54 research outputs found
Observability, Identifiability and Sensitivity of Vision-Aided Navigation
We analyze the observability of motion estimates from the fusion of visual
and inertial sensors. Because the model contains unknown parameters, such as
sensor biases, the problem is usually cast as a mixed identification/filtering,
and the resulting observability analysis provides a necessary condition for any
algorithm to converge to a unique point estimate. Unfortunately, most models
treat sensor bias rates as noise, independent of other states including biases
themselves, an assumption that is patently violated in practice. When this
assumption is lifted, the resulting model is not observable, and therefore past
analyses cannot be used to conclude that the set of states that are
indistinguishable from the measurements is a singleton. In other words, the
resulting model is not observable. We therefore re-cast the analysis as one of
sensitivity: Rather than attempting to prove that the indistinguishable set is
a singleton, which is not the case, we derive bounds on its volume, as a
function of characteristics of the input and its sufficient excitation. This
provides an explicit characterization of the indistinguishable set that can be
used for analysis and validation purposes
Identification and Optimal Linear Tracking Control of ODU Autonomous Surface Vehicle
Autonomous surface vehicles (ASVs) are being used for diverse applications of civilian and military importance such as: military reconnaissance, sea patrol, bathymetry, environmental monitoring, and oceanographic research. Currently, these unmanned tasks can accurately be accomplished by ASVs due to recent advancements in computing, sensing, and actuating systems. For this reason, researchers around the world have been taking interest in ASVs for the last decade. Due to the ever-changing surface of water and stochastic disturbances such as wind and tidal currents that greatly affect the path-following ability of ASVs, identification of an accurate model of inherently nonlinear and stochastic ASV system and then designing a viable control using that model for its planar motion is a challenging task. For planar motion control of ASV, the work done by researchers is mainly based on the theoretical modeling in which the nonlinear hydrodynamic terms are determined, while some work suggested the nonlinear control techniques and adhered to simulation results. Also, the majority of work is related to the mono- or twin-hull ASVs with a single rudder. The ODU-ASV used in present research is a twin-hull design having two DC trolling motors for path-following motion.
A novel approach of time-domain open-loop observer Kalman filter identifications (OKID) and state-feedback optimal linear tracking control of ODU-ASV is presented, in which a linear state-space model of ODU-ASV is obtained from the measured input and output data. The accuracy of the identified model for ODU-ASV is confirmed by validation results of model output data reconstruction and benchmark residual analysis. Then, the OKID-identified model of the ODU-ASV is utilized to design the proposed controller for its planar motion such that a predefined cost function is minimized using state and control weighting matrices, which are determined by a multi-objective optimization genetic algorithm technique. The validation results of proposed controller using step inputs as well as sinusoidal and arc-like trajectories are presented to confirm the controller performance. Moreover, real-time water-trials were performed and their results confirm the validity of proposed controller in path-following motion of ODU-ASV
Advanced Computational-Effective Control and Observation Schemes for Constrained Nonlinear Systems
Constraints are one of the most common challenges that must be faced in control systems design. The sources of constraints in engineering applications are several, ranging from actuator saturations to safety restrictions, from imposed operating conditions to trajectory limitations. Their presence cannot be avoided, and their importance grows even more in high performance or hazardous applications. As a consequence, a common strategy to mitigate their negative effect is to oversize the components. This conservative choice could be largely avoided if the controller was designed taking all limitations into account. Similarly, neglecting the constraints in system estimation often leads to suboptimal solutions, which in turn may negatively affect the control effectiveness. Therefore, with the idea of taking a step further towards reliable and sustainable engineering solutions, based on more conscious use of the plants' dynamics, we decide to address in this thesis two fundamental challenges related to constrained control and observation.
In the first part of this work, we consider the control of uncertain nonlinear systems with input and state constraints, for which a general approach remains elusive.
In this context, we propose a novel closed-form solution based on Explicit Reference Governors and Barrier Lyapunov Functions. Notably, it is shown that adaptive strategies can be embedded in the constrained controller design, thus handling parametric uncertainties that often hinder the resulting performance of constraint-aware techniques.
The second part of the thesis deals with the global observation of dynamical systems subject to topological constraints, such as those evolving on Lie groups or homogeneous spaces. Here, general observability analysis tools are overviewed, and the problem of sensorless control of permanent magnets electrical machines is presented as a case of study. Through simulation and experimental results, we demonstrate that the proposed formalism leads to high control performance and simple implementation in embedded digital controllers
Exergy-based Planning and Thermography-based Monitoring for energy efficient buildings - Progress Report (KIT Scientific Reports ; 7632)
Designing and monitoring energy efficiency of buildings is vital since they account for up to 40% of end-use energy. In this study, exergy analysis is investigated as a life cycle design tool to strike a balance between thermodynamic efficiency of energy conversion and economic and environmental costs of construction. Quantitative geo-referenced thermography is proposed for monitoring and quantitative assessment via continued simulation and parameter estimation during the operating phase
Model based fault detection and isolation approach for actuator and sensor faults in a UAV
Thesis (MEng)--Stellenbosch University, 2021.ENGLISH ABSTRACT: This thesis presents the design and validation of model-based fault detection and
isolation (FDI) approach for unmanned aerial vehicles (UAV). In safety-critical sys-
tems such as chemical, nuclear plants and passenger aircraft, FDI is typically founded
on hardware redundancy. In hardware redundancy, multiple actuators are spatially
distributed to localise faults quickly, and sensor measurements are compared for
consistency. The primary drawback with hardware redundancy is the increased
installation complexity, weight, and costs. With modern computing technologies,
model-based FDI offers a cost-effective, iterative and efficient FDI design process,
verifiable with high fidelity computer-aided simulation (CAS).
This thesis investigates the application of the Two-Stage Kalman filter (TSKF)
to the problem of FDI. The TSKF solves the main deficiencies faced with the aug-
mented state Kalman filter (ASKF), namely, numerical instability in ill-conditioned
systems, and computational inefficiency where large parameter vectors are aug-
mented. The TSKF approach utilises two parallel reduced-order KFs to estimate
the system state and the parameter vectors separately. The UAVâs two rudders are
not "isolable" because they produce identical moments. A novel active FDI (AFDI)
method is proposed to isolate rudder actuator faults.
The FDI displays high noise sensitivity under the evere Dryden turbulence
model, resulting in high false detection and missed detection rates. A novel adap-
tive technique is proposed to improve the robustness and sensitivity of the FDI.
Unlike most methods which rely on a single scaling factor, the proposed adaptation
technique employs multiple factors to weight the spread of fault parameter covari-
ance matrix in the direction of flow of information, resulting in selective adaptation.
Fault parameter variations are nonuniform in time and space. A static alarm
threshold will induce high false alarms or missed alarms when set to low or too
high, respectively. A novel adaptive threshold based on the normalised innovation
squared (NIS) is proposed. A Monte Carlo campaign is carried out to validate the
FDI while fault-sizes, the aircraftâs physical parameters, and disturbances are scat-
tered, each with a distinct mean dispersion. The proposed strategy exhibits high
robustness to noise and sensitivity to faults which indicates a reliable FDI.AFRIKAANSE OPSOMMING: Hierdie tesis beskryf die ontwerp en validering van ân model-gebaseerde foutop-
sporing en isolasie (âfault deteciton and isolation (FDI)â) tegniek vir onbemande
lugvoertuie (âunmanned aerial vehicles (UAVs)â). In veiligheidskritieke stelsels
soos chemiese aanlegte, kernkragaanlegte, en passasiersvliegtuie, word FDI gewoon-
lik gebaseer op hardeware-oortolligheid. Vir hardeware-oortolligheid word verskeie
aktueerders ruimtelik versprei om foute vinnig op te spoor, en sensormetings word
vergelyk vir ooreenstemming. Die primĂȘre nadeel van hardeware-oortolligheid is
die verhoogde installasie-kompleksiteit, gewig en koste. Met moderne rekenaarteg-
nologieĂ« bied model-gebaseerde FDI ân koste-effektiewe, iteratiewe en doeltref-fende FDI-ontwerpproses met ân hoĂ« betroubaarheid wat bevestig kan word met
rekenaargesteunde simulasie.
Hierdie tesis ondersoek die toepassing van die twee-stadium Kalman filter (âtwo-
stage Kalman filter (TSKF)â) op die probleem van FDI. Die TSKF los die belangrik-
ste tekortkominge van die uitgebredie-toestand Kalman-filter (âaugmented state
Kalman filter (ASKF)â) op, naamlik numeriese onstabiliteit in swak gekondisioneerde
stelsels, en berekeningsondoeltreffendheid waar groot parametervektore bygevoeg
word. Die TSKF-benadering gebruik twee parallelle Kalman filters met vermin-
derde orde om die stelseltoestand en die parametervektore afsonderlik af te skat.
Die UAV se twee roere (âruddersâ) is egter nie âisoleerbaarâ nie omdat dit hulle
identiese draaimoment veroorsaak. ân Nuwe aktiewe FDI-metode (AFDI) word
voorgestel om die roeraktueerderfoute te isoleer.
Die FDI vertoon hoë sensitiwiteit vir geraas vanaf erge turbulensie soos gemod-
elleer deur die Dryden-turbulensie-model, wat lei tot ân groot aantal vals deteksies
en gemiste deteksies. ân Nuwe aanpassingstegniek word voorgestel om die robu-
ustheid en sensitiwiteit van die FDI te verbeter. Anders as die meeste metodes wat
op een enkele skaalfaktor staatmaak, gebruik die voorgestelde aanpassingstegniek
verskeie faktore om die verspreiding van die foutparameterkovariansiematriks in
die rigting van informasievloei te weeg, wat lei tot selektiewe aanpassing.
Foutparametervariasies is nie eenvormig in tyd of ruimte nie. ân Statiese alar-
mdrempel sal hoĂ« vals deteksies of gemiste deteksies veroorsaak as dit onderskei-delik Ăłf te laag Ăłf te hoog gestel is. ân Nuwe aanpassingsdrempel wat gebaseer is
op die genormaliseerde innovasie kwadraat (NIS) word voorgestel. ân Monte Carlo
simulasieveldtog is uitgevoer om die FDI te toets met die foutgroottes, die fisiese
parameters van die vliegtuig, en die steurings lukraak gevarieer elk met ân duide-
like gemiddelde verspreiding. Die voorgestelde strategie vertoon ân hoĂ« robuus-
theid vir geraas en sensitiwiteit vir foute, wat dui op ân betroubare FDI
Agent-Based Lost Person Movement Modelling, Prediction and Search in Wilderness
In this research we investigate the problem of searching for a Lost Person (LP) in wilderness using an autonomous Unmanned Aerial Vehicle (UAV). The problem of search with a UAV is often treated as gridded environment search where the state of each grid (cell) is examined individually for the presence or absence of the target. However, this idealised way of search fails to exploit many potentially valuable dependencies and secondary cues â such as material deposited or left by the LP or topographical features such as natural tracks (trails) â which could significantly aid the search process. We discuss the need for such a system and review the current state-of-the-art work. Since key to a quick and successful search is a well defined initial distributions. We further argue the need to generate the initial distribution over the trajectory of the LP, not merely the end location, usually done in literature. We propose a search framework consisting of three key phases: information gathering, initial distribution generation and search. In the information gathering phase, we collect detailed information related to both the LP and the search environment. Then in the initial distribution generation phase, using the information gathered, we generate distribution over the LPâs trajectory using particles. Each particle represented by an agent model of LP movement with sampled parameters, navigating and interacting with the environment represented using data-sets in the form of terrain elevation, topography and vegetation. To ensure, the agent model is a good representation of the LP behaviour, we calibrate its parameters using the method called SMC2 . Finally in the Search phase, a UAV is deployed to explore the search area and detect the LP, any evidence features or changes in the environment. All information detected are localised and used to update the distribution over the LP trail until either the LP is located or the search is terminated
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