Gwi-Tae Park

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.

Robust adaptive fuzzy controller for nonlinear system using estimation of bounds for approximation errors

This paper describes the design of the robust adaptive fuzzy controller for uncertain single-input single-output nonlinear dynamical systems with unknown nonlinearities. These unknown nonlinearities are approximated by the fuzzy system with a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptive laws for the purpose of controlling the output of the nonlinear system to track a given trajectory. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive fuzzy model. The key assumption is that the reconstruction errors satisfy certain bounding conditions. The control law consists of two terms. One is the certainty equivalent control and the other is the bounding control. A bounding parameter adaptive law is used to obtain this bounding control. The overall control system guarantees that the tracking error converges in the small neighborhood of zero and that all signals involved are uniformly bounded. It is also shown that, in the special case, the tracking error exponentially converges to zero even though the approximation errors exist.

Direct adaptive self-structuring fuzzy controller for nonaffine nonlinear system

A direct adaptive state-feedback controller for highly nonlinear systems is proposed. This paper considers uncertain or ill-defined nonaffine nonlinear systems and employ a static fuzzy logic system (FLS) with an on-line structuring algorithm. The FLS approximates and adaptively cancels an unknown plant nonlinearity. A control law and adaptive laws for unknown fuzzy parameters and bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. The tracking error is guaranteed to be uniformly asymptotically stable rather than uniformly ultimately bounded with the aid of an additional robustifying control term. No a priori knowledge of an upper bound on an lumped uncertainty is required.

Output-feedback control of uncertain nonlinear systems using a self-structuring adaptive fuzzy observer

This paper describes the design of an output-feedback controller based on an adaptive fuzzy observer for uncertain single-input, single-output nonlinear dynamical systems. We have focused on the realization of a minimal dynamic order for the adaptive fuzzy observer. For this purpose, we adopt an adaptive fuzzy observer in which no strictly positive real (SPR) condition is needed and we combine a self-structuring scheme with an on-line estimation of fuzzy parameters. By using this proposed scheme, we can reduce the dynamic order of the adaptive output-feedback fuzzy control system. The Lyapunov synthesis approach is used to guarantee a global uniform ultimate boundedness property of the state estimation error and tracking error, as well as all other signals in the closed-loop system. No a priori knowledge of an upper limits on the uncertainties including optimal parameters and modeling errors is required. The theoretical results are illustrated through simulation examples.

Adaptive repetitive control for an eccentricity compensation of optical disk drivers

This paper present an adaptive repetitive control scheme for optical disk drivers to track a periodic reference signal with dynamic change in period. Periodic disturbances can be adequately attenuated using the concept of repetitive control, provided the known period. Optical disk drivers support various speeds. So optical disk drivers have the varying periodic disturbance. To deal with time varying periodic disturbances, a proposed repetitive controller is turned based on repetitive control to change sampling frequency to follow the change of reference period The proposed adaptive repetitive control consists of two portions, the repetitive controller and the frequency multiplier, where the former uses a varying sampler operating at a variable sampling rate maintained at fixed multiple times of the disturbance frequency and the latter generate the vary sampling frequency based on the disturbance frequency. An adaptive repetitive control scheme is proposed, implemented on experimental set of an optical disk driver, and demonstrate the effectiveness of the proposed methods and the improvement of the random access time.

Robust localization over obstructed interferences for inbuilding wireless applications

Location-awareness can be applied to the various consumer applications. A received signal strength (RSS) based localization system is relatively inexpensive and simple to be implemented without additional hardware supports. However, the radio signals are strongly affected by the obstructed interferences. This is a difficult problem for the RSS based localization systems to be implemented in real world. To solve this problem, we propose a practical and robust localization algorithm in the obstructed environments. The proposed algorithm uses the Maximum Likelihood Estimation (MLE) based on the position probability grid. In addition, the proposed algorithm detects and compensates the large measurement error using the Min-Max algorithm. We evaluated the performance of the proposed algorithm in the obstructed environments. Performance results show that the proposed algorithm outperforms other algorithms on the obstructed environments.

Dead time compensation in a vector-controlled induction machine

Dead time, which is inserted in PWM signals of VSIs, distorts the inverter output voltage waveforms and deteriorates the control performance of an induction machine by producing torque ripples. In this paper, a dead time compensation method in a vector controlled induction machine is proposed. The method is based on a feedforward approach that compensates the dead time effect by adding the compensating voltages to the inverter output voltage references in a two-phase stationary frame. The proposed method is only software intensive and easy to realize without additional hardware. Experimental results show the validity and effectiveness of the proposed method.

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.

Fuzzy Modeling of Zero Moment Point Trajectory for a Biped Walking Robot

The biped walking robot has almost the same mechanisms as a human and is suitable for moving in an environment which contains stairs, obstacles, etc. However, the complex dynamics involved make the biped robot control a challenging task. For the stability of the biped walking robot, the zero moment point (ZMP) trajectory in the robot foot support area is a significant criterion. If the ZMP during walking can be measured, it is possible to realize stable walking and to stably control the biped robot by the use of the measured ZMP. In this paper, actual ZMP data are measured in real time situations from practical biped walking robot and the obtained ZMP data are modeled by TS-type fuzzy system. By the simulation results, the TS-type fuzzy system can be effectively used to model practical biped walking robot.

A study on traffic signal control at signalized intersections in vehicular ad hoc networks

Abstract The Seoul metropolitan government has been operating a traffic signal control system with the name of COSMOS (Cycle, Offset, Split MOdel for Seoul) since 2001. COSMOS analyzes the degrees of saturation and congestion which are calculated by installing loop detectors. At present, subterranean inductive loop detectors are generally used for detecting vehicles but their maintenance is inconvenient and costly. In addition, the estimated queue length might be influenced by errors in measuring speed, because the detectors only consider the speed of passing vehicles. Instead, we proposed a traffic signal control algorithm which enables smooth traffic flow at intersections. The proposed algorithm assigns vehicles to the group of each lane and calculates traffic volume and congestion degree using the traffic information of each group through inter-vehicle communication in Vehicular Ad-hoc Networks (VANETs). This does not require the installation of additional devices such as cameras, sensors or image processing units. In this paper, the algorithm we suggest is verified for AJWT (Average Junction Waiting Time) and TQL (Total Queue Length) under a single intersection model based on the GLD (Green Light District) simulator. The results are better than random control method and best-first control method. For a generalization of the real-time control method with VANETs, this research suggests that the technology of traffic control in signalized intersections using wireless communication will be highly useful.