Hyung-Jin Kang

Comments on "Robust stabilization of a class of uncertain nonlinear systems via fuzzy control: quadratic stabilizability, H/sup /spl infin// control theory, and linear matrix inequalities"

This paper presents stability analysis for a class of uncertain nonlinear systems and a method for designing robust fuzzy controllers to stabilize the uncertain nonlinear systems, First, a stability condition for Takagi and Sugenos fuzzy model is given in terms of Lyapunov stability theory. Next, new stability conditions for a generalized class of uncertain systems are derived from robust control techniques such as quadratic stabilization, H/sup /spl infin// control theory, and linear matrix inequalities. The derived stability conditions are used to analyze the stability of Takagi and Sugenos fuzzy control systems with uncertainty which can be regarded as a generalized class of uncertain nonlinear systems, The design method employs the so-called parallel distributed compensation, important issues for the stability analysis and design are remarked. Finally, three design examples of fuzzy controllers for stabilizing nonlinear systems and uncertain nonlinear systems are presented.

A new sliding-mode control with fuzzy boundary layer

This study develops a sliding-mode controller based on fuzzy variable boundary layer with a control gain and boundary layer thickness as design parameters. The control gain is an important factor affecting the control performance of variable structure system (VSS). Sliding-mode controllers based on a variable boundary layer are superior to the fixed-layer method for tracking. In order to regulate the design parameters and increase operating efficiency, the proposed methodologies make use of fuzzy inference which reduces the number of fuzzy inputs. By using fuzzy algorithms in choosing a control gain and boundary layer, we propose methods which have better tracking performance than the conventional method. Finally, the results of simulation are given to demonstrate the validity of this algorithm.

A New Evolutionary Particle Filter for the Prevention of Sample Impoverishment

Particle filters perform the nonlinear estimation and have received much attention from many engineering fields over the past decade. Unfortunately, there are some cases in which most particles are concentrated prematurely at a wrong point, thereby losing diversity and causing the estimation to fail. In this paper, genetic algorithms (GAs) are incorporated into a particle filter to overcome this drawback of the filter. By using genetic operators, the premature convergence of the particles is avoided and the search region of particles enlarged. The GA-inspired proposal distribution is proposed and the corresponding importance weight is derived to approximate the given target distribution. Finally, a computer simulation is performed to show the effectiveness of the proposed method.

Robust stability analysis and design method for the fuzzy feedback linearization regulator

A robust stability analysis and design method for a fuzzy feedback linearization regulator is presented. The well-known Takagi-Sugeno fuzzy model is used as the nonlinear plant model. Uncertainties and disturbance are assumed to be included in the model structure with known bounds. For these structured uncertainties, stability robustness of the closed system is analyzed in both input-output sense and Lyapunov sense. The robust stability conditions are proposed using multivariable circle criterion and the relationship between input-output stability and Lyapunov stability. Also, based on the stability analysis, a systematic design procedure for the fuzzy feedback linearization regulator is provided. The effectiveness of the proposed analysis and design method is illustrated by a simple example.

Numerical stability analysis of fuzzy control systems via quadratic programming and linear matrix inequalities

This paper proposes a numerical stability analysis methodology for the singleton-type linguistic fuzzy control systems based on optimization techniques. First, it demonstrates that a singleton-type linguistic fuzzy logic controller (FLC) can be converted into a region-wise sector-bounded controller or, more generally, a polytopic system by quadratic programming (QP). Next, the convex optimization technique called linear matrix inequalities (LMI) is used to analyze the closed loop of the converted polytopic system. Finally, the applicability of the suggested methodology is highlighted via simulation results.

Comments on "H/sub /spl infin// tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach" [with reply]

The objective of H/sub /spl infin// disturbance attenuation problem is to attenuate the effect of disturbance to the prescribed level and achieve performance robustness. Chen, Lee and Chang (Fuzzy Syst., vol.4, p.32-43, 1996) proposed an adaptive fuzzy H/sub /spl infin// disturbance attenuation algorithm. The fuzzy approximation error, which is influenced by the control input, is taken as the disturbance signal in the proposed algorithm. The authors argue that, because of the tradeoff between the attenuation level and the control input, performance robustness cannot be achieved by the proposed algorithm. The original authors agree that the approximation error will influence the performance robustness, especially in the case without external disturbance and with very small attenuation level. However, in practical control applications, very small attenuation levels are avoided in order to prevent high-gain control.

A new approach to adaptive fuzzy control

In this paper, we have proposed the new approach to the indirect adaptive fuzzy control algorithms using Takagi-Sugeno fuzzy model. The regulation problem for the SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the estimation error is proved. The performance of the proposed algorithm is illustrated by a simple example.

Graphical and numerical approach to robust stability analysis of fuzzy modeled systems with parametric uncertainty and disturbance

Abstract In this paper, robust stability analysis methods for the fuzzy feedback control systems are presented, the graphical method via multivariable circle criterion and the numerical method via linear matrix inequalities (LMI). The well-known Takagi–Sugeno fuzzy model is used as the nonlinear plant model. Uncertainties are assumed to be included in the model structure with known bounds. For these structured parametric uncertainties, L 2 robust stability analysis is performed by taking external disturbance as input and system state as output. The effectiveness of the proposed methods is illustrated by examples.

L/sub 2/ robust stability analysis for the fuzzy feedback linearization regulator

Using Takagi-Sugeno fuzzy model, feedback linearisation which is widely used in nonlinear control theory can be applied to fuzzy control. If perfect linearization can be obtained, the stability can be analysed by linear system theory. However, since modeling uncertainty and disturbance affect the stability, the robust stability analysis is needed to deal with uncertainty. In this paper, the sufficient L/sub 2/ robust stability condition is derived from the multivariable circle criterion. A simple example illustrates the proposed method.

Camera and LIDAR Combined System for On-Road Vehicle Detection

In this paper, we design an on-road vehicle detection system based on the combination of a camera and a LIDAR system. In the proposed system, the candidate area is selected from the LIDAR data using a grouping algorithm. Then, the selected candidate area is scanned by an SVM to find an actual vehicle. The morphological edged images are used as features in a camera. The principal components of the edged images called eigencar are employed to train the SVM. We conducted experiments to show that the on-road vehicle detection system developed in this paper demonstrates about 80% accuracy and runs with 20 scans per second on LIDAR and 10 frames per second on camera.