Woojong Yoo

Development of automatic temperature control system in blast furnace

In this paper, we consider automatic temperature control system which is the most important part in blast furnace operation. In general, automatic temperature control for blast furnace is very difficult and sensitive because it is affected by a number of factors. So firstly, this paper describes temperature control model for blast furnace by using Takagi-Sugeno (TS) fuzzy model based on input-output data. Secondly, this paper describes automatic temperature control techniques based on model predictive control (MFC) techniques that are widely used in industrial process control practice. We propose the framework to build numerical model describing blast furnace based on TS-fuzzy inference system and to control automatic temperature control system using MFC

Efficient Fuzzy-MPC for Nonlinear systems : rule rejection

In this paper, efficient fuzzy model predictive control (EEMPC) is studied for nonlinear systems. Nonlinear system is represented by a Takagi-Sugeno (T-S) fuzzy system that is useful for nonlinear plant modeling. T-S fuzzy system consists of several linear models. The control problem is solved based on MPC which solves an online optimization problem with linear matrix inequality (LMI) at each step to compute an optimal control. This also increases the online computational complexity. The proposed efficient fuzzy MPC using rule rejection reduces such online computational complexity. It is possible to diminish a number of LMIs by rejecting less weighting rules in T-S fuzzy system at each time. To illustrate its effectiveness, the proposed method is applied to the inverted pendulum on a cart with nonlinear characteristics

Decentralized control based on modified fuzzy disturbance observer for interconnected nonlinear system

In this paper, a decentralized control based on modified fuzzy disturbance observer for interconnected nonlinear system is developed under the interaction and the internal/external disturbances and both. The performance of modified fuzzy disturbance observer (MFDO) is improved by considering the disturbance reconstruction error which consists of minimum approximation error and optimal parameter error. To adjust the parameters of the MFDO, two tuning methods are proposed by means of Lyapunov stability theorem. One employs only the disturbance observation error for adaptation law to guarantee that the MFDO monitors the disturbance. The other adopts the augmented error to achieve control objective as well. Finally, computer simulation results are illustrated to demonstrate the effectiveness of the MFDO in this paper.

Robust model predictive control for uncertain state delayed system using polytope dependent Lyapunov function

In this paper, we propose a method of computing a improved model predictive control (MPC) law for uncertain state delayed system with unknown bounded delay term and input constraints. The proposed method improves feasibility characteristics and system tracking performance by deriving a sufficient condition for the cost monotonicity using polytope dependent Lyapunov function corresponding to a different vertex. The control problem is formulated as a minimization of the upper bound of infinite horizon cost function satisfying the sufficient conditions. The polytope dependent Lyapunov function yield less conservative sufficient condition in terms of linear matrix inequalities (LMIs) so that it allows to design a more robust MPC. A numerical example is included to illustrate the effectiveness of the proposed method.

A new fuzzy stabilizer with tuning parameters for nonlinear system

In this paper, we consider a new fuzzy stabilizer which has tuning parameter so as to be applicable easily in real nonlinear plants. We adopted Inverse LQ(ILQ) regulator design method in which the state feedback control law is optimal for some unknown weights, thereby simplifying the design procedures. A design procedure is developed to extend the applications of ILQ regulator design method to nonlinear plants described by Takagi-Sugeno (TS) fuzzy model. Since ILQ control laws are optimal for each linear subsystems of TS fuzzy model, the weighted sum of general fuzzy stabilizer control law and ILQ control shows better performance than selecting one alternative. An numerical example is provided to illustrate the procedures of the proposed method.

Multivariable TS fuzzy model identification based on mixture of Gaussians

Identification of fuzzy models with multidimensional membership functions is considered. Many proposed fuzzy models use one-dimensional fuzzy sets and partition multidimensional input-spaces by Cartesian products of these univariate membership functions. The drawback of this approach is the complexity of the model in terms of the number of rules, which grows exponentially with the number of inputs (curse of dimensionality). Furthermore, decomposition errors which are detrimental to the performance of the model can be occurred. In order to avoid such drawbacks, it is desirable to work with multidimensional membership functions directly for the modeling of multidimensional and highly nonlinear systems. This paper proposes a clustering based identification of Takagi-Sugeno (TS) fuzzy models. The clusters are obtained by the expectation-maximization (EM) identification of a mixture of Gaussians. The proposed method is applied to well-known benchmark problems, and the obtained results are compared with results from the existing fuzzy clustering based identification techniques.

Dynamic output feedback H ∞ receding horizon controller design for uncertain discrete-time system with parameter dependent Lyapunov function

In this paper, we present a dynamic output feedback Hinfin receding horizon controller for uncertain systems with both norm bound uncertainty and disturbance using new parameter dependent Lyapunov function. This Lyapunov function is proposed to get a less conservative condition and guarantees both closed loop stability and the Hinfinnorm bound. The controller is obtained numerically from the output feedback Hinfin optimization problem, which is hardly solved analytically. The results are illustrated with numerical example

Dynamic output feedback H ∞ receding horizon controller design for uncertain discrete-time system with parameter dependent Lyapunov function

In this paper, we present a dynamic output feedback Hinfin receding horizon controller for uncertain systems with both norm bound uncertainty and disturbance using new parameter dependent Lyapunov function. This Lyapunov function is proposed to get a less conservative condition and guarantees both closed loop stability and the Hinfinnorm bound. The controller is obtained numerically from the output feedback Hinfin optimization problem, which is hardly solved analytically. The results are illustrated with numerical example