First, plant parameters are estimated by the identifier and based the parameter estimates the coefficients of the compensator are calculated so that the estimated plant model with the compensator becomes minimum-phase. In this calculation, Nehari Algorithm is used. Parameters of the control law are then updated so that the extended plant model follows the given reference model. It is shown that the performance of the designed controller is satisfied via computer simulations.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.565-568
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• 2010

A pressure control law to regulate mass flow rate of gas generator is suggested. The governing equation is modeled by considering the burning rate of solid propellant and the conservation equation of gas generator. And then, a classical control law is applied after verifying the accuracy of dynamic model through comparing with ground test and internal ballistic results. The results show degradation of performance as shown in typical time varying system. To overcome this problem, an adaptive scheme is suggested and the performance is verified through numerical simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.267-270
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• 1994

To increase the robustness of tile feedforward tracking control system, a new discrete time sliding function has been defined and utilized for the formulation of control law, In adaptive case the robustness is achieved by using both a normalized gradient algorithm with deadzone and a sliding function-based nonlinear feedback, while in nonadaptive case by using only a sliding function-based nonlinear feedback.