Chang-Woo Park

T-S model based indirect adaptive fuzzy control using online parameter estimation

A parameter estimation scheme with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory for the general MIMO Takagi-Sugeno (T-S) fuzzy models. The parameters of the Takagi-Sugeno fuzzy models can be estimated by observing the behavior of the system and with the online parameter estimator, any type of fuzzy controllers works adaptively to the parameter perturbation. In order to show the applicability of the proposed estimator, an existing fuzzy state feedback controller is adopted and indirect adaptive fuzzy control design with the proposed estimator is shown. From the numerical simulations and experiments, it is shown that the derived adaptive law works for the estimation model to follows the parameterized plant model and the overall control system has robustness to the parameter perturbation.

Adaptive parameter estimator based on T-S fuzzy models and its applications to indirect adaptive fuzzy control design

In this paper, a new on-line parameter estimation methodology for the general continuous time Takagi-Sugeno (T-S) fuzzy model whose parameters are poorly known or uncertain is presented. An estimator with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the parameterized plant model. By the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for indirect adaptive fuzzy control. In order to show the applicability of the proposed estimator, indirect adaptive fuzzy control design examples with the proposed estimator are presented.

Design of an adaptive fuzzy model based controller for chaotic dynamics in Lorenz systems with uncertainty

This paper presents the control methodology for uncertain chaotic dynamics of Lorenz systems. An adaptive fuzzy control (AFC) scheme based on well-known Takagi-Sugeno (T-S) fuzzy models for the MIMO plants is constructed. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model (SRM) asymptotically with time for any bounded reference input signal. The proposed control is applied to control of an uncertain Lorenz system such as stabilization, synchronization and chaotic model following control (CMFC).

Fuzzy disturbance observer approach to robust tracking control of nonlinear sampled systems with the guaranteed suboptimal H/sub /spl infin// performance

This paper presents a new approach to robust tracking control of the nonlinear sampled systems using a discrete-time fuzzy disturbance observer (DFDO). Novel update and control laws are proposed to guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in a compact set. No persistence of excitation (PE) condition, nor the assumption on the slowness of the change of the fuzzy parameters, is required. In addition, a robustifying controller is designed to improve the tracking performance. Finally, a computer simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.

Indirect Adaptive Regulator Design Based on TSK Fuzzy Models

In this paper, we have proposed a new adaptive fuzzy control algorithm based on Takagi-Sugeno fuzzy model. The regulation problem for the uncertain SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the parameter approximation error is proved. Unlike the existing feedback linearization based methods, the proposed algorithm can guarantee the global stability in the presence of the singularity in the inverse dynamics of the plant. The performance of the proposed algorithm is demonstrated through the problem of balancing and swing-up of an inverted pendulum on a cart.

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.

Development and control of BLDC Motor using fuzzy models

This paper presents the design and control of a small brushless DC (BLDC) motor. In order to control the developed BLDC motor, an adaptive fuzzy control (AFC) scheme via parallel distributed compensation (PDC) is developed for the multi- input/multi-output plant model represented by the Takagi-Sugeno (TS) model. The alternative AFC scheme is proposed to provide asymptotic tracking of a reference signal for systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. The suggested design technique is applied to velocity control of a developed small BLDC motor.

Adaptive Model Reference Control Based on Takagi-Sugeno Fuzzy Models With Applications to Flexible Joint Manipulators

The control scheme using fuzzy modeling and Parallel Distributed Compensation (PDC) concept is proposed to provide asymptotic tracking of a reference signal for the flexible joint manipulators with uncertain parameters. From Lyapunov stability analysis and simulation results, the developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop multi-input/multi-output system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

Robust TSK fuzzy modeling approach using noise clustering concept for function approximation

This paper proposes the algorithm that additional term is added to an objective function of noise clustering algorithm to define fuzzy subspaces in a fuzzy regression manner to identify fuzzy subspaces and parameters of the consequent parts simultaneously and obtain robust performance against outliers.

Robust Feature Detection Using 2D Wavelet Transform under Low Light Environment

A novel local feature detection method is presented for mobile robot’s visual simultaneous localization and map building (v-SLAM). Camera-based visual localization can handle complicated problems, such as kidnapping and shadowing, which come with other type of sensors. Fundamental requirement of robust self-localization is robust key-point extraction under affine transform and illumination change. Especially, localization under low light environment is crucial for the purpose of guidance and navigation. This paper presents an efficient local feature extraction method under low light environment. A more efficient local feature detector and a compensation scheme of noise due to the low contrast images are proposed. The propose scene recognition method is robust against scale, rotation, and noise in the local feature space. We adopt the framework of scale-invariant feature transform (SIFT), where the difference of Gaussian (DoG)-based scale-invariant feature detection module is replaced by the difference of wavelet (DoW).