• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.11
• /
• pp.1013-1021
• /
• 2000

This paper suggests fuzzy-PI controllers for a heavy-duty gas turbine. The fuzzy-PI controllers are designed to regulate rotor speed and exhaust temperature of the gas turbine. The controller gains are tuned by genetic algorithm(GA). This paper also proposes a new fitness function of GA using a desired output response. The suggested controller is compared with previous controllers via simulations and it is shown that the rotor speed variation of our controller is smaller than those of previous ones.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.878-883
• /
• 1991

In this paper, we propose the fuzzy-neural controller which is fuzzy controller with learning ability of neural network. The neural network in this controller is same as the membership function in current fuzzy controller and a parts of inference rules. And, it can be easily extend the control algorithm to multivariable systems. We can show effectiveness of the control algorithm through experiment of the inverted pendulum system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1995.10b
• /
• pp.139-144
• /
• 1995

LIBL(Linguistic Instruction Based Leaning) is an effective learning algorithm for fuzzy controller which interpretes and uses natural language of human The possibiliy of the LIBL algorithm to the fuzzy control of dynamic systems is investigated in this paper. Rise time, percent overshoot, and steady stste are proposed as suitable meaning elements for dynamic systems. A supervisor is able to give "higer-level linguistic instruction" to the learning algorithm through these three meaning elements Simulation results for a DC servo motor show the validity of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1804-1807
• /
• 1997

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learnign architecture. It is proposed a learning controller consisting of two neural networks-fuzzy based on independent reasoning and a connecton net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.8
• /
• pp.735-743
• /
• 2010

A new Fuzzy sliding mode controller is proposed to improve the cornering performance of the four wheel hybrid vehicles. The Fuzzy sliding mode control is applied for the control of rear motor and EHB (Electro-Hydraulic Brake) to improve the cornering performance. The modeling of the automobile is simplified that each of the two wheels is modeled as two degrees of freedom object and the friction coefficient between the wheel and the ground is assumed to be constant. The output of the Fuzzy sliding mode algorithm is the direct yaw moment for the rear wheels, which compensates for the slip angle. Through the simulations using ADAMS and MATLAB Simulink, the cornering performance of the proposed algorithm is compared to the conventional PID to show the superiority of the proposed algorithm. In the simulation experiments, the J-Turn and single lane change are used for each of the Fuzzy sliding mode algorithm and PID controller with the optimal gains which are tuned empirically.

• Journal of the Korean Society of Safety
• /
• v.26 no.4
• /
• pp.69-79
• /
• 2011

This paper proposes a GA-based optimal fuzzy control technique for the vibration control of earthquakeexcited adjacent structures interconnected with semi-active magneto-rheological(MR) dampers. Rule-based fuzzy logic controllers are designed first by implementing heuristic knowledge and the genetic algorithm(GA) is then introduced to optimally tune the fuzzy controllers for enhancing the seismic performance of semi-active control system. For practical implementation, the fuzzy controller simply uses locally measured responses of the dampers involved and directly returns the input voltage to the magneto-rheological dampers in real time through the fuzzy inference mechanism. The local measurement based fuzzy controller provides optimal damping force in a decentralized manner so that it does not require a primary central controller unlike the conventional semi-active control techniques. As a result, it can avoid the unbridgeable discrepancy between the desired control force and the actual damper force that may occur in the conventional control approaches. The validity and effectiveness of the proposed control method are shown numerically on two 20-story earthquake-excited buildings interconnected with MR dampers.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.5 no.4
• /
• pp.75-81
• /
• 2001

Fuzzy supervisory control method is studied for the active control of earthquake excited structures. The proposed algorithm supervises and tunes previously designed control gains by evaluating the state of a structure through the fuzzy inference mechanism, which uses the information of relative displacements and velocities. Example designs and numerical simulations of earthquake exited three degrees of freedom structures are performed to prove the validity of the proposed control algorithm. Comparative results with conventional LQR method show that the proposed method is effective for the vibration suppression of earthquake excited structures.

• Smart Structures and Systems
• /
• v.12 no.6
• /
• pp.595-617
• /
• 2013

Semi-active control equipments are used to effectually enhance the seismic behavior of structures. Magneto-rheological (MR) dampers are semi-active devices that can be utilized to control the response of structures during seismic loads and have received voracious attention for response suppression. They supply the adaptability of active devices and stability and reliability of passive devices. This paper presents an optimal fuzzy logic control scheme for vibration mitigation of buildings using magneto-rheological dampers subjected to near-fault ground motions. Near-fault features including a directivity pulse in the fault-normal direction and a fling step in the fault-parallel direction are considered in the requisite ground motion records. The membership functions and fuzzy rules of fuzzy controller were optimized by genetic algorithm (GA). Numerical study is performed to analyze the influences of near-fault ground motions on a building that is equipped with MR dampers. Considering the uncontrolled system response as the base line, the proposed method is scrutinized by analogy with that of a conventional maximum dissipation energy (MED) controller to accentuate the effectiveness of the fuzzy logic algorithm. Results reveal that the fuzzy logic controllers can efficiently improve the structural responses and MR dampers are quite promising for reducing seismic responses during near-fault earthquakes.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1430-1434
• /
• 2003

In this paper, for the fuzzy-reaching law method which has the characteristic of elimination of chattering at sliding mode as well as the characteristic of fast response at the design of variable structure controller with reaching law, optimal solutions for the determination of parameters of fuzzy membership functions by using genetic algorithm are proposed. Generally, the design of fuzzy controller has difficulties in determining the parameters of fuzzy membership functions by using a tedious trial-and-error process. To overcome these difficulties, this paper develops genetic algorithm of an optimal searching method based on genetic operation, and to verify the validity of this proposed method it is simulated through 2 link robot manipulator.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.3
• /
• pp.289-300
• /
• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.