Hyung-Soo Hwang

A tuning algorithm for the PID controller utilizing fuzzy theory

In this paper, we proposed a new PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kp, /spl tau//sub I/, /spl tau//sub D/, by the Ziegler-Nichols formula (1942) that uses the ultimate gain and ultimate period from a relay tuning experiment. We will get the error and the error rate of plant output corresponding to the initial value of parameter and find the new proportion gain (Kp) and the integral time (/spl tau//sub I/) from fuzzy tuner by the error and error rate of plant output as a membership function of fuzzy theory. This fuzzy auto tuning algorithm for PID controller considerably reduced the overshoot and rise time as compared to any other PID controller tuning algorithms. And in real parametric uncertainty systems, it constitutes an appreciable improvement of performance. The significant properties of this algorithm is shown by simulation.

Design of neuro-fuzzy controller on DSP for real-time control of induction motors

This paper deals with the DSP implementation of the high performance induction motor drive that presented on the viewpoint of the design and experiment. The speed control system for the induction motor drive is based on the ANFIS (adaptive network-based fuzzy inference system) controller, that is, a sophisticated neuro-fuzzy controller. This ANFIS controller acts as a feed forward controller that provides the plant with the proper control input and accomplish error backpropagation algorithm through the network. In this paper, the DSP (TMS320F240) has been used to perform the high-speed calculation of the space vector PWM and to build the ANFIS control algorithm. It is confirmed that proposed algorithm provides the more improved control performance for the conventional V/F controller and vector controller. The proposed ANFIS algorithm and DSP technique can be applied to the precise speed control of the induction motor drive system or the field of power electronics.

Optimization of Fuzzy Model Driven to IG and HFC-Based GAs

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) and information data granulation. HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. The granulation is realized with the aid of the Hard C-means clustering (HCM). The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly organized based on center points of data group extracted by the HCM clustering. It concerns the fuzzy model-related parameters such as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the polynomial type of the consequence part of fuzzy rules. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

Design of Hybrid Smith-Predictor Fuzzy Controller Using Reduction Model

In this paper, we propose an improved reduction model and a reduction model-based hybrid smith-predictor fuzzy controller. The transient and steady-state responsed of the reduction model was evaluated. In tuning the controller, the parameters of PID and the factors fuzzy controllers were obtained from the reduced model and by using genetic algorithms, respectively. Simulation examples demonstrated a better performance of the proposed controller than conventional ones.

Modeling of The Fuzzy Discrete Event System and It s Application

This paper deals with modeling method and application of Fuzzy Discrete Event System(FDES). FDES have characteristics which Crisp Discrete Event System(CDES) can`t deals with and is constituted with the events that is determined by vague and uncertain judgement like biomedical or traffic control. In general, the modeling method of CDES has been studied many times, but that of FDES hasn`t been nearly studied by qualitative character and scarcity of applicated system. This paper models traffic system with FDES`s character in FTTPN and designs a traffic signal controller.

Model Identification and Design of Optimized IMC-Cascade Controller

In this paper, we proposed to model identification in frequency domain using relay feedback and Design of internal model controller(IMC) with Cascade controller. The parameters of controller in the inner loop are determined to minimize the integral of time multiplied by the absolute value of error (ITAE) value of performance Index. The controller of outer loop and parameters of IMC-PID controller can be obtain using identified model. The model identification is considered that it is the transient response and the steady-state response through the use of nyquist curve. Simulation examples are given to show the better performance of the proposed method than conventional methods.

Design of Optimized Adaptive PID Control Structures using Model Reduction and RLSE

We propose an optimized adaptive PID control scheme. This paper is focused on the development of model reduction as well as a new adoptive control structure (viz. a recursive least square estimation (RLSE) method-based structure) that is constructed with smith-predictor structure and a real time estimator. The estimator adjust parameters of a reduced model in real time. It leads to robust and superb control performance for the noise or variation of parameters of process. Experimental study reveals that the proposed control structure exhibits more superb output performance in comparison to some previous methods.

Deadlock Analysis and Control of FMS`s Using Siphon property

Concurrent competition for finite resources by multiple parts in flexible manufacturing systems(FMS`s) and inappropriate initial marking or net structure of Petri net with share resources results in deadlock. This is an important issue to be addressed in the operation of the systems. Deadlock is a system state so that some working processes can never be finished. Deadlock situation is due to a wrong resource allocation policy. In fact, behind a deadlock problem there is a circular wait situation for a set of resources. Deadlock can disable an entire system and make automated operation impossible. Particularly, an unmanned system cannot recover from such a status and a set of jobs waits indefinitely for never-to-be-released resources. In this paper, we proposed a deadlock prevention method using siphon and trap of Petri net. It is based on potential deadlock which are siphon that eventually became empty. This method prevents the deadlock by the control of transition fire and initial marking in the Petri net. An given example of FMS is shown to illustrate our results with deadlock-free.

Identification of ANFIS-Based fuzzy systems with the aid of genetic optimization and information granulation

In this study, we introduce a new category of ANFIS-based fuzzy inference systems with the aid of information granulation to carry out the model identification of complex and nonlinear systems. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.