Suk-Kyo Hong

Antisway Tracking Control of Overhead Cranes With System Uncertainty and Actuator Nonlinearity Using an Adaptive Fuzzy Sliding-Mode Control

An adaptive fuzzy sliding-mode control (AFSMC) is presented for the robust antisway trajectory tracking of overhead cranes subject to both system uncertainty and actuator nonlinearity. First, a fuzzy sliding-mode control (FSMC) law is designed for the antisway trajectory tracking of the nominal plant. In association with a conventional trajectory tracking control law, this FSMC law guarantees asymptotic stability as well as improved transient response of the load sway dynamics while the trolley tracking error dynamics is rendered uniformly asymptotically stable. Second, a fuzzy uncertainty observer is designed to cope with system uncertainty as well as actuator nonlinearity present in an actual plant, and it is incorporated with the FSMC law for the development of the AFSMC law. In addition to stability analysis, the robust performance of the proposed AFSMC law is verified via numerical simulations and experiments.

Path planning of mobile robot using neural network

In this paper, the authors present an effective method to achieve both obstacle-avoidance and target-tracking for an autonomous mobile robot in an indoor environment. They employ a wall following algorithm using neural network pattern recognition to avoid obstacles. An autonomous mobile robot reaches a given goal target by tracking algorithm. In case obstacles are detected by sonar sensors, an autonomous mobile robot avoids collision with obstacles by wall following algorithm. They propose a simple making method to avoid being trapped in a local minima which was a serious problem in local path planning. Simulation results using mobile robot demonstrate that the proposed algorithms are well suited to the obstacle-avoidance using wall-following and the path planning task for target-tracking.

A roadmap construction algorithm for mobile robot path planning using skeleton maps

Path planning using conventional roadmaps, such as visibility graphs, probability roadmaps and skeleton maps, may have some disadvantages of long length, sharp turns or collisions with obstacles. Specifically, the paths using the conventional skeleton map have unnecessary turns around crossing points, which make longer paths and prevent the robot from moving smoothly. To improve the skeleton map, this paper proposes a new roadmap construction algorithm for path planning of a mobile robot using skeleton maps. The proposed algorithm alleviates the problems of the conventional algorithms by constructing roadmaps which consist of polygons around the crossing points. Simulation results show the efficiency of the proposed algorithm by comparing the results with those obtained using the conventional algorithm.

A study on the 3-DOF attitude control of free-flying vehicle

This paper is studying on the 3-DOF attitude control of a helicopter. The characteristic to be heavily coupled with inputs and outputs, and the serious nonlinearity appear in the flying helicopter system. Due to these characteristics of a helicopter, people apply the nonlinear control, multi-variable control or optimal control for the attitude control of helicopter. It is too hard to define the relations of input and output in a view of dynamics, therefore others also study this theme in a view of AI control-(artificial intelligent control) based on the fuzzy theory or neural network. But in the hovering, those characteristics-heavily coupled system and nonlinearity, reduce considerably. In this paper, we assume that those characteristics are able to be disregarded in hovering. The model helicopter system that is constructed with three independent SISO (single input single output) systems, is acquired by system identification method, and then the PD-control (proportional derivative control) is applied individually to three independent SISO systems for a 3-DOF attitude control of a model helicopter in hovering. Those assumptions are verified by the experiment.

Decoupling Control of A Class of Underactuated Mechanical Systems Based on Sliding Mode Control

This paper presents a sliding mode control (SMC) based decoupling control for a class of underactuated mechanical systems. For comprehensive description of the proposed approach, we consider a two degree-of-freedom underactuated system. The whole systems, firstly, decomposed into two subsystems and then individual controllers are designed for each subsystem via SMC. Considering zero-dynamics of each closed loop system, a controller for the whole system is designed by a coupling method of each individual controller such that the whole closed loop system is semi-globally asymptotically stable. The proposed design approach is applied to an inverted pendulum system and simulation test is performed to demonstrate the effectiveness of the proposed design method

Hybrid control for longitudinal speed and traction of vehicles

This paper presents a hybrid system approach to longitudinal speed and traction control of a vehicle with wheel slip constraints. The vehicle system is modeled as a hybrid system where the system is divided into local subsystems and each subsystem is selected to control the vehicle, in terms of control modes and operating region. Controllers are designed to track a desired speed value while maintaining the safety constraint that the absolute value of the slip between wheels and ground must be less than a given limit value to prevent the wheels from skidding or spinning. Simulation results are provided to show the feasibility of the proposed control system.

HYWINMARC: an autonomic management architecture for hybrid wireless networks

The envisioned realization of ubiquity has resulted into the emergence of new kinds of the hybrid networks. The modern hybrid networks, e.g. combination of wireless mesh and Mobile Ad-hoc Networks (MANETs), help realize ubiquity through spontaneous networking. The network management for these hybrid networks is different from conventional and infrastructure based network management. Heterogeneity, mobility, dynamic topologies, physical security, and survivability make the challenge hard. A new class of management called self-management can effectively be used to cater for the autonomous behavior of hybrid networks. We present HYbrid WIreless Network Management ARChitecture (HYWINMARC), a three-tier framework, covering all the management levels, for autonomic network management for hybrid networks. We integrate policy-based network management with mobile-agent technology and design a prototype for a context-aware and self-managing architecture. The context information is collected, from all levels in network hierarchy through monitoring agents, and is used to apply needed self-management operations that include self-optimization, self-healing, self-configuration, and self-growing.

Adaptive fuzzy nonlinear anti-sway trajectory tracking control of uncertain overhead cranes with high-speed load hoisting motion

In the present paper, an adaptive fuzzy nonlinear control (AFNC) law is proposed for the anti-sway trajectory tracking of uncertain overhead cranes with high-speed hoisting motion. In the proposed AFNC, fuzzy systems are utilized in two different ways. One is for fuzzy uncertainty observer (FUO) and the other is for fuzzy nonlinear control (FNC). Two FUOs are employed to cope with system uncertainties in an adaptive manner, while an FNC is developed to reduce the high-frequency chattering effect of variable structure control. Through the stability analysis and numerical simulations, it is shown that the proposed AFNC guarantees robust asymptotic stability of trajectory tracking error dynamics in trolley and hoisting motion as well as in sway dynamics, against not only the coupling effect of hoisting velocity and hoisting rope length but also system uncertainties such as system parameter variations, external wind disturbance, and unknown actuator nonlinaerities.

A Fix-Up for the EKF Parameter Estimator

Abstract We have reduced recursive parameter estimation to Kalman filtering, with a few added fixes. By incorporating projections in the parameter gain updates and parameter variance estimates, the recursive maximum likelihood method asymptotically becomes a reformulation and fix-up of the extended Kalman filter used as a parameter estimator (EKFPE), except that an additional n x n linear symmetric matrix must also be updated for each parameter estimate. Estimates for both the process and measurement noise variances, as well as for structural parameters, have been proven globally convergent to a local maximum of the likelihood function. This obviates the usual guesswork in finding noise variances when fitting data using the EKFPE, and assures the existence of the innovations representation for the recursive maximum likelihood method. Slightly non-linear and also slightly unstable linear, as well as drastically time-varying stable linear, system parameters can be estimated even in severe noise environments On average, the rate of convergence of parameter estimates appears to be faster than other methods if no projection limit is hit.

Unifying strategies of obstacle avoidance and shooting for soccer robot systems

This paper presents unifying strategies of obstacle avoidance and shooting for robot soccer systems. Using a fuzzy-net based avoidance algorithm and limit-cycle based shooting algorithm, we propose two unifying schemes based on both parallel and series combination of them to achieve smooth transition between obstacle avoidance and shooting mode. Two proposed schemes are compared for several scenarios in experiments to evaluate the performance of them.