• 대한기계학회논문집A
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• 제21권11호
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• pp.1809-1818
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• 1997

Mathematical models of industrial robots or manipulators are highly nonlinear equations with nonlinear coupling between the variables of motion. As the working speed has been fast, the effects of nonlinear terms have become serious. So the control algorithm based on approximately linearized equation looses the efficiency. In order to design the control law for the nonlinear models, Hunt-Su's nonlinear transformation method and Marino's feedback equivalence condition are used with linear quadratic regulator(LQR) theory in this study. Nonlinear terms of the system are eliminated and coupled terms are decoupled by this feedback law. This method is applied to a 3-D.O.F. vertical articulated manipulator by both experiments and simulations and compared with PID control which is widely used in the industry.

• 전기학회논문지
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• 제56권5호
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• pp.966-972
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• 2007

This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.

• Journal of Power Electronics
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• 제18권5호
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• pp.1380-1397
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• 2018

The synchronous reluctance motor (SynRM) servo-drive system has highly nonlinear uncertainties owing to a convex construction effect. It is difficult for the linear control method to achieve good performance for the SynRM drive system. The nonlinear backstepping control system using upper bound with switching function is proposed to inhibit uncertainty action for controlling the SynRM drive system. However, this method uses a large upper bound with a switching function, which results in a large chattering. In order to reduce this chattering, a nonlinear backstepping control system using an adaptive law is proposed to estimate the lumped uncertainty. Since this method uses an adaptive law, it cannot achiever satisfactory performance. Therefore, a nonlinear backstepping control system using a reformed recurrent Hermite polynomial neural network with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SynRM drive system. Further, the reformed recurrent Hermite polynomial neural network with two learning rates is derived according to an increment type Lyapunov function to speed-up the parameter convergence. Finally, some experimental results and a comparative analysis are presented to verify that the proposed control system has better control performance for controlling SynRM drive systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.526-529
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• 1998

In this paper, we proposed a nonlinear sliding mode controller to regulate the swinging angle of Overhead Crane System. Roughly speaking, the controller is designed to regulate an output(the swing angle) while providing internal stability. It is difficult to apply many of standard nonlinear control design techniques. In contrast to control that use a command generator and possibly a time-varying feedback, our control law is simple autonomous nonlinear controller. We analyze the stability of the closed-loop system using an $L_2$ Sliding surface conditions approach on a nonlinear feedback linearization of the system about the desired periodic orbit. One can easily extend this approach to analyze the robustness of the control system with respect to disturbances and parameter variations.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집A
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• pp.692-697
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• 2001

A precise tracking control scheme on the system in presence of nonlinear dynamic friction is proposed. In this control scheme, the standard SMC is combined with the nonlinear observer to estimate the dynamic friction state that is impossible to measure. Then this control scheme has the good tracking performance and the robustness to parameter variation compared with the standard SMC and the PiD based nonlinear observer control system. This fact is proved by the experiment on the ball-screw driven servo system with the dynamic friction model.

• 전기의세계
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• 제16권3호
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• pp.16-20
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• 1967

To analyze the stability creterion and the dynamic performance of the nonlinear reactor control system which involve the on-off element and gear backlash, the concept of discribing function is developed for the system of two nonlinear elements are connected by linear element. Using the derived discribing function and frequency responce method, the stability creterion and the dynamic performance of the nonlinear reactor control system are analyzed, and the results of the analysis are conformed by analog computor.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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• pp.33-36
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• 2002

In this paper, we propose a control method of an unknown nonlinear system using a dynamical neural network. The method proposed in this paper performs for a nonlinear system with unknown system, identification with using the dynamical neural network, and then a nonlinear adaptive controller is designed with these identified informations. In order to verify the effectiveness of the proposed algorithm, we simulated one-link manipulator. The simulation result showed the effectiveness of using the dynamical neural network in the adaptive control of one-link manipulator.

• 전기학회논문지
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• 제59권6호
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• pp.1173-1178
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• 2010

In this note, a systematic design of a new robust nonlinear integral variable structure controller based on state dependent nonlinear form is presented for the control of uncertain more affine nonlinear systems with mismatched uncertainties and matched disturbance. After an affine uncertain nonlinear system is represented in the form of state dependent nonlinear system, a systematic design of a new robust nonlinear integral variable structure controller is presented. To be linear in the closed loop resultant dynamics and remove the reaching phase problems, the linear integral sliding surface is suggested. A corresponding control input is proposed to satisfy the closed loop exponential stability and the existence condition of the sliding mode on the linear integral sliding surface, which will be investigated in Theorem 1. Through a design example and simulation studies, the usefulness of the proposed controller is verified.

• 전기학회논문지
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• 제59권5호
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• pp.945-949
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• 2010

In this paper, a systematic design of a new robust nonlinear variable structure controller based on state dependent nonlinear form is presented for the control of uncertain affine nonlinear systems with mismatched uncertainties and matched disturbance. After an affine uncertain nonlinear system is represented in the form of state dependent nonlinear system, a systematic design of a new robust nonlinear variable structure controller is presented. To be linear in the closed loop resultant dynamics, the linear sliding surface is applied. A corresponding control input is proposed to satisfy the closed loop exponential stability and the existence condition of the sliding mode on the linear sliding surface, which will be investigated in Theorem 1. Through a design example and simulation study, the usefulness of the proposed controller is verified.

• 제어로봇시스템학회논문지
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• 제14권3호
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• pp.254-262
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• 2008

In this paper, we propose an adaptive neural dynamic surface control (DSC) approach with $H_{\infty}$ tracking performance for full dynamics of nonlinear flight systems. It is assumed that the model uncertainties such as structured and unstrutured uncertainties, and external disturbances influence the nonlinear aircraft model. In our control system, self recurrent wavelet neural networks (SRWNNs) are used to compensate the model uncertainties of nonlinear flight systems, and an adaptive DSC technique is extended for the disturbance attenuation of nonlinear flight systems. All weights of SRWNNs are trained on-line by the smooth projection algorithm. From Lyapunov stability theorem, it is shown that $H_{\infty}$ performance nom external disturbances can be obtained. Finally, we present the simulation results for a nonlinear six-degree-of-freedom F-16 aircraft model to confirm the effectiveness of the proposed control system.