Euntai Kim

New approaches to relaxed quadratic stability condition of fuzzy control systems

This paper deals with the quadratic stability conditions of fuzzy control systems that relax the existing conditions reported in the previous literatures. Two new conditions are proposed and shown to be useful in analyzing and designing fuzzy control systems. The first one employs the S-procedure to utilize information regarding the premise parts of the fuzzy systems. The next one enlarges the class of fuzzy control systems, whose stability is ensured by representing the interactions among the fuzzy subsystems in a single matrix and solving it by linear matrix inequality. The relationships between the suggested stability conditions and the conventional well-known stability conditions reported in the previous literatures are also discussed, and it is shown in a rigorous manner that the second condition of this paper includes the conventional conditions. Finally, some examples and simulation results are presented to illustrate the effectiveness of the stability conditions.

A new approach to fuzzy modeling

This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugenos model (1985), because it has the same structure as that of Takagi and Sugenos model. It is also as easy to implement as Sugeno and Yasukawas model (1993) because its identification mimics the simple identification procedure of Sugeno and Yasukawas model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used, which is a modified version of fuzzy C-means (FCM). In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm.

A fuzzy disturbance observer and its application to control

In this paper, a fuzzy disturbance observer (FDO) is developed and its application to the control of a nonlinear system under the internal and external disturbances is presented. To construct the FDO, two parameter tuning methods are proposed and shown to be useful in adjusting the parameters of the FDO. The first tuning method employs the disturbance observation error to guarantee that the FDO monitors the unknown disturbance. The next one enlarges the concept of error and introduces augmented error to guarantee that the FDO monitors the disturbance and the control objective is achieved. In addition, the relationships between the suggested FDO-based control and the conventional adaptive fuzzy controls reported in the previous literatures are discussed and it is shown in a rigorous manner that the disturbance observation error or the augmented error converges to a region of which size can be kept arbitrarily small. Finally, some examples and computer simulation results are presented to illustrate the effectiveness and the applicability of the FDO.

A soft computing approach to localization in wireless sensor networks

In this paper, we propose two intelligent localization schemes for wireless sensor networks (WSNs). The two schemes introduced in this paper exhibit range-free localization, which utilize the received signal strength (RSS) from the anchor nodes. Soft computing plays a crucial role in both schemes. In the first scheme, we consider the edge weight of each anchor node separately and combine them to compute the location of sensor nodes. The edge weights are modeled by the fuzzy logic system (FLS) and optimized by the genetic algorithm (GA). In the second scheme, we consider the localization as a single problem and approximate the entire sensor location mapping from the anchor node signals by a neural network (NN). The simulation and experimental results demonstrate the effectiveness of the proposed schemes by comparing them with the previous methods.

Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic

Many robot controllers require not only joint position measurements but also joint velocity measurements; however, most robotic systems are only equipped with joint position measurement devices. In this paper, a new output feedback tracking control approach is developed for the robot manipulators with model uncertainty. The approach suggested herein does not require velocity measurements and employs the adaptive fuzzy logic. The adaptive fuzzy logic allows us to approximate uncertain and nonlinear robot dynamics. Only one fuzzy system is used to implement the observer-controller structure of the output feedback robot system. It is shown in a rigorous manner that all the signals in a closed loop composed of a robot, an observer, and a controller are uniformly ultimately bounded. Finally, computer simulation results on three-link robot manipulators are presented to show the results which indicate good position tracking performance and robustness against payload uncertainty and external disturbances.

A transformed input-domain approach to fuzzy modeling

This paper presents an explanation of a fuzzy model considering the correlation among components of input data. Generally, fuzzy models have a capability of dividing an input space into several subspaces compared to a linear model. But hitherto suggested fuzzy modeling algorithms have not taken into consideration the correlation among components of sample data and have addressed them independently, which results in an ineffective partition of the input space. In order to solve this problem, this paper proposes a new fuzzy modeling algorithm, which partitions the input space more effectively than conventional fuzzy modeling algorithms by taking into consideration the correlation among components of sample data. As a way to use the correlation and divide the input space, the method of principal component is used. Finally, the results of the computer simulation are given to demonstrate the validity of this algorithm.

A simply identified Sugeno-type fuzzy model via double clustering

Abstract Recently fuzzy models have received significant attention from various fields and many researchers have conducted researches regarding them. Especially, Sugeno suggested so called the Sugeno-type fuzzy model which superbly describes a nonlinear system. In this paper, we suggest a new identification method for the Sugeno-type fuzzy model. The suggested algorithm is much simpler than the original identification strategy adopted in [1–4]. The algorithm suggested in this paper is similar to that of [5,6] in that the algorithm suggested in this paper consists of two steps: coarse tuning and fine tuning. In this paper, double clustering strategy is proposed for coarse tuning. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

Stability analysis and synthesis for an affine fuzzy system via LMI and ILMI: discrete case

This paper develops a stability analysis and controller synthesis methodology for a discrete affine fuzzy system based on the convex optimization techniques. In analysis, the stability condition under which the affine fuzzy system is quadratically stable is derived. Then, the condition Is recast in the formulation of Linear Matrix Inequalities (LMI) and numerically addressed. The emphasis of this paper, however, is on the synthesis of fuzzy controller based on the derived stability condition. In synthesis, the stabilizability condition turns out to be in the formulation of nonconvex matrix inequalities and is solved numerically in an iterative manner. Discrete iterative LMI (ILMI) approach is proposed to obtain the feasible solution for the synthesis of the affine fuzzy system. Finally, the applicability of the suggested methodology is demonstrated via some examples and computer simulations.

Robust tracking control of an electrically driven robot: adaptive fuzzy logic approach

This paper is concerned with the robust tracking control of an electrically driven robot with the model uncertainties in the robot dynamics and the motor dynamics. The motors driving the joints of the robot are assumed to be equipped with only the joint position and the current measurement devices. Adaptive fuzzy logic and adaptive backstepping method are employed to provide the solution to the control problem. The suggested method does not require the measurement of the velocity nor the acceleration. Simulation results from a two-link electrically driven robot show the satisfactory performance of the proposed control scheme even in the presence of internal model uncertainties in both the robot and motor dynamics and external disturbances

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.