Chang Boon Low

Modeling and Analysis of Skidding and Slipping in Wheeled Mobile Robots: Control Design Perspective

This paper aims to give a general and unifying presentation on modeling of wheel mobile robots (WMRs) in the presence of wheel skidding and slipping from the perspective of control design. We present kinematic models that explicitly relate perturbations to the vehicle skidding and slipping. Four configurations of mobile robots are considered, and perturbations due to skidding and slipping are categorically classified as input-additive, input multiplicative, and/or matched/unmatched perturbations. Furthermore, we relate the WMRs maneuverability with the vehicle controllability that provides a measure on the WMR ability to track a trajectory in the presence of wheel skidding and slipping. These classifications and formulations lay a base for the deployments of various control design techniques to overcome the addressed perturbations.

GPS-Based Path Following Control for a Car-Like Wheeled Mobile Robot With Skidding and Slipping

Most wheeled mobile robot (WMR) controllers have been developed based on nonskidding and nonslipping assumptions. Unfortunately, wheel skidding and slipping are inevitable due to wheel tire-deformation; consequently, the stability and performance of these controllers are not guaranteed. This brief aims to develop a global positioning system (GPS)-based path following a controller for a car-like wheeled mobile robot in the presence of wheel skidding and slipping. The proposed control scheme uses real-time kinematic (RTK)-GPS and other aiding sensors to measure the WMRs posture, velocities, and perturbations due to wheel skidding and slipping. These measurements are applied to compensate the path following errors based on a backstepping controller. The reported experimental results validate the control scheme. With this solution, the WMR is able to maneuver with better precision in outdoor environments in the presence of wheel skidding and slipping.

Causality Assignment and Model Approximation for Hybrid Bond Graph: Fault Diagnosis Perspectives

Bond graph (BG) is an effective tool for modeling complex systems and it has been proven useful for fault detection and isolation (FDI) for continuous systems. BG provides the causal relations between systems variables which allow FDI algorithms to be developed systematically from the graph. In the same spirit, Hybrid bond graph (HBG) is a BG-based modeling approach which provides an avenue to model complex hybrid systems. However, due to mode-varying causality properties of HBG, HBG has not been efficiently-exploited for fault diagnosis. In this work, a comprehensive study on the HBG from FDI viewpoints is presented. Some properties pertaining to the HBG are gained in the study. Based on these findings, a causality assignment procedure and a model approximation technique are developed to achieve a HBG with a desirable causality assignment that leads a unified description of systems behavior. These results lay a foundation for quantitative FDI design for complex hybrid systems.

GPS-Based Tracking Control for a Car-Like Wheeled Mobile Robot With Skidding and Slipping

Many wheeled mobile robot (WMR) controllers are developed based on nonskidding and nonslipping assumptions; however, these assumptions are usually violated due to wheel tire deformation. As a result, the performance of these controllers is not guaranteed. This paper presents a GPS-based tracking controller for a car-like WMR in the presence of wheel skidding and slipping. The controller exploits real-time kinematic (RTK)-GPS and other aiding sensors to measure the WMRs posture, velocities, and perturbations due to wheel skidding and slipping for control compensation. The reported experimental results validate the control scheme.

Quantitative Hybrid Bond Graph-Based Fault Detection and Isolation

This research result consists of two parts: one is general theory on causality assignment for hybrid bond graph (HBG) and another is application of this concept to the quantitative fault diagnosis. From Low et al., 2008, a foundation for quantitative bond graph-based fault detection and isolation (FDI) design using HBG is laid. Useful causality properties pertaining to the HBG from FDI perspectives, and the concept of diagnostic hybrid bond graph (DHBG) which is advantageous for efficient and effective FDI applications are proposed. This paper is a continuation of our previous paper (Low et al., 2008). Here, the DHBG is exploited to analyze the hybrid systems fault detectability and fault isolability. Additionally, a quantitative FDI framework for effective fault diagnosis for hybrid systems is proposed. Simulation and experimental results are presented to validate some key concepts of the quantitative hybrid bond graph-based FDI framework.

Model-based Health Monitoring of Hybrid Systems

This book systematically presents a comprehensive framework and effective techniques for in-depth analysis, clear design procedure, and efficient implementation of diagnosis and prognosis algorithms for hybrid systems. It offers an overview of the fundamentals of diagnosis\prognosis and hybrid bond graph modeling. This book also describes hybrid bond graph-based quantitative fault detection, isolation and estimation. Moreover, it also presents strategies to track the system mode and predict the remaining useful life under multiple fault condition. A real world complex hybrid systema vehicle steering control systemis studied using the developed fault diagnosis methods to show practical significance. Readers of this book will benefit from easy-to-understand fundamentals of bond graph models, concepts of health monitoring, fault diagnosis and failure prognosis, as well as hybrid systems. The reader will gain knowledge of fault detection and isolation in complex systems including those with hybrid nature, and will learn state-of-the-art developments in theory and technologies of fault diagnosis and failure prognosis for complex systems.

Fault Detection Isolation and Estimation in a Vehicle Steering System

Recently, a bond-graph-based fault detection and isolation (FDI) framework has been developed with a new concept of global analytical redundancy relations (GARRs) (Low, Wang, Arogeti, and Luo, 2009, 2010; Low, Wang, Arogeti, and Zhang, 2010). This new concept allows the fault diagnosis for hybrid systems which consist of both continuous dynamics and discrete modes. A failure of a safety critical system such as the steering system of an automated guided vehicle may cause severe damage. Such failure can be avoided by an early detection and estimation of faults. In this paper, the newly developed FDI method is studied in details using an electrohydraulic steering system of an electric vehicle. The steering system and faults are modeled as a hybrid dynamic system by the hybrid bond graph (HBG) modeling technique. GARRs are then derived systematically from the HBG model with a specific causality assignment. Fault detection, isolation, and estimation are applied, experimental setup is described, and results are discussed.

Ground Target Tracking Using UAV with Input Constraints

This paper provides a solution to the problem of ground target tracking using an unmanned aerial vehicle (UAV) with control input constraints. Target tracking control with input constraints is an important and challenging topic in the study of UAVs. In order to achieve precise target tracking in the presence of constant background wind and target motion, this paper proposes a saturated heading rate controller based on a guidance vector field while the airspeed is held constant. This proposed approach guarantees the global convergence of the UAV to a desired circular orbit around a target. To estimate unknown constant background wind and target motion, an adaptive observer with bounded estimate is developed. Simulation results demonstrate the effectiveness of the proposed approach.

Model-based fault diagnosis/prognosis for wheeled mobile robots: a review

This paper presents a survey on model-based fault diagnostics and prognostics for wheeled mobile robots (WMRs). Numerous cases of faults diagnosis and prognosis solutions show that, for a complex machine like a WMR, model-based methods that utilize analytical models of the system are desirable because analytical models can well represent the nominal behaviours of the system. On the other hand, data-driven methods are not practical for WMRs since they require a lot of failure cases and data that are too costly, if possible at all, to obtain. Fault prognosis for a system in general and for WMRs in particular is still at the preliminary stage. System models can also be used to predict the remaining useful life of the system.

Mode Identification of Hybrid Systems in the Presence of Fault

A mode identification method for hybrid system diagnosis is proposed. The method is presented as a module of a quantitative health monitoring framework for hybrid systems. After fault occurrence, the fault is detected and isolated. The next step is fault parameters estimation, where the size of the fault is identified. Fault parameter estimation is based on data collected from the hybrid system while the system is faulty, and its dynamical model is partially unknown. A hybrid systems dynamics consists of continuous behavior and discrete states represented by modes. Fault parameter estimation requires knowledge of the monitored systems operating mode. The new method utilizes the partially known dynamical model to identify hybrid system modes in the presence of a single parametric fault.