• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2398-2400
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• 2000

In this paper, we consider multi-objective synthesis of fuzzy controllers for a widely used special class of the Takagi-Sugeno(TS) fuzzy systems. We propose a new fuzzy controller utilizing the strategy of rescaling and show that synthesis of the proposed controllers satisfying multiple design objectives can be reduced to a simple linear matrix inequality(LMI) problem. Finally, an application to an inverted pendulum on a cart is presented to illustrate the validity of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.5
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• pp.11-18
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• 2000

In this paper, we propose a new condition to test the quadratic stability of fuzzy control systems. The Proposed one enlarges the class of fuzzy control systems whose stability is ensured by representing the interactions among the fuzzy subsystems in a single power matrix and solving it by LMI (linear matrix inequality). Compared with the previous methods, the proposed one relaxes the stability condition to release the conservatism. Finally, the relationship between the suggested condition and the conventional well-known stability conditions reported in the previous literatures is discussed and it is shown in a rigorous manner that the proposed one includes the conventional conditions.

• Wind and Structures
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• v.38 no.3
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• pp.161-170
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• 2024

In recent years, there have been an increasing number of experimental studies showing the need to include robustness criteria in the design process to develop complex active control designs for practical implementation. The paper investigates the crosswind aerodynamic parameters after the blocking phase of a two-dimensional square cross-section structure by measuring the response in wind tunnel tests under light wind flow conditions. To improve the accuracy of the results, the interpolation of the experimental curves in the time domain and the analytical responses were numerically optimized to finalize the results. Due to this combined effect, the three aerodynamic parameters decrease with increasing wind speed and asymptotically affect the upper branch constants. This means that the aerodynamic parameters along the density distribution are minimal. Taylor series are utilized to describe the fuzzy nonlinear plant and derive the stability analysis using polynomial function for analyzing the aerodynamic parameters and numerical simulations. Due to it will yield intricate terms to ensure stability criterion, therefore we aim to avoid kinds issues by proposing a polynomial homogeneous framework and utilizing Euler's functions for homogeneous systems. Finally, we solve the problem of stabilization under the consideration by SOS (sum of squares) and assign its fuzzy controller based on the feasibility of demonstration of a nonlinear system as an example.

• Steel and Composite Structures
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• v.47 no.4
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• pp.513-521
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• 2023

The goal of this work is to create a new and improved GWO (Grey Wolf Optimizer), the so-called Robot GWO (RGWO), for dynamic and static target tracking involving multiple robots in unknown environmental conditions. From applying ourselves with the Gray Wolf Optimization Algorithm (GWO) and how it works, as the name suggests, it is a nature-inspired metaheuristic based on the behavior of wolf packs. Like other nature-inspired metaheuristics such as genetic algorithms and firefly algorithms, we explore the search space to find the optimal solution. The results also show that the improved optimal control method can provide superior power characteristics even when operating conditions and design parameters are changed.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1220-1223
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• 2002

In this paper, numerical robust stability analysis method and its design are presented. L$_2$robust stability of the fuzzy system is analyzed by casting the systems into the diagonal norm bounded linear differential inclusions (DNLDI) formulation. Based on the linear matix inequality (LMI) optimization programming, a numerical method for finding the maximum stable ranges of the fuzzy feedback linarization control gains is proposed.

• Smart Structures and Systems
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• v.30 no.1
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• pp.1-15
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• 2022

This article focuses on stability analysis and fuzzy controller synthesis for large neural network (NN) systems consisting of several interconnected subsystems represented by the NN model. Advanced and fuzzy NN differential inclusion (NNDI) for stability based on the developed algorithm with H infinity can be designed based on the evolved biological design. This representation is constructed using sector linearity for NN models. Sector linearity transforms a non-linear model into a linear model based on proposed operations. New sufficient conditions are realized in the form of LMI (linear matrix inequalities) to ensure the asymptotic stability of the trans-Lyapunov function. This transforms the nonlinear model into a linear model based on multiple rules. At last, a numerical case study with simulations is derived as illustration to prove its feasibility in real nonlinear structures.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1724_1725
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• 2009

In this paper, new approaches to optimal controller design for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems are proposed based on a relaxed approach, in which non-quadratic Lyapunov function and non-parallel distributed compensation (PDC) control law are used. New relaxed conditions and linear matrix inequality (LMI) based design methods are proposed that allow outperforming previous results found in the literature. Finally, an example is given to demonstrate the efficiency of the proposed approaches.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.4
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• pp.1-10
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• 2001

In this paper, we propose the stability analysis and design methodology for the discrete-time affine Type III fuzzy system via the convex optimization technique. First, the stability condition is derived under which the discrete-time affine Type III fuzzy system is quadratically stable in the large. Next, the derived condition is reformulated into the convex optimization problem called Linear Matrix Inequalities (LMI) and numerically addressed. Finally, the effectiveness and the feasibility of the proposed analysis and design methodology is highlighted via an example and its computer simulation result.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.72-80
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• 1997

A new design method for Takagi-Sugeno (T-S in short) fuzzy controller which guarantees global asymptotic stability and satisfies a desired performance is proposed in this paper. The method uses LMI(Linear Matrix Inequality) approach to find the common symmetric positive definite matrix P and feedback fains K/sub i/, i= 1, 2,..., r, numerically. The LMIs for stability criterion which treats P and K'/sub i/s as matrix variables is derived from Wang et al.'s stability criterion. Wang et al.'s stability criterion is nonlinear MIs since P and K'/sub i/s are coupled together. The desired performance is represented as $ LMIs which place the closed-loop poles of $ local subsystems within the desired region in s-plane. By solving the stability LMIs and pole placement constraint LMIs simultaneously, the feedback gains K'/sub i/s which gurarntee global asymptotic stability and satisfy the desired performance are determined. The design method is verified by designing a T-S fuzzy controller for an inverted pendulum with a cart using the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.51-60
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• 1998

There have been several recent studies concerning the stability of fuzzy control system and the synthesis of stabilizing fuzzy controllers. This paper reports on a related study nf the TS (Takagi-Sugeno) fuzzy systems, and it is shown that the controller synthesis problems for the nonlinear systems described by the TS fuzzy model can be reduced to convex problems involving LMIs (linear matrix ineclualities). After classifying the TS fuzzy systems into three families based on how diverse their input matrices are, different controller synthesis procedure is given for each of these families. A numerical example is presented to illustrate the synthesis procedures developed in this paper.