• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.244-249
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• 1998

In this paper, the hybrid state space self-tuning control technique Is studied within the framework of fuzzy systems and dual-rate sampling control theory. We show that fuzzy modeling techniques can be used to formulate chaotic dynamical systems. Then, we develop the hybrid state space self-tuning fuzzy control techniques with dual-rate sampling for digital control of chaotic systems. An equivalent fast-rate discrete-time state-space model of the continuous-time system is constructed by using fuzzy inference systems. To obtain the continuous-time optimal state feedback gains, the constructed discrete-time fuzzy system is converted into a continuous-time system. The developed optimal continuous-time control law is then convened into an equivalent slow-rate digital control law using the proposed digital redesign method. The proposed technique enables us to systematically and effective]y carry out framework for modeling and control of chaotic systems. The proposed method has been successfully applied for controlling the chaotic trajectories of Chua's circuit.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.337-345
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• 2009

The dependence of numerous systems on electronic devices is causing rapidly increasing concern over fault tolerance because of safety issues of safety critical system. As an example, a vehicle with electronics-controlled system such as x-by-wire systems, which are replacing rigid mechanical components with dynamically configurable electronic elements, should be fault￢tolerant because a devastating failure could arise without warning. Fault-tolerant systems have been studied in detail, mainly in the field of aeronautics. As an alternative to solve these problems, this paper presents the fuzzy hybrid redundancy system that can remove most erroneous faults with fuzzy fault detection algorithm. In addition, several numerical simulation results are given where the fuzzy hybrid redundancy outperforms with general voting method.

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

• Journal of Power Electronics
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• v.4 no.3
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• pp.127-137
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• 2004

This paper introduces a hybrid induction motor control using a genetically optimized pseudo-on-line method. Optimization results from the use of a look-up table based on genetic algorithms to find the global optimum of an unconstrained optimization problem. The approach to induction motor control includes a pseudo-on-line procedure that optimally estimates parameters of a fuzzy PID (FPID) controller. The proposed hybrid genetic fuzzy PID (GFPID) controller is applied to speed control of a 3-phase induction motor and its computer simulation is carried out. Simulation results show that the proposed controller performs better than conventional FPID and PID controllers. The contribution of this paper is the introduction of a high performance hybrid form of induction motor control that makes on-line and real-time control of the drive system possible.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.599-609
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• 2000

The new design methodology of a hybrid fuzzy controller by means of the genetic algorithms is presented. First, a hybrid fuzzy controller(HFC) related to the optimal estimation of control parameters is proposed. The control input for the system in the HFC combined PID controller with fuzzy controller is a convex combination of the FLC's output and PID's output by a fuzzy variable, namely, membership function of weighting coefficient. Second, an auto-tuning algorithms utilizing the simplified reasoning method and genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The proposed HFC is evaluated and discussed to show applicability and superiority with the and of three representative processes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.211-216
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• 2004

In this thesis, we present the Takagi-Sugeno-Kang (TSK) fuzzy model based adaptive controller and adaptive identification for a general class of uncertain nonlinear dynamic systems. We use an estimated model for the unknown plant model and use this model for designing the controller. The hybrid adaptive control combined direct and indirect adaptive control based on TSK fuzzy model is constructed. The direct adaptive law can be showed by ignoring the identification errors and fails to achieve parameter convergence. Thus, we propose an TSK fuzzy model based hybrid adaptive (HA) law combined of the tracking error and the model ins error to adjust the parameters. Using a Lyapunov synthesis approach, the proposed hybrid adaptive control is proved. The hybrid adaptive law (HA) is better than the direct adaptive (DA) method without identifying the model ins error in terms of faster and improved tracking and parameter convergence. In order to show the applicability of the proposed method, it is applied to the inverted pendulum system and the performance is verified by some simulation results.

• International Journal of Automotive Technology
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• v.8 no.5
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• pp.651-658
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• 2007

This paper presents a new type of fuzzy logic-based power control strategy for fuel cell hybrid electric vehicles designed to improve their fuel economy while maintaining the battery's state of charge. Since fuel cell systems have inherent limitations, such as a slow response time and low fuel efficiency, especially in the low power region, a battery system is typically used to assist them. To maximize the advantages of this hybrid type of configuration, a power distribution control strategy is required for the two power sources: the fuel cell system and the battery system. The required fuel cell power is procured using fuzzy rules based on the vehicle driving status and the battery status. In order to show the validity and effectiveness of the proposed power control strategy, simulations are performed using a mid-size vehicle for three types of standard drive cycle. First, the fuzzy logic-based power control strategy is shown to improves the fuel economy compared with the static power control strategy. Second, the robustness of the proposed power control strategy is verified against several variations in system parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.193-196
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• 1996

A pneumatic cylinder has been used in the production facilities of various industries. However, it is difficult to achieve deciding the precise position of the piston rod, due to the nonlinear properties arising from the air compression and the friction. In recent years, the fuzzy control algorithm has been frequently applied to various kinds of systems on account of its simple algorithm, good adaptability to complex or nonlinear systems and so on. On the other hand, the PID or I-PD control has been used in many engineering fields because of the excellent performance. However, it is known that each one of them has disadvantages. In this paper, we propose a hybrid control which is strived to obtain the advantages of each other. It is shown that the proposed hybrid control performs better than the conventional I-PD control through the experimental results.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.36-51
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• 1995

An approach to robot hybrid position/force control, which allows force manipulations to be realized without overshoot and overdamping while in the presence of unknown environment, is given in this paper. The manin idea is to used dynamic compensation for known robot parts and fuzzy compensation for unknown environment so as to improve system performance. The fuzzy compensation is implemented by using rule based fuzzy approach to identify the unknown environment. The establishment of proposed control system consists of following two stages. First, similar to the resovled acceleration control method, dynamic compensation and PD control based on known robot dynamics, kinematics and estimated environment stiffness is introduced. To avoid overshoot the whole control system is constructed with overdamping. In the second stage, the unknown environment stiffness is identified by using fuzzy reasoning, where the fuzzy compensation rules are obtained priori as the expression of the relationship betweenenvironment stiffness and system. Based on the simulation result, comparison between cases with or without fuzzy identifications are given, which illustrate the improvement achieced.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.700-702
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• 2000

The new design methodology of a hybrid fuzzy controller by means of the genetic algorithms is presented. In fuzzy controller which has been widely applied and used. in order to construct the best fuzzy rules that include adjustment of fuzzy sets, a highly skilled techniques using trial and error are required. To deal with such a problem, first, a hybrid fuzzy controller(HFC) related to the optimal estimation of control parameters is proposed. The HFC combined a PID controller with a fuzzy controller concurrently produces the better output performance than any other controller from each control output in steady state and transient state. Second, a auto-tuning algorithms is presented to automatically improve the performance of hybrid fuzzy controller, utilizing the simplified reasoning method and genetic algorithms. In addition, to obtain scaling factors and PID Parameters of HFC using GA, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The HFCs are applied to the first-order second-order process with time-delay and DC motor Computer simulations are conducted at step input and the performances of systems are evaluated and also discussed from performance indices.