• Journal of Information Processing Systems
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• v.18 no.4
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• pp.510-523
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• 2022

Considering the problems of poor control effect, weak disturbance rejection ability and adaptive ability of two-wheeled self-balanced robot (TWSBR) systems on undulating roads, this paper proposes a fuzzy neural network active disturbance rejection controller (FNNADRC), that is based on fuzzy neural network (FNN) for online correction of active disturbance rejection controller (ADRC)'s nonlinear control rate. Firstly, the dynamic model of the TWSBR is established and decoupled, the extended state observer (ESO) is used to compensate dynamically and linearize the upright and displacement subsystems. Then, the nonlinear PD control rate and FNN are designed, and the FNN is used to modify the control parameters of the nonlinear PD control rate in real time. Finally, the proposed control strategy is simulated and compared with the traditional ADRC and fuzzy active disturbance rejection controller (FADRC). The simulation results show that the control effect of the proposed control strategy is slightly better than ADRC and FADRC.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.6
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• pp.517-525
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• 2015

A grid connected inverter must be operated as the main electricity source under an isolated condition caused by the grid problem. Conventionally, the dual loop controller is used for the grid inverter, and the controller is used for control under the stand-alone mode. Generally, the PI(Proportional - Integral) controller is highly efficient under a synchronous reference frame, and stable control can be available. However, in this synchronous frame-based control, high-quality DSP is required because many sinusoidal calculations are necessary. When the PI control is conducted under a stationary frame, the controller constructions are made simple so that they work even with a low-price micro controller. However, given the characteristics of the PI controller, it should be designed with the phase of reference voltage considered. Otherwise, the phase delay of the output voltage can occur. Although the current controller also has a higher bandwidth than the voltage controller, distortion of the voltage is difficult to avoid only by the rapid response of the PI controller, as a sudden load change can occur in the nonlinear load. In this study, a new control method that solves the voltage controller bandwidth problem and rapidly copes with it even in the nonlinear load situation is proposed. The validity of the proposed method is proved by simulation and experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1557-1560
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• 1997

In this paper, we have built an experimental magnetic levitation system for a possible use of control education. We have give a mathermatical model of the nonlinear system and have shown the stability region of the linearized system when it is controlled by a PD controller. We also proposed a neural network control system which uses a neural network as a feedforward controller thgether with a conventional feedback PF controller. We have generated a desired output trajectory, which was designed for the benefit of the generalization of the neural network controller, and trained the desired output trajectory, which was desigend for the benefit of the generalization of the neural netowrk controller, and trained a neural network controller with the data of the actual input and the output of the system obtained by applying the desired output trajectroy. A good tracking performance was observed for both the desired trajectiories used and not used for the neural network training.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.143-146
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• 1997

In this paper, we propose a fuzzy-supervised nonlinear H$_{\infty}$ controller which guarantees the robustness and has exact tracking performance for robot manipulator with system parameter uncertainty and exogenous disturbance, The proposed controller which is based on robotic H$_{\infty}$ controller has fuzzy supervisor which decides the optimal control input weighting value through fuzzy making-decision process. Owing to the fuzzy supervisor, The proposed controller can take the optimal control input. Then, we will apply the proposed controller to rigid robot manipulator to verify the performance of our controller.r.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.5
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• pp.65-72
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• 2000

This paper presents the study of the tension control in a web transfer system. In this study the sliding mode controller is applied to a time-varying nonlinear mathematical model. The model was derived to consider the effects of changing the roll radius in tension variation during winding and unwinding. The uncertainty in modeling may be due to unmodelled dynamics, on variations in system model. Designed sliding mode controller made the system error always staying in the suggested surface from the beginning. Through this, system is maintained to be robust against a disturbance and uncertainty. To verify the designed controller has a good performance, various inputs such as desired velocity, step input, and trapezoidal input are applied. When the sliding mode controller was used, the system(the tension control) performance was improved comparing to the PID controller. The robustness of the controller with respect to an estimation error was verified through simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.9
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• pp.1264-1269
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• 2013

In this paper, we study a disturbance observer (DOB) based controller for an EMS(Electro-Magnetic Suspension) system in presence of mass uncertainty and input disturbance. The DOB based controller is employed in order to compensate the modeling uncertainty and attenuate disturbance signals. For the design of DOB based controller, the Jacobain linearization of nonlinear system model equation is used. Computer simulation is carried out for nonlinear model in order to compare the performance of the proposed DOB controller with that of the conventional PID controller. The simulation results show that the substantial improvement in the performance can be achieved by the proposed DOB controller.

• Journal of Institute of Control, Robotics and Systems
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• v.1 no.1
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• pp.25-32
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• 1995

Nonlinear depth control algorithms for the hovering system of underwater vehicles are presented. In this paper, a nonlinear effect in heave motion for underwater vehicles, a deadzone effect of the flow control valve in the hovering tank and an impact disturbance are considered. In this situation, in order to choose a desirable controller, sliding mode controller and fuzzy sliding mode controller are designed and compared. The computer simulation results show that the fuzzy sliding mode control system is more suitable in order to maintain a desirable depth of an underwater vehicle with a deadzone and impact disturbance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• Journal of Industrial Technology
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• v.18
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• pp.303-308
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• 1998

In this paper a neural network controller called "Feedback-State Learning" for control of the attitude of a wheeled inverted pendulum is presented. For the controller the design of a stable feedback controller is necessary, so the LQR is used for the feedback controller because the LQR has good performance on controlling nonlinear systems. And the neural networks are used for a feed forward controller. The designed controller is applied to the stabilization of a wheeled inverted pendulum. Because of its nonlinear characteristics such as friction and parameter variations in the linearization, the wheeled inverted pendulum is used for demonstration of the effectiveness of the proposed controller.

• Journal of Ocean Engineering and Technology
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• v.23 no.2
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• pp.1-7
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• 2009

This paper presents a sliding mode controller based on Ackermann's formula and applies it to stabilizing a two-wheeled mobile inverted pendulum in equilibrium. The mobile inverted pendulum is a system with an inverted pendulum on a mobile cart. The dynamic modeling of the mobile inverted pendulum was established under the assumptions of a cart with no slip and a pendulum with only planar motion. The proposed sliding mode controller was based upon a class of nonlinear systems whose nonlinear part of the modeling can be linearly parameterized. The sliding surface was obtained in an explicit form using Ackermann's formula, and then a control law was designed from reachability conditions and made the sliding surface attractive to the equilibrium state of the mobile inverted pendulum. The proposed controller was implemented in a Microchip PIC16F877 micro-controller. The developed overall control system is described. The simulation and experimental results are presented to show the effectiveness of the modeling and controller.