• Smart Structures and Systems
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• 제33권4호
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• pp.281-290
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• 2024

This work investigates the mechanical energy harvesting from smart and adaptive devices using magnetic interactions. The energy harvester is built from an elastic beam connected to an electric circuit by a magnetostrictive material that promotes energy transduction. Besides, magnetic interactions define the system stability characterizing multistable configurations. The adaptiveness is provided by magnets that can change their position with respect to the beam, changing the system configuration. A mathematical model is proposed considering a novel model to describe magnetic interactions based on the single-point magnet dipole method, but employing multiple points to represent the magnetic dipole, which is more effective to match experimental data. The adaptive behavior allows one to alter the system stability and therefore, its dynamical response. A nonlinear dynamics analysis is performed showing the possibilities to enhance energy harvesting capacity from the magnet position change. The strategy is to perform a system dynamical characterization and afterward, alter the energetic barrier according to the environmental energy sources. Results show interesting conditions where energy harvesting capacity is dramatically increased by changing the system characteristics.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.388-388
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• 2000

The complexity of nonlinear systems makes it difficult to ascertain their behavior using classical methods of analysis. Many efforts have been focused on the advanced algorithms and techniques that hold the promise of improving real-time optimal control while at the same time providing higher accuracy. In this paper, a fuzzy cell mapping method of real-time optimal control far nonlinear dynamical systems is proposed. This approach combines fuzzy logic with cell mapping techniques in order to find the optimal input level and optimal time interval in the finite set which change the state of a system to achieve a desired obiective. In order to illustrate this method, we analyze the behavior of an inverted pendulum using fuzzy cell mapping.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.804-806
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• 1995

This paper discusses Self-organized Distributed Networks(SODN) as identifier of nonlinear dynamical systems. The structure of system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. The learning with the proposed SODN is fast and precise. Such properties arc caused from the local learning mechanism. Each local networks learns only data in a subregion. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the SODN. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.939-942
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• 1996

This paper presents a stable learning algorithm for diagonal recurrent neural network(DRNN). DRNN is applied to a problem of controlling nonlinear dynamical systems. A architecture of DRNN is a modified model of the Recurrent Neural Network(RNN) with one hidden layer, and the hidden layer is comprised of self-recurrent neurons. DRNN has considerably fewer weights than RNN. Since there is no interlinks amongs in the hidden layer. DRNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. To guarantee convergence and for faster learning, an adaptive learning rate is developed by using Lyapunov function. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed algorithm is demonstrated by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.985-989
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• 2005

This paper presents chaos synchronization between two different chaotic systems using nonlinear control method. The proposed technique is applied to achieve chaos synchronization for the Lorenz and Rossler dynamical systems. Numerical simulations are also implemented to verify the results.

• 대한의용생체공학회:의공학회지
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• 제14권4호
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• pp.379-386
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• 1993

This paper describes design of a Chaos analyser and its applications to analysis of nonlinear characteristirs of ECG. The proposed system can easily distinguish chaotic system among the various dynamical systems by chaotic quantitative and qualitative analysis and also chaotic characteristics which represents states of nonlinear dynamical system. And we have also proposed new possibilities to recognize abnormal state of ECG signal using the chaotic characteristics.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.56-60
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• 1992

This paper addresses the problem of designing a neural network based controller for a discrete-time nonlinear dynamical system. Using two multi-layered neural networks we first design an indirect controller the weights of which are updated by the informations obtained from system identification. The weight update is executed by parameter optimization method under Lagrangian formulation. For the nonlinear dynamical system, we define several cost functions and by computer simulations analyze the control performances of them and the effects of penalty-weighting values.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.118-123
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• 1989

This work deals with fast-to-compute global control laws for time-optimal motion of strongly nonlinear dynamic systems like resolute robots. the theory of cell-to-cell mappings for dynamical systems offer the possibility of doing the vast majority of the control law computation offline in case of time optimization with constrained inputs. These cells result from a coarse discretization of likely swaths of state space into a set of nonuniform, contiguous volumes of relatively simple shapes. Once the cells have been designed, the bang-bang schedules for the inputs are determined for all likely starting cells and terminating cells. the resulting control law is an open-loop optimal control law with feedback monitoring and correction.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1262-1266
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• 2003

Fuzzy sliding mode controller for a class of uncertain nonlinear dynamical systems is proposed and analyzed. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. Sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum. The results show that both alleviation of chattering and performance are achieved.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.