• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.494-494
• /
• 2000

This paper demonstrates that the largest Lyapunov exponent $\lambda$ of recurrent neural networks can be controlled by a gradient method. The method minimizes a square error $e_{\lambda}=(\lambda-\lambda^{obj})^2$ where $\lambda^{obj}$ is desired exponent. The $\lambda$ can be given as a function of the network parameters P such as connection weights and thresholds of neurons' activation. Then changes of parameters to minimize the error are given by calculating their gradients $\partial\lambda/\partialP$. In a previous paper, we derived a control method of $\lambda$via a direct calculation of $\partial\lambda/\partialP$ with a gradient collection through time. This method however is computationally expensive for large-scale recurrent networks and the control is unstable for recurrent networks with chaotic dynamics. Our new method proposed in this paper is based on a stochastic relation between the complexity $\lambda$ and parameters P of the networks configuration under a restriction. Then the new method allows us to approximate the gradient collection in a fashion without time evolution. This approximation requires only $O(N^2)$ run time while our previous method needs $O(N^{5}T)$ run time for networks with N neurons and T evolution. Simulation results show that the new method can realize a "stable" control for larege-scale networks with chaotic dynamics.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1998년도 하계학술대회 논문집 B
• /
• pp.740-743
• /
• 1998

This paper presents an optimization algorithm for a stable Dynamic Neural Network (DNN) using genetic algorithm. Optimized DNN is applied to a problem of controlling nonlinear dynamical systems. DNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDNN has considerably fewer weights than DNN. The object of proposed algorithm is to the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling, a nonlinear dynamic system using the proposed optimized SDNN considering stability' is demonstrated by case studies.

• 제어로봇시스템학회논문지
• /
• 제22권10호
• /
• pp.779-787
• /
• 2016

A nonlinear control law for the quad-rotor of a low-complexity, global approximation-free from system uncertainties and external disturbances are described in this paper. The control law guarantees convergence to a small bounded error using a prescribed performance function. The stability of the proposed nonlinear control system is also proven by the Lyapunov stability theorem. The advantage of this technique is that it has a simpler form than any other nonlinear compensators and is applicable to any nonlinear systems without precise knowledge of the systems. In this paper, the proposed approach is applied to attitude/altitude control of a quad-rotor. Numerical simulations are performed to investigate the proposed nonlinear attitude control law by applying it to an uncertain quadcopter system with external disturbances.

• 로봇학회논문지
• /
• 제6권3호
• /
• pp.237-246
• /
• 2011

Robots have been used in many fields due to its performance improvement and variety of its functionality, to the extent which robots can replace human tasks. Individual feature and better performance of robots are expected and required to be created. As their performances and functions have increased, systems have gotten more complicated. Multi mobile robots can perform complex tasks with simple robot system and algorithm. But multi mobile robots face much more complex driving problem than singular driving. To solve the problem, in this study, driving algorithm based on the energy method is applied to the individual robot in a group. This makes a cluster be in a formation automatically and suggests a cluster the automatic driving method so that they stably arrive at the target. The energy method mentioned above is applying attractive force and repulsive force to a special target, other robots or obstacles. This creates the potential energy, and the robot is controlled to drive in the direction of decreasing energy, which basically satisfies lyapunov function. Through this method, a cluster robot is able to create a formation and stably arrives at its target.

• 제어로봇시스템학회논문지
• /
• 제22권6호
• /
• pp.429-434
• /
• 2016

This paper introduces a stabilization condition for polynomial fuzzy systems that guarantees $H_{\infty}$ performance under the imperfect premise matching. An $H_{\infty}$ control of polynomial fuzzy systems attenuates the effect of external disturbance. Under the imperfect premise matching, a polynomial fuzzy model and controller do not share the same membership functions. Therefore, a polynomial fuzzy controller has an enhanced design flexibility and inherent robustness to handle parameter uncertainties. In this paper, the stabilization conditions are derived from the polynomial Lyapunov function and numerically solved by the sum-of-squares (SOS) method. A simulation example and comparison of the performance are provided to verify the stability analysis results and demonstrate the effectiveness of the proposed stabilization conditions.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
• /
• pp.249-255
• /
• 2000

HRIV(Hybrid Rule-Interval Variation) method is presented to stabilize a class of nonlinear systems, where SMC(Sliding Mode Control) and ADC (ADaptive Control) schemes are incorporated to overcome the unstable characteristics of a conventional FLC(Fuzzy Logic Control). HRIV method consists of two modes: I-mode (Integral Sliding Mode PLC) and R-mode(RIV method). In I-mode, SMC is used to compensate for MAE(Minimum Approximation Error) caused by the heuristic characteristics of FLC. In R-mode, RIV method reduces interval lengths of rules as states converge to an equilibrium point, which makes the defined Lyapunov function candidate negative semi-definite without considering MAE, and the new uncertain parameters generated in R-mode are compensated by SMC. In RIV method, the overcontraction problem that the states are out of a rule-table can happen by the excessive reduction of rule intervals, which is solved with a dynamic modification of rule-intervals and a transition to I-mode. Especially, HRIV method has advantages to use the analytic upper bound of MAE and to reduce Its effect in the control input, compared with the previous researches. Finally, the proposed method is applied to stabilize a simple nonlinear system and a modified inverted pendulum system in simulation experiments.

• 전기학회논문지
• /
• 제59권7호
• /
• pp.1289-1294
• /
• 2010

In this paper, a new discrete integral static output feedback variable structure controller based on the a new integral dynamic-type sliding surface and output feedback discrete version of the disturbance observer is suggested for the control of uncertain linear systems. The reaching phase is completely removed by introducing a new proposed integral dynamic-type sliding surface. The output feedback discrete version of disturbance observer is presented for effective compensation of uncertainties and disturbance. A corresponding control with disturbance compensation is selected to guarantee the quasi sliding mode on the predetermined integral dynamic-type sliding surface for guaranteeing the designed output in the integral dynamic-type sliding surface from any initial condition for all the parameter variations and disturbances. Using discrete Lyapunov function, the closed loop stability and the existence condition of the quasi sliding mode is proved. Finally, an illustrative example is presented to show the effectiveness of the algorithm.

• Journal of Power Electronics
• /
• 제16권3호
• /
• pp.1067-1075
• /
• 2016

This paper presents an adaptive observer-based approach to estimate voltage parameters, including frequency, amplitude, and phase angle, for single-phase power systems. In contrast to most existing estimation methods of grid voltage parameters, in this study, grid voltage is treated as a dynamic system related to an unknown grid frequency. Based on adaptive observer theory, a full-order adaptive observer is proposed to estimate voltage parameters. A Lyapunov function-based argument is employed to ensure that the proposed estimation method of voltage parameters has zero steady-state error, even when frequency varies or phase angle jumps significantly. Meanwhile, a reduced-order adaptive observer is designed as the simplified version of the proposed full-order observer. Compared with the frequency-adaptive virtual flux estimation, the proposed adaptive observers exhibit better dynamic response to track the actual grid voltage frequency, amplitude, and phase angle. Simulations and experiments have been conducted to validate the effectiveness of the proposed observers.

• 한국전자통신학회논문지
• /
• 제2권2호
• /
• pp.67-74
• /
• 2007

채터링 현상은 VSS의 주요한 약점이며 이 문제를 극복하기위하여 많은 연구들이 발표되었다. 슬라이딩 섹터 이론이 최근에 발표되었으며, 섹터 안에서 제어입력 없이 상태의 놈이 감소하고 상태가 섹터 안에 있는 동안에 상태의 놈은 영으로 수렴한다. 슬라이딩 섹터 이론은 기본적인 연구단계에 있으며 선형시스템에 대하여 연구되어있다. 본 논문에서는 슬라이딩 섹터를 이용하여 불안정한 비선형시스템의 추적제어하는 새로운 방식을 제안하며 슬라이딩 섹터에 리아프노프 함수를 이용하여 안정도를 분석한다. 역진자 시스템에 컴퓨터 시뮬레이션을 통하여 제안한 슬라이딩 섹터 제어가 채터링을 줄일 수 있는 것을 확인한다.

• 한국정보통신학회논문지
• /
• 제8권4호
• /
• pp.807-814
• /
• 2004

채터링 현상은 VSS의 주요한 약점이며 이 문제를 극복하기위하여 많은 연구들이 발표되었다. 슬라이딩 섹터 이론이 최근에 발표되었으며, 섹터 안에서 제어입력 없이 상태의 놈이 감소하고 상태가 섹터 안에 있는 동안에 상태의 놈은 영으로 수렴한다. 슬라이딩 섹터 이론은 기본적인 연구단계에 있으며 선형시스템에 대하여 연구되어있다. 본 논문에서는 슬라이딩 섹터를 이용하여 불안정한 비선형시스템의 안정화하는 새로운 방식을 제안하며 슬라이딩 섹터에 리아프노프 함수를 이용하여 안정도를 분석한다. 역진자 시스템에 컴퓨터 시뮬레이션을 통하여 제안한 슬라이딩 섹터 제어가 채터링을 줄일 수 있는 것을 확인한다.