• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.9-15
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• 1998

This paper presents an adaptive learning control method for unknown nonlinear systems by combining neuro control and iterative learning control techniques. In the present control system, an iterative learning controller (ILC) is used for a process of short term memory involved in a temporary adaptive and learning manipulation and a short term storage of a specific temporary action. The learning gain of the iterative learning law is estimated by using a neural network for an unknown system except relative degrees. The control informations obtained by ILC are transferred to a long term memory-based feedforward neuro controller (FNC) and accumulated in it in addition to the previously stored infonnations. This scheme is applied to a two link robot manipulator through simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.23-29
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• 2016

This paper presents balancing control of a two-wheel mobile robot (TWMR). TWMR is aimed to maintain balance while moving. Although TWMR can be controlled by linear controllers such as PD controller, time-delayed controller is employed for robustness. Performances of PD controllers and time-delayed controllers are compared. Especially, experimental studies on different acceleration estimation for the time-delayed controller are conducted. Performances by different acceleration estimations of the balancing angle, of the position, and of both angle and position are compared empirically.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.9
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• pp.45-52
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• 2006

In this study, a nonlinear control by feedback linearization method, one of nonlinear control schemes based on the nonlinear model, is proposed to suppress the flutter of a supersonic composite panel using piezoelectric materials. Most of the previous panel flutter controllers are the LQR(Linear Quadratic Regulator) which is based on the linear model. A nonlinear feedback linearizing controller proposed in this study considers the nonlinear characteristics of the system model. We use the actuator implemented by piezoceramic PZT. Using the principle of virtual displacements and a finite element discretization with the conforming four-node rectangular element, we first derive the discretized dynamic equations of motion, which are transformed into a nonlinear coupled-modal equations of motion of state space form. The effectiveness of the proposed method is also compared with the LQR based on the linear model through numerical simulations in the time domain using the Newmark method.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.583-592
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• 1999

In order to control systems which contain nonlinearities control strategies must deal with the effects of them. Since most of control methods based on system mathematical models have been mainly developed focused on stability robustness against nonlinearities or uncertainties under the assumption that controlled systems are linear time invariant they have certain amount of limita-tions to smartly improve control perfomances of systems disturbed by nonlinearities or uncertainties. In this paper the fuzzy PID control law is suggested which can improve control performances of the nonlinear heavy load hydraulic systems disturbed by nonlinearities and uncertainties. Although the derivation process is based on the design process similar to general fuzzy logic con-troller resultant control law has analytical forms with time varying PID gains rather than linguis-tic forms so that implementation using commn-used versatile microprocessors can be achieved easily and effectively in real-time control aspect.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.6
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• pp.1-8
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• 2002

This paper presents a generalized minimum-variance self-tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior and time delays. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation is done to adapt the nonlinear nonminimum phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct adaptive controller using neural network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.5
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• pp.667-675
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• 2002

In this Paper, we present a new nonlinear active noise control scheme using neural networks. Two neural network4 are used, one is for the active controller and another one is for the secondary path model. This scheme is suitable for the plant which has time-varing secondary path dynamics, because the secondary path modeling is performed via on-line fashion. Simulation results of active noise control with nonlinear primary/secondary path are presented. The results show that the new algorithm can reduce the noise level greatly.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.600-606
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• 2015

Applications of the drone have been enlarged and study on the quad-copter system has been widely progressed. Quad-copter system is raised vertically with four propellers, and it is free to move side to side, and upper and lower. It is also a typical example of non-linear systems. In this paper, we design two-input fuzzy logic control systems in order to control the quad-copter that is complex nonlinear system. And then we analyze their control rule tables and derive some characteristics that they present skew symmetric property and the control actions are enhanced as the distance from the diagonal band. This property enables the design of other control systems. We here design simple-structured fuzzy logic control systems and simulate them. We confirm some effects of the proposed systems and finally discuss about them.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.95-100
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• 2001

In this paper, we proposed the robust control scheme for a class of nonlinear dynamical systems using output feedback linearization method. The presented control scheme is based on the VSS. We assume that the nonlinear dynamical system is minimum phase, the relative degree of the system is r＜n and zero dynamics is stable. It is also shown that the global asymtotically stability is guaranted. And we verified that the proposed control scheme Is the feasible through a computer simulation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.1
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• pp.1-13
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• 2001

Due to the friction characteristics of cylinders and the rail of a passenger car, in the elevator actuated with hydraulic systems, there exist dead zones, which can not be controlled by a PID controller. To overcome the drawbacks, in this paper, we first try a hybrid controller which switches between a fuzzy logic controller and a PID controller. However, because the hybrid control scheme uses only a single type controller, except the switched layer, the high control performance can not be achieved. To solve this problem, we propose a new type fuzzy hybrid control scheme, which outputs of the output mixer arc controlled by a fuzzy logic. The hydraulic elevator system controlled by inverters has more then one switched layers due to the highly nonlinear characteristics. The proposed fuzzy hybrid control scheme achieves improved control performances by using both controllers with weighted outputs depend on the system status, to achieve improved control performances. The effectiveness of the proposed control scheme arc shown by simulation results, which the proposed fuzzy hybrid control method yields good control performance not only in the zero crossing speed region but also in the overall control region including steady-state region.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.268-274
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• 2013

Any new method for controlling a nonlinear system has widely been reported. An inverted pendulum system has typically been used as a target system for demonstrating its usefulness. In this paper, we propose an algorithm to control a flexible joint cart based inverted pendulum system. Two carts are connected with a spring and one is a driving cart and the other is no driving cart with a pole. We here present a system modeling and a good fuzzy logic based control algorithm. We also introduce LQR (Linar Quadratic Regulator) technique for reducing the number of control variables. By using this technique, the number of input variables for a fuzzy logic controller is become only two not six. So the computational complexity is largely reduced. Moreover, a two-input fuzzy logic controller has a control rule table with a skew-symmetric property. And it will lead the design of a single-input fuzzy logic controller. In order to demonstrate the usefulness of the proposed method and prove the superiority of the proposed method, some computer simulations are presented.