• Proceedings of the KIEE Conference
• /
• 1988.07a
• /
• pp.930-933
• /
• 1988

This paper demonstrates an AI application for representing operator's heuristic knowledges in implementing process control. AI controller constitutes servo and regulatory controller, respectively. The knowledge base for servo controller was designed to obtain the process output with respect to the desired set-point in short period. The regulatory controller was to provide smooth output near the set-point. The AI controllers was implemented in Turbo-PROLOG on IBM-PC. For the parallel processing, Turbo-C was linked to calculate the process output. The result shows that this AI controller can be well suited not only for minimum phase but also for non-minimum phase. This controller may be used as a back-up controller for rather extensive expert system.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.617-619
• /
• 1998

A PID-controller is proposed as a controller to the IFAC93 benchmark process. It is compared with a Textbook PID-controller and a Derivative of output PID-controller. Especially, the Derivative of output PID controller works within the critical bounds of ${\pm}1.5$ except for 1 out of 15 periods at stress level 1,2. The objective of this paper, then, is to report on an alternative benchmark (IFAC93) and reveal more efficient PID controller between Textbook PID-control and Derivative of output PID-controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.05a
• /
• pp.144-147
• /
• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• Journal of IKEEE
• /
• v.18 no.2
• /
• pp.206-213
• /
• 2014

In this note, a new robust nonlinear output feedback variable structure controller is first systematically and generally designed for the output control of more affine uncertain nonlinear systems with mismatched uncertainties and matched disturbance. A transformed integral output feedback sliding surface with a most simple form is applied in order to remove the reaching phase problems. The closed loop exponential stability and the existence condition of the sliding mode on the integral output feedback sliding surface is investigated with a corresponding output feedback control input in Theorem 1. For practical application the continuous implementation of the control input is made by the modified saturation function. The effectiveness of the proposed controller is verified through a design example and simulation study.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.14 no.3
• /
• pp.208-213
• /
• 2004

This paper is concerned with the theoretical estimation of the single-input single-output(SISO) positive position feedback(PPF) controller and the derivation of the stability conditions for the multi-input multi-output (MIMO) PPF controller. Although the stability condition for the SISO PPF controller was derived in the earlier works, the question regarding the performance estimation of the SISO PPF controller has never been studied theoretically. Hence, the SISO PPF controller for the single degree-of-freedom system was first investigated and then control parameters including gain, the filter frequency, and the damping factor of the PPF controller were analyzed in detail thus providing the design methodology for the SISO PPF controller. In the case of real structure. there are infinite number of natural modes so that some modes are to be controlled by a limited number of actuator and sensor. Based on the theoretical results on the SISO PPF controller, the stability condition for the multi-input multi-output PPF controller was derived when only the few number of modes are to be controlled. The control spillover problem is also discussed in detail.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.54 no.1
• /
• pp.8-14
• /
• 2005

This paper is proposed hybrid artificial intelligent(HAI) controller for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on HAI controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.05a
• /
• pp.228-231
• /
• 2000

A hybrid fuzzy controller by means of the genetic algorithms is presented. The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PlD's output in steady state by a fuzzy variable. The HFC combined a PID controller with a fuzzy controller concurrently produces the better output performance than any other controller. A auto-tuning algorithms is presented to automatically improve the performance of hybrid fuzzy controller using genetic algorithms. The algorithms estimates automatical Iy the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA three kinds of estimation modes are effectively utilized. The HFCs are applied to the second process with time-delay. Computer simulations are conducted at step input and the performances of systems are evaluated and also discussed in ITAE(Integral of the Time multiplied by the Absolute value of Error ) and other ways.

• Proceedings of the KIEE Conference
• /
• 2000.07b
• /
• pp.1408-1410
• /
• 2000

The regulation performances needs high control gain in novel output feedback controller but high control gain is decreased relative stability of the total system. Thus, this paper proposed neural network controller(NNC) for output feedback controller. In this scheme, output feedback controller are guarantee global stability and NNC are controller steady-state error and defined optimal control law. And we demonstrated this scheme by simulations.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.5
• /
• pp.463-471
• /
• 1999

This paper presents a vector controlled induction motor is implemented by neural networks system compared with PI controller for the speed control. The design employed the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE) for speed. In order to update the weights of the controller First of all Emulator updates its parameters by identifying the motor input and output next it supplies the error path to the output stage of the controller using backpropagation algorithm, As Controller produces an adequate output to the system due to neural networks learning capability Vector controlled induction motor characteristics actual motor speed with based on neural network system follows the reference speed better than that of linear PI speed controller.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.22 no.5
• /
• pp.440-448
• /
• 2017

This paper proposes a controller design of a distributed source inverter in stand-alone mode or grid-connected mode to compensate the current or voltage harmonics caused by local nonlinear load. The PR-based multi loop controller has been used to improve the dynamic performance of the system and to compensate the output voltage or grid current harmonics. The multi-loop controller consists of an outer current controller and an inner voltage controller for the output voltage control in stand-alone mode. In grid-connected mode, an outer current controller is added to the output voltage controller for the grid current control. The design performance of each controller is described through the Root locus and Bode plot of the transfer functions. The validity of the proposed control algorithm and design parameters has been verified through the PSiM simulation and experimental results.