Sung-Hoe Huh

Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks

A direct adaptive state-feedback controller is proposed for highly nonlinear systems. We consider uncertain or ill-defined nonaffine nonlinear systems and employ a neural network (NN) with flexible structure, i.e., an online variation of the number of neurons. The NN approximates and adaptively cancels an unknown plant nonlinearity. A control law and adaptive laws for the weights in the hidden layer and output layer of the NN are established so that the whole closed-loop system is stable in the sense of Lyapunov. Moreover, the tracking error is guaranteed to be uniformly asymptotically stable (UAS) rather than uniformly ultimately bounded (UUB) with the aid of an additional robustifying control term. The proposed control algorithm is relatively simple and requires no restrictive conditions on the design constants for the stability. The efficiency of the proposed scheme is shown through the simulation of a simple nonaffine nonlinear system.

An adaptive fuzzy controller for power converters

An adaptive power converter control system that contains an adaptive fuzzy controller is presented. The proposed APCCS (adaptive power converter control system) combines fuzzy logic with adaptive learning algorithm to adjust parameters of the fuzzy control to the most appropriate values. Neither the exact mathematical models of power converters nor the tuning process of the parameters of the fuzzy control are needed in the proposed system. The transient, steady-state responses, and load regulation of the proposed system will be compared with those of the conventional fuzzy logic control system through the simulation results.

Self-organizing radial basis function network modeling for robot manipulator

Intelligent and adaptive approach to model two links manipulator system with self-organizing radial basis function (RBF) network is presented in this paper. The self-organizing algorithm that enables the RBF neural network to be structured automatically and on-line is developed, and with this proposed scheme, the centers and widths of RBF neural network as well as the weights are to be adaptively determined. Based on the fact that a 3-layered RBF neural network has the capability that represents the nonlinear input-output map of any nonlinear function to a desired accuracy, the input output mapping of the two link manipulator using the proposed RBF neural network is shown analytically through experimental results without knowing the information of the system in advance.

Robustly stable fuzzy controller for uncertain nonlinear systems with unknown input gain sign

We propose and analyze a robust adaptive fuzzy controller for uncertain nonlinear systems without information on the input gain sign. The proposed scheme completely overcomes the singularity problem which occurs in the indirect adaptive feedback linearizing control. No projection in the estimated parameters and no switching in the control input are needed. The stability of the closed-loop system is guaranteed in the Lyapunov viewpoint.

Modeling Corrupted Time Series Data via Nonsingleton Fuzzy Logic System

This paper is concerned with the modeling and identification of time series data corrupted by noise using nonsingleton fuzzy logic system (NFLS). Main characteristic of the NFLS is a fuzzy system whose inputs are modeled as fuzzy number. So the NFLS is especially useful in cases where the available training data, or the input data to the fuzzy logic system, are corrupted by noise Simulation results of the Box-Jenkin’s gas furnace data will be demonstrated to show the performance. We also compare the results of the NFLS approach with the results of using only a traditional fuzzy logic system. Thus it can be considered NFLS does a much better job of modeling noisy time series data than does a traditional fuzzy logic system.

Use of adaptive learning radial basis function network in real-time motion tracking of a robot manipulator

In this paper, real time motion tracking of a robot manipulator based on the adaptive learning radial basis function network is proposed. This method for adaptive learning needs little knowledge of the plant in the design processes. So the centers and widths of the employed radial basis function network (RBFN) as well as the weights are determined adaptively. With the help of the RBFN, motion tracking of the robot manipulator is implemented without knowing the information of the system in advance. Furthermore, identification error and the tuned parameters of the RBFN are guaranteed to be uniformly ultimately bounded in the sense of Lyapunovs stability criterion.

Robust adaptive flux observer for induction motor control

This paper presents a robust adaptive flux observer for induction motor control. The proposed flux observer guarantees the robustness against parametric uncertainties, and needs no estimation schemes with employing additional robustifying signal to cope with parametric uncertainties. The proposed adaptive scheme is determined so that the observer dynamics are stable in the sense of Lyapunov. The estimated rotor speed is then used to generate feedback control signal for speed sensorless vector control system. The simulation results are presented to show the validity and efficiency of the proposed system.