• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.6
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• pp.15-24
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• 2000

This paper presents an output feedback robust H$\infty$ control problem for a class of uncertain nonlinear systems, which can be represented by an fuzzy dynamic model. The nonlinear system is represented by Takagi-Sugeno fuzzy model, and the control design is carried out on the basis of the fuzzy model. Using a single quadratic Lyapunov function, the globally exponential stability and disturance attenuation of the closed-loop fuzzy control system are discussed. Sufficient conditions for the existence of robust H$\infty$ controllers are given in terms of linear matrix inequalities(LMIs). Constructive algorithm for design of robust H$\infty$ controller is also developed. The resulting controller is nonlinear and automatically tuned based on fuzzy operation.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.243-246
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• 1994

In this paper, a self-learning fuzzy controller is designed with a fuzzy approximation of an inverse model. The aim of an identification is to find an input command which is control of a system output. It is intuitional and easy to use a classical adaptive inverse modeling method for the identification, but it is difficult and complex to implement it. This problem can be solved with a fuzzy approximation of an inverse modeling. The fuzzy logic effectively represents the complex phenomena of the real world. Also fuzzy system could be represented by the neural network that is useful for a learning structure. The rule of a fuzzy inverse model is modified by the gradient descent method. The goal is to be obtained that makes the design of fuzzy controller less complex, and then this self-learning fuzz controller can be used for nonlinear dynamic system. We have applied this scheme to a nonlinear Ball and Beam system.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.2
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• pp.102-112
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• 2017

Many studies on dynamic positioning control algorithms using fixed feedback gains have been carried out to improve station keeping performance of dynamically positioned vessels. However, the control algorithms have disadvantages in that it can not cope with changes in environmental disturbances and response characteristics of vessels motion in real time. In this paper, the Fuzzy Gain Scheduling - PID(FGS - PID) control algorithm that can tune PID gains in real time was proposed. The FGS - PID controller that consists of fuzzy system and a PID controller uses weighted values of PID gains from fuzzy system and fixed PID gains from Ziegler - Nichols method to tune final PID gains in real time. Firstly, FGS - PID controller, control allocation algorithm, FPSO and environmental disturbances were modeled using Matlab/Simulink to evaluate station keeping performance of the proposed control algorithm. In addition, simulations that keep positions and a heading angle of vessel with wind, wave, current disturbances were carried out. From simulation results, the FGS - PID controller was confirmed to have better performances of keeping positions and a heading angle and consuming power than those of the PID controller. As a consequence, the proposed FGS - PID controller in this paper was validated to have more effectiveness to keep position and heading angle than that of PID controller.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.2
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• pp.346-349
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• 1994

In this paper, we implemented the variable spped controller of an induction motor using the self-tuning fuzzy control algorithms, which recently is invoking the remarkable interest. Also we preposed a self-tuning technique of scale factors which could easily design the fuzzy speed controller. Comparing with conventional PI speed controller, the performances of proposed fuzzy controller such as dynamic responses and its the robustness against load disturbance were substantially improved.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1359-1361
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• 1996

This paper describes a design of real time fuzzy controller using Minimum fuzzy control Rule Selection Method(MRSM). The control algorithm of dynamic systems needs less computation time and memory. To reduce the computation time of fuzzy logic controller, minimum number of rules are to be selected for the fuzzy input variable. The universe of discourse is divided by the number of linguistic labels to allocate the assigned membership function to the fuzzy input variables. In this case, since fuzzy input variables are continuous, scale factor SU is tuned independently. According to increment of SU control surface is improved to adapt the change of system parameter. At this, crisp control surface is increased. With the increament of crisp control surface, fuzzy control surface is reduced. When error state deviates from desirable error state, crisp control surface is more useful than fuzzy control surface for obtaining fast rising time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.5
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• pp.822-827
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• 2002

In general, jerk phenomenon appeared because of gear changing, when a vehicle starts off or climbs an incline. Therefore, it makes ride quality worse. In this paper, an optimal pattern of automatic transmission was designed using fuzzy logic in order to improve ride quality. After del eloping fuzzy rule for shift pattern control of automatic transmission, dynamic characteristics (i.e. acceleration, velocity, distance and so on) were simulated using dynamic model of a car. To do this he powertrain model of a vehicle with automatic transmission including torque converter, gear box, and final gear drive - from engine to tire - is developed.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.499-502
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• 1996

In this paper, we propose a fuzzy iterative learning controller(FILC). It can control fully unknown dynamic plants through iterative learning. To design learning controllers based on the steepest descent method, it is one of the difficult problems to identify the change of plant output with respect to the change of control input(.part.e/.part.u). To solve this problem, we propose a method as follows: first, calculate .part.e/.part.u using a similarity measure and information in consecutive time steps, then adjust the fuzzy logic controller(FLC) using the sign of .part.e/.part..u. As learning process is iterated, the value of .part.e/.part.u is reinforced. Proposed FILC has the simple architecture compared with previous other controllers. Computer simulations for an inverted pendulum system were conducted to verify the performance of the proposed FILC.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.2
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• pp.62-69
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• 2000

In this paper, we design a GA-fuzzy precompensated PID controller for the load frequency control of two-area interconnected power system. Here, a fuzzy precompensated PID controller is designed as a fuzzy logic-based precompensation approach for PID controller. This scheme is easily implemented simply by adding a fuzzy precompensator to an existing PID controller. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and control rules. Simulation results show that the proposed control technique is superior to a conventional PID control and a fuzzy precompensated PID control in dynamic responses about the load disturbances of power system and is convinced robustness reliableness in view of structure.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.103-106
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• 1996

This paper presents an iterative fuzzy learning control scheme which is applicable to a broad class of nonlinear systems. The control scheme achieves system stability and boundedness by using the linear feedback plus adaptive fuzzy controller and achieves precise tracking by using the iterative learning rules. The switching mode control unit is added to the adaptive fuzzy controller in order to compensate for the error that has been inevitably introduced from the fuzzy approximation of the nonlinear part. It also obviates any supervisory control action in the adaptive fuzzy controller which normally requires high gain signal. The learning control algorithm obviates any output derivative terms which are vulnerable to noise.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.76-82
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• 2009

A Heating, Ventilating and Air Conditioning (HVAC) system is a nonlinear multi-input multi-output (MIMO) system. This system is very difficult to control the temperature and the humidity ratio of a thermal space because of complex nonlinear characteristics. This paper proposes an adaptive fuzzy output feedback control based on observer for the nonlinear HVAC system. The nonlinear HVAC system is linearized through dynamic extension. State observers are designed for estimating state variables of the HVAC system. Fuzzy systems are employed to approximate uncertain nonlinear functions of the HVAC system with unavailable state variables. The obtained controller compares with an adaptive feedback controller. Simulation is given to demonstrate the effectiveness of our proposed adaptive fuzzy method.