• Proceedings of the KIEE Conference
• /
• 1993.07a
• /
• pp.227-230
• /
• 1993

Recently, the development of novel control methodology enables us to improve the performance of AC-machine drives by using pulse width modulation (PWM) technique. Usually, the dynamic characteristic of induction motor (IM) has been represented by the 5-th order nonlinear differential equation. This dynamics, however, can be reduced to 3-rd order dynamics by applying direct control of IM input current. This methodology concludes that it is much easier to control IM by means of the field-oriented methods employing the current controller. Therefore a precise current control is crucial to achieve a high control performance both in dynamic and steady state operations. This paper presents an adaptive fuzzy current controller with artificial neural network (ANN) for field-oriented controlled IM. This new control structure is able to adaptively minimize a current ripple while maintaining constant switching frequency. Especially the proposed controller employs neuro-computing philosophy as well as adaptive learning pattern recognizing principles with respect to variations of the system parameters. The proposed approach is applied to the IM drive system, and its performance is tested through various simulations. Simulation results show that the proposed system, compared among several known classical methods, has a superb performance.

• International Journal of Precision Engineering and Manufacturing
• /
• v.3 no.1
• /
• pp.66-75
• /
• 2002

The crane operation used fur transporting heavy loads causes a swinging motion with the loads due to the crane\`s acceleration and deceleration. This sway causes the suspension ropes to leave their grooves and can cause serious damage. Ideally, the purpose of a crane system is to transport loads to a goal position as soon as possible without any oscillation of the rope. Currently, cranes are generally operated based on expert knowledge alone, accordingly, the development of a satisfactory control method that can efficiently suppress object sway during transport is essential. The dynamic behavior of a crane shows nonlinear characteristics. When the length of the rope is changed, a crane becomes a time-varying system thus the design of an anti-sway controller is very difficult. In this paper, a nonlinear dynamic model is derived for an industrial overhead crane whose girder, trolley, and hoister move simultaneously. Furthermore, a fuzzy logic controller, based on expert experiments during acceleration, constant velocity, deceleration, and stop position periods is proposed to suppress the swing motion and control the position of the crane. Computer simulation is then used to test the performance of the fuzzy controller with the nonlinear crane model.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.8
• /
• pp.193-202
• /
• 1999

Crane operation for transporting heavy loads causes swinging motion at the loads due to crane's acceleration and deceleration. This sway causes the suspension ropes to leave their grooves and leads to possibility of serious damages. So, this swing of the objects is a serious problem and the goal of crane system is transporting to a goal position as soon as possible without the oscillation of the rope. Generally crane is operated by expert's knowledge. Therefore, a satisfactory control method to supress object sway during transport is indispensible. The dynamic behavior of the crane shows nonlinear characteristics. when the length of the rope is changed the crane is time varying system and the design of anti-sway controller is very difficult. In this paper, the nonlinear dynamic model for the industrial overhead crane whose girder, trolley and hoister move simultaneously is derived. and the Fuzzy logic controller based on the expert experiments during acceleration, constant velocity, deceleration and stop position period is proposed to supress the swing motion and control the position of the crane. The performance of the fuzzy controller for the nonlinear crane model is simulated on the personal computer.

• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.6
• /
• pp.55-61
• /
• 2003

Control of multi-link robot arms is a challenging and difficult problem because of the highly nonlinear dynamics. Independent joint adaptive scheme is developed for control of robot manipulators based on Sugeno-type of fuzzy logic. Fuzzy logic system is used to approximate the coupling forces among the joints, coriolis force, centrifugal force, gravitational force, and frictional forces. The proposed scheme does not require an accurate manipulator dynamic, and it is proved that closed-loop system is asymptotic stable despite the gross robot parameter variations. Numerical simulations for three-axis PUMA robot are included to show the effectiveness of controller.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.4 no.3
• /
• pp.23-31
• /
• 1994

In this paper, the yo-yo control system is introduced as a new benchmark system for evaluation of intellignet controllers. In order to control an yo-yo, and asymmetric nonlinear controller is needed due to the unique nonlinear asymmetric dynamic characteristics of the system. As such, it is difficult to control an yo-yo either by a linear controller or by a bang-bang controller. In the paper, we have inplemented a yyo control system with a general=purpose fuzzy controller. In the fuzzy control, 14 if-then rules are used, being extracted from human experties and, for real-time control, a fuzzy inference hardware(called FLEXi) is used.

• Structural Engineering and Mechanics
• /
• v.69 no.3
• /
• pp.307-315
• /
• 2019

This paper proposes a chattering-free sliding mode control (CFSMC) method for seismically excited structures. The method is based on a fuzzy logic (FL) model applied to smooth the control force and eliminate chattering, where the switching part of the control law is replaced by an FL output. The CFSMC is robust and keeps the advantages of the conventional sliding mode control (SMC), whilst removing the chattering and avoiding the time-consuming process of generating fuzzy rule basis. The proposed method is tested on an 8-story shear frame equipped with an active tendon system. Results indicate that the new method not only can effectively enhance the seismic performance of the structural system compared to the SMC, but also ensure system stability and high accuracy with less computational cost. The CFSMC also requires less amount of energy from the active tendon system to produce the desired structural dynamic response.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.238-242
• /
• 1991

This paper presents an approach to self-tuning PI control of dynamic plants, based on fuzzy logic application. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a fuzzy logic controller, one of the most difficult problem is the selection of linguistic control rules and parameters. To overcome this difficulty, self-tuning fuzzy PI controller (STFPIC) with a hierarchical structure in which the fuzzy PI controller is assigned as the lower level and the rule modification and parameter adjustment as the higher level. The rules and parameters are generated by the adjustment of membership function through performance index(PE). In this paper, the algorithm for of the controller performance is estimated by means of computer simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.1
• /
• pp.77-84
• /
• 2007

In this paper, Takagi-Sugeno (TS) fuzzy integral control is investigated to regulate the output voltage of an asymmetric half-bridge (AHB) DC/DC converter; First, we model the dynamic characteristics of the AHB DC/DC converter with state-space averaging method and small perturbation at an operating point. After introducing an additional integral state of the output regulation error, we obtain the $5^{th}$-order TS fuzzy model of the AHB DC/DC converter. Second, the concept of the parallel distributed compensation is applied to design the fuzzy integral controller, in which the state feedback gains are obtained by solving the linear matrix inequalities (LMIs). Finally, simulation results are presented to show the performance of the considered design method as the output voltage regulator and compared to the results for which the conventional loop gain method is used.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.1
• /
• pp.113-120
• /
• 1994

The dynamic characteristics of turning processes are complex, non-linear and time-varying. Consequently, the conventional techniques based on crisp mathematical model may not guarantee cutting force regulation. This paper presents a fuzzy controller which can regulate cutting force in turning process under varying cutting conditions. The fuzzy control rules are extablished from operator experience and expert knowledge about the process dynamics. Regulation which increases productivity and tool life is achieved by adjusting feedrate according to the variation of cutting conditions. The performance of the proposed controller is evaluated by cutting experiments in the converted conventional lathe. The results of experiments show that the proposed fuzzy controller has a good cutting force regulation capability in spite of the variation of cutting conditions.

• Journal of Advanced Marine Engineering and Technology
• /
• v.24 no.2
• /
• pp.72-81
• /
• 2000

In this paper, we design an adaptive neuro-fuzzy inference system(ANFIS) precompensator for load frequency control of 2-area power systems. While proportional integral derivative (PID) controllers are used in power systems, they may have some problems because of high nonlinearities of the power systems. So, a neuro-fuzzy-based precompensation scheme is incorporated with a convectional PID controller to obtain robustness to the nonlinearities. The proposed precompensation technique can be easily implemented by adding a precompensator to an existing PID controller. The applied neruo-fuzzy inference system precompensator uses a hybrid learning algorithm. This algorithm is to use both a gradient descent method to optimize the premise parameters and a least squares method to solve for the consequent parameters. Simulation results show that the proposed control technique is superior to a conventional Ziegler-Nichols PID controller in dynamic responses about load disturbances.