• Nuclear Engineering and Technology
• /
• v.35 no.5
• /
• pp.399-411
• /
• 2003

A model predictive control method is applied to design an automatic controller for thermal power control in a reactor core. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, the second optimal control input is not implemented and the procedure to solve the optimization problem is then repeated. The objectives of the proposed model predictive controller are to minimize the difference between the output and the desired output and the variation of the control rod position. The nonlinear PWR plant model (a nonlinear point kinetics equation with six delayed neutron groups and the lumped thermal-hydraulic balance equations) is used to verify the proposed controller of reactor power. And a controller design model used for designing the model predictive controller is obtained by applying a parameter estimation algorithm at an initial stage. From results of numerical simulation to check the controllability of the proposed controller at the $5\%/min$ ramp increase or decrease of a desired load and its $10\%$ step increase or decrease which are design requirements, the performances of this controller are proved to be excellent.

• Journal of Biosystems Engineering
• /
• v.21 no.3
• /
• pp.283-292
• /
• 1996

Automatic guidance of farm tractors would improve productivity by reducing operator fatigue and increasing machine performance. To control tractors within $\pm$5cm of the desired path, fuzzy and adaptive steering controllers were developed to evaluate their characteristics and performance. Two input variables were position and yaw errors, and a steering command was fed to tractor model as controller output. Trapezoidal membership functions were used in the fuzzy controller, and a minimum-variance adaptive controller was implemented into the 2-DOF discrete-time input-output model. For unit-step and composite paths, a dynamic tractor simulator was used to test the controllers developed. The results showed that both controllers could control the tractor within $\pm$5cm error from the defined path and the position error of tractor by fuzzy controller was the bigger of the two. Through simulations, the output of self-tuning adaptive controller was relatively smooth, but the fuzzy controller was very sensitive by the change of gain and the shape of membership functions. Contrarily, modeling procedure of the fuzzy controller was simple, but the adaptive controller had very complex procedure of design and showed that control performance was affected greatly by the order of its model.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.3
• /
• pp.218-226
• /
• 2003

Due to the friction characteristics of pump, cylinder packing and passenger car, in the elevation system actuated with hydraulic inverter, there exist dead zones. which cannot be controlled by a PID controller. To overcome the drawbacks, in this paper, we propose a new hybrid fuzzy-sliding mode control scheme, which controls the controller output between a sliding mode control output and a PID control output by fuzzy control method. The proposed hybrid control scheme achieves an improved control performance by using both controllers. We first propose a design method of the hybrid controller far a hydraulic system controlled by inverters, then propose a design method of a hybrid fuzzy-sliding mode centre] scheme. The effectiveness of the proposed control scheme is shown by simulation results, in which the proposed hybrid control method yields better control performance then the PID controlled scheme, not only in the zero-crossing speed region but also in the overall control region including steady-state region.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.12
• /
• pp.977-983
• /
• 2003

An output feedback stabilizing controller far non-minimum phase nonlinear systems is presented. We first perform the standard input-output linearization of the system and then transform the zero dynamics into a special normal form in which the antistable part is not affected by the stable part and the antistable part is given in approximately linear form. Under the assumption that the nonlinear system satisfies the observability rank condition, we can design an observer f3r the extended system that is made of the augmentation of a chain of integrators. The proposed output feedback stabilizing controller can then be designed by combining the observer and the state feedback controller.

• Journal of IKEEE
• /
• v.17 no.1
• /
• pp.83-88
• /
• 2013

This paper presents the output feedback control design for feedforward nonlinear systems with input and output delay. The proposed output feedback controller is composed of a linear observer and a linear controller. It is shown that by using Lyapunov-Krasovskii theorem, the proposed controller ensures a global asymptotic stability for arbitrarily large delay. Finally, an illustrative example is given in order to show the effectiveness of our design method.

• Journal of the Korean Society of Industry Convergence
• /
• v.25 no.2_1
• /
• pp.161-168
• /
• 2022

This study proposed a new approach to impliment a robusut control of comsumer-friendly flexible manipulator with five joint for untact working in filed work-site. The output redefinition approach was used to overcome the non minimum phase characteristic of the system. The new output is defined so that the zero dynamics related to this output are stable. The control strategy is based on an computed torque method which is applicable to a class of time-invariamt phase linear systems whose uncertainties appear in output loop stable. The controller is composed of a stabilizing joint controller and an output redefinition tracking controller. Experimental results are also presented to verify the effectiveness of the proposed control scheme.

• Proceedings of the KIPE Conference
• /
• 1999.07a
• /
• pp.311-314
• /
• 1999

This paper presents the design method of a repetitive controller to educe the harmonics in the output voltage waveform of the AC power supply systems. Output voltage error under the nonlinear load like a rectifier is cyclic with the same period to fundamental wave, therefore one can design the repetitive controller calculating cycle by cycle. The controller is verified mathematically and by simulations.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.176.5-176
• /
• 2001

This paper presents a fuzzy dynamic output feedback controller design method for Parallel Distributed Compensation (PDC)-type Takagi-Sugeno (T-S) model based fuzzy dynamic system with H$\infty$ performance and additional constraints on the closed pole placement. Design condition for these controller is obtained in terms of the linear matrix inequalities (LMIs). The proposed fuzzy controller satisfies the disturbance rejection performance and the desired transient response. The design method is verified by this method for an inverted pendulum with a cart using the proposed method.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.4
• /
• pp.15-26
• /
• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.18 no.2
• /
• pp.63-67
• /
• 2004

We know that Neural Network is in use in many control fields. In time of using as controller, Neural Network controller is needed to learning by Input-output pattern. But in many times of control field. we can not get Input-output pattern of Neural Network controller. As a method solving this problem, in this paper, we try New control method that output node of Neural Network bringing control object. Such a New control method application, we can solve the data taking problem to Neural Network controller Input-output. The effectiveness of proposed control algorithm is verified by simulation results of DC servo motor.