• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.387-400
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• 2015

A fuzzy logic based mobile robot navigation system was developed to improve the driving ability without trapping inside obstacles in complex terrains, which is one of the most concerns in robot navigation in unknown terrains. The navigation system utilizes the data from ultrasonic sensors to recognize the distances from obstacles and the position information from a GPS sensor. The fuzzy navigation system has two groups of behavior rules, and the robot chooses one of them based on the information from sensors while navigating for the targets. In plain terrains the robot with the proposed algorithm uses one rule group consisting of behavior rules for avoiding obstacle, target steering, and following edge of obstacle. Once trap is detected the robot uses the other rule group consisting of behavior rules strengthened for following edge of obstacle. The output signals from navigation system control the speed of two wheels of the robot through the fuzzy logic data process. The test was conducted in the Matlab based mobile robot simulator developed in this study, and the results show that escaping ability from obstacle is improved.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.162.1-162
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• 2001

In this paper, the adaptive fuzzy system is used as an adaptive approximator for robot nonlinear dynamic. A theoretical justification for the adaptive approximator is proving that if the representive point(RP or switching function) and its derivative in sliding mode control are used as the inputs of the adaptive fuzzy system, the adaptive fuzzy system can approximate robot nonlinear dynamics in the neighborhood of the switching surface. Thus the fuzzy controller design is greatly simplified and at the same time, the fuzzy control rule can be obtained easily by the reaching condition. Based on this, a new method for designing an adaptive fuzzy control system based on sliding mode is proposed for the trajectory tracking control of a robot with unknown nonlinear dynamics.

• Smart Structures and Systems
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• v.19 no.5
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• pp.577-585
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• 2017

In this paper, an adaptive fuzzy sliding mode controller (AFSMC) is designed to reduce dynamic responses of seismically excited structures. In the conventional sliding mode control (SMC), direct implementation of switching-type control law leads to chattering phenomenon which may excite unmodeled high frequency dynamics and may cause vibration in control force. Attenuation of chattering and its harmful effects are done by using fuzzy controller to approximate discontinuous part of the sliding mode control law. In order to prevent time-consuming obtaining of membership functions and reduce complexity of the fuzzy rule bases, adaptive law based on Lyapunov function is designed. To demonstrate the performance of AFSMC method and to compare with that of SMC and fuzzy control, a linear three-story scaled building is investigated for numerical simulation based on the proposed method. The results indicate satisfactory performance of the proposed method superior to those of SMC and fuzzy control.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.100-107
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• 2002

The advanced bilateral control algorithm, which can enlarge a reflected force by combining force reflection and compliance control, greatly enhances workability in teleoperation. In this scheme the maximum boundaries of a compliance controller and a force reflection gain guaranteeing stability and good task performance greatly depend upon characteristics of a slave arm, a master arm, and an environment. These characteristics, however, are generally unknown in teleoperation. It is, therefore, very difficult to determine such maximum boundary of the gain. The paper presented a novel method for design of an advanced bilateral controller. The factors affecting task performance and stability in the advanced bilateral controller were analyzed and a design guideline was presented. The neurofuzzy compliance model (NFCM)-based bilateral control proposed herein is an algorithm designed to automatically determine the suitable compliance for a given task or environment. The NFCM, composed of a fuzzy logic controller (FLC) and a rule-learning mechanism, is used as a compliance controller. The FLC generates compliant motions according to contact forces. The rule-learning mechanism, which is based upon the reinforcement learning algorithm, trains the rule-base of the FLC until the given task is done successfully. Since the scheme allows the use of large force reflection gain, it can assure good task performance. Moreover, the scheme does not require any priori knowledge on a slave arm dynamics, a slave arm controller and an environment, and thus, it can be easily applied to the control of any telerobot systems. Through a series of experiments effectiveness of the proposed algorithm has been verified.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.9
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• pp.1718-1726
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• 2006

In this paper, we proposed a fuzzy controller and algorithm for efficiently obstacle avoidance in unknown space. The ultrasonic sensor is used for position and distance recognition of obstacle, and fuzzy controller is used for left and right wheels angular velocity control. The fuzzification is used singleton method and the control rule is each wheel forty-nine. The fuzzy inference is used simplified Mamdani's reasoning and defuzzification is used SCOG(Simplified Center Of Gravity). The computer simulation based on mobile robot modelling was performed for the capacity of fuzzy controller and the really applicable possibility revaluation of the proposed avoidance algorithm and fuzzy controller. As a result, mobile robot was exactly reached in target and it avoided obstacle efficiently.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.434-443
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• 2008

This paper develops a novel automatic parking algorithm based on a fuzzy logic controller with the vehicle pose for the input and the steering rate for the output. It localizes the vehicle by using only external sensors - a vision sensor and ultrasonic sensors. Then it automatically learns an optimal fuzzy if-then rule set from the training data, using an evolutionary fuzzy system. Furthermore, it also finds the green zone for the ready-to-reverse position in which parking is possible just by reversing. It has been tested on a 4-wheeled Pioneer mobile robot which emulates the real vehicle.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.66-78
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• 2009

This paper introduces the design of a fuzzy logic controller in conjunction with direct torque control strategy for a Permanent Magnet synchronous machine. A stator flux angle mapping technique is proposed to reduce significantly the size of the rule base to a great extent so that the fuzzy reasoning speed increases. Also, a fuzzy resistance estimator is developed to estimate the change in the stator resistance. The change in the steady state value of stator current for a constant torque and flux reference is used to change the value of stator resistance used by the controller to match the machine resistance.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.3
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• pp.29-39
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• 1992

We investigate a systematic design procedure of automatic rule generation of fuzzy logic based controllers for highly nonlinear dynamic systems such as an engine dynamic modle. By "automatic rule generation" we mean autonomous clustering or collection of such meaningful transitional relations from one conditional subspace to another. During the design procedure, we also consider optimaly control strategies such as minimum squared error, near minimum time, minimum energy or combined performance critiera. Fuzzy feedback control systems designed by our method have the properties of closed-loop stability, robustness under parameter variabitions, and a certain degree of optimality. Most of all, the main advantage of the proposed approach is that reliability can be potentially increased even if a large grain of uncertainty is involved within the control system under consideration. A numerical example is shown in which we apply our strategic fuzzy controller dwsign to a highly nonlinear model of engine idling speed control.d control.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.7-13
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• 2002

Almost applications using fuzzy theory are based on the fuzzy inference. However fuzzy inference needs much time in calculation process for the fuzzy system with many input variables or many fuzzy labels defined on each variable. Inference time is dependent on the number of arithmetic Product in computation Process. Especially, the inference time is a primary constraint to fuzzy control applications using microprocessor or PC-based controller. In this paper, a simple fast fuzzy inference algorithm(FFIA), without loss of information, was proposed to reduce the inference time based on the fuzzy system with triangular membership functions in antecedent part of fuzzy rule. The proposed algorithm was induced by using partition of input state space and simple geometrical analysis. By using this scheme, we can take the same effect of the fuzzy rule reduction.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.39.2-39
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• 2001

This paper describes the design of an output-feedback controller based on an adaptive fuzzy observer for uncertain single-input single-output nonlinear dynamical systems. Especially, we have focused on the realization of minimal dynamic order of the adaptive fuzzy observer. For the purpose, we propose a new method in which no strictly positive real(SPR) condition is needed and combine dynamic rule activation scheme with on-line estimation of fuzzy parameters. By using proposed scheme, we can reduce computation time, storage space, and dynamic order of the adaptive fuzzy observer ...