• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.70-73
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• 1996

Many researchers had considered biological systems as linear systems. In many cases of biological systems, the phenomena that show the regular and periodic dynamics are considered the normal state. However, some clinical experiments reported, in some cases, the periodic signals represented the abnormal state. We assume that signals from human body system are generated from deterministic, intrinsic mechanisms and can be represented a simple equation that show nonlinear dynamics dependent on control parameters. The objective of our study is to model a nonlinear dynamics correctly from the nonlinear time series using the genetic programming method; to find a simple equation of nonlinear dynamics using collected time series and its nonlinear characteristics. We applied genetic programming to model RR interval of ECG that shows chaotic phenomena. We used 4 statistic measures and 2 fractal measures to estimate fitness of each chromosome, and could obtain good solutions of which chaotic features are similar.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.675-677
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• 2004

This paper presents a nonlinear controller design for optical disk drive systems to improve anti-shock performance. The nonlinear anti-shock controller is added parallel to the original linear servo control loop. In the previous work, dead-zone nonlinear element is used for nonlinear controller and PID control method is used for linear controller. Although this strategy improves anti-shock performance, it has a narrow stability bound. In this paper, we propose dead-zone with saturation nonlinear element for the nonlinear controller. Since this nonlinear element improves stability margin, we can use higher gain of dead-zone than the controller with dead-zone only. In the linear controller design, we show that lead-lag control has improved stability margin over PID control. Numerical simulation results show that the proposed method can get better performance to the external shock than previously proposed method.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.160-170
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• 2008

Temperature control of an enclosed thermal system which has many applications including Rapid Thermal Processing (RTP) of semiconductor wafers showed an input-constraint violation for nonlinear controllers due to inherent strong coupling between the elements [1]. In this paper, a constrained nonlinear optimal control design is developed, which accommodates input constraints using the linear algebraic equivalence of the nonlinear controllers, for the temperature control of an enclosed thermal process. First, it will be shown that design of nonlinear controllers is equivalent to solving a set of linear algebraic equations-the linear algebraic equivalence of nonlinear controllers (LAENC). Then an input-constrained nonlinear optimal controller is designed based on that LAENC using the constrained linear least squares method. Through numerical simulations, it is demonstrated that the proposed controller achieves the equivalent performances to the classical nonlinear controllers with less total energy consumption. Moreover, it generates the practical control solution, in other words, control solutions do not violate the input-constraints.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.259-267
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• 2003

In this paper, we propose a nonlinear variable PID controller using a cell-mediated immune response. An immune feedback response is based on the functioning of biological T-cells. An immune feedback response and P-controller of conventional PID controllers resemble each other in role and mechanism. Therefore, we extend immune feedback mechanism to nonlinear PE controller. And in order to choose the optimal nonlinear PID controller games, we also propose the on-line tuning algorithm of nonlinear functions parameters in immune feedback mechanism. The trained parameters of nonlinear functions are adapted to the variations of the system parameters and any command velocity. And the adapted parameters obtained outputs of nonlinear functions with an optimal control performance. To verify performances of the proposed control systems, the speed control of nonlinear BC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system variations.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.460-465
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• 2004

In this paper, we present some properties on receding horizon $H_{\infty}$ control for nonlinear discrete-time systems. First, we propose the nonlinear inequality condition on the terminal cost for nonlinear discrete-time systems. Under this condition, noninceasing monotonicity of the saddle point value of the finite horizon dynamic game is shown to be guaranteed. We show that the derived condition on the terminal cost ensures the closed-loop internal stability. The proposed receding horizon $H_{\infty}$ control guarantees the infinite horizon $H_{\infty}$ norm bound of the closed-loop systems. Also, using this cost monotonicity condition, we can guarantee the asymptotic infinite horizon optimality of the receding horizon value function. With the additional condition, the global result and the input-to-state stable property of the receding horizon value function are also given. Finally, we derive the stability margin for the saddle point value based receding horizon controller. The proposed result has a larger stability region than the existing inverse optimality based results.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.78-83
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• 2003

In this paper, it is dealt with a controller design problem for nonlinear systems with higher order relative degree. A robust adaptive control for uncertain nonlinear systems with stable zero dynamics will be proposed based on the high-gain adaptive output feedback and backstepping strategies. The proposed method is useful in the case where only the output signal is available.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.139-145
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• 2002

In this paper, we proposed an intelligent digital redesign method for a class of fuzzy-model-based controllers, effective fur stabilization of continuous-time nonlinear systems. The TS fuzzy model is used to expend the results of the digital redesign technique to nonlinear systems. The proposed method utilized the recently developed LMI technique to obtain a digitally redesigned fuzzy-model-based controller. The intelligent digital redesign problem is converted to equivalent problem, and the LMI method is used to find the digitally redesigned fuzzy-model-based controller. The stabilization conditions of TS fuzzy model are derived for stabilization in the sense of Laypunov stability. In order to demonstrates the effectiveness and feasibility of the proposed controller design methodology, we applied this method to the single link flexible-joint robot arm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.354-358
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• 1994

This study is concerned with H$_{\infty}$ control theory of nonlinear systems. Recently H$_{\infty}$ control theory has been developed to nonlinear systems, and especially nonlinear H$_{\infty}$ control theory based on the Hamilton-Jacobi inequality has been proposed. This corresponds to linear H$_{\infty}$ control theory based on the Riccati equation. In this paper, we apply it to a semi-active dynamic vibration absorber for multi-degree-of-freedom structure, and we design its state feedback controller via the Riccati equation. In the simulation, we show that it is effective for a vibration control.rol.