Sensor-based formation control using a generalised rigidity framework and passivity techniques

The research in this thesis addresses the subject of sensor-based formation control for a network of autonomous agents. The task of formation control involves the stabilisation of the agents to a desired set of relative states, with the possible additional objective of manoeuvring the agents while maintaining this formation. Although the formation control challenge has been widely studied in the literature, many existing control strategies are based on full state information, and give little consideration to the sensor modalities available for the task. The focus of this thesis lies in the use of a generic arrangement of partial state measurements as can commonly be acquired by onboard sensors; for example, time-of-flight sensors can be used to measure the distances between vehicles, and onboard cameras can provide the bearing from one vehicle to each of the others. Particular aspects of the problem that are addressed in this thesis include (i) ways of modelling the formation control task, (ii) methods of analysing the system's behaviour, and (iii) the design of a formation control scheme based on generic arrangements of sensors that provide only partial position information. A key contribution in this thesis is a generalisation of the classical notion of rigidity, which considers the use of distance constraints between agents in R^2 or R^3 to specify a rigid body (or formation). This enables the concept of rigidity to be applied to agent networks involving a variety of (possibly non-Euclidean) state-spaces, with a generic set of state constraints that may, for example, include bearings between agents as well as distances. I demonstrate that this framework is very well-suited for modelling a wide variety of formation control problems (addressing goal (i) above), and I extend several fundamental results from classical rigidity theory in order to provide significant insight for system analysis (addressing goal (ii) above). To design a formation control scheme that uses generic partial position measurements (addressing goal (iii) above), I employ a modular passivity-based approach that is developed using the bondgraph modelling formalism. I illustrate how adaptive compensation can be incorporated into this design approach in order to account for the unknown position information that is not available from the onboard sensors. Although formation control is the subject of this thesis, it should be noted that the rigidity-based and passivity-based frameworks developed here are quite general and may be applied to a wide range of other problems

Component-based modeling of PEM fuel cells with bond graphs

A polymer electrolyte membrane (PEM) fuel cell is a power generation device that transforms chemical energy contained within hydrogen and oxygen gases into useful electricity. The performance of a PEMFC unit is governed by three interdependent physical phenomena: heat, mass, and charge transfer. When modelling such a multi-physical system it is advantageous to use an approach capable of representing all the processes in a unified fashion. This paper presents a component-based model of PEMFCs developed using the bond graph (BG) technique in Modelica language. The basics of the BG method are outlined and a number of relevant publications are reviewed. Model assumptions and necessary equations for each fuel cell component are outlined. The overall model is constructed from a set of bond-graphic blocks within thermal, pneumatic and electrical domains. The model output was compared with the experimental data gathered from a two-cell stack and demonstrated a good accuracy in predicting system behaviour. In the future the designed model will be used for fuel cell reliability studies

A distributed optimization framework for localization and formation control: applications to vision-based measurements

Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures

A digital twin development framework for fatigue failure prognosis of a vertical oil well drill string

This thesis presents a novel methodology for fatigue life prognosis of vertical oil well drill strings through the development of a digital twin frame work. A technique is proposed to classify vibration types with their severities and estimate the remaining useful life time of the drill string based on various indirect measurements made at the surface level. The classification was done using a machine learning algorithm developed based on a Hidden Markov Model HMM). Training data for the algorithm were generated using a bond graph simulation of a vertical drill string. A three-dimensional lumped segment bond graph element and an interface element available in the literature were used to develop the simulation. The bond graph elements are developed based on a Newton-Eular formulation and body-fixed coordinates. The simulation was upgraded by introducing a fluid drag model and refining it with accurate element compliance values. Non linear fluid drag force statistical models were developed through the design of experiments(DoE) approach considering the non-linear geometry of the drill pipes,the drilling fluid rheology, and fluid velocity. A series of fluid-structure interaction(FSI) simulations were employed to develop the statistical models for the lateral vibration damping and the axial drag force dueto the drilling fluid flow through the pipe and the annular space. An apparatus was designed and fabricated to verify the FSI simulation. Further, a method was introduced to accurately determine the axial, shear, bending, and torsional compliances of geometrically-complex drill string segments represented by the bond graph elements. The trained HMM-based classifier using bond graph-generated training data selects the appropriate parameter set for the same bond graph to generate stress history for fatigue life prognosis. A generalized fatigue life estimation method was developed using SalomeMecaᵀᴹ, an open-source finite element analysis code. A detailed workflow for multi-axial, non-proportional, and variable amplitude (MNV) fatigue analysisis also provided. Three case studies are presented to demonstrate the significance of the nonlinear fluid drag models, the fatigue prognosis framework, and the digital twin development framework. In the first case study, the bond graph with the developed drag models showed higher stress fluctuations at the drill pipe threaded connection than the one with a static model. The second case study demonstrated the function of the proposed fatigue life prognosis framework as an optimization tool. In the case study, the optimum placement of the stabilizers reduced the drill collar damage by 66% compared to the worst-case scenario. The third case study used a laboratory-scale vertical drill string vibration simulator apparatus designed and fabricated to implement the framework as a proof of concept. It demonstrated the potential to use surface measurements to classify the vibration type and its severity for fatigue life prognosis

Ion transport modeling for retinal rod photoreceptor cells

"July 2010.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Jinglu Tan.In this study, a mathematical model is developed to describe the ion transport activities associated with the response of rod photoreceptor to light stimulus. In the model, the cell body is modeled as two capacitors connected via the connecting cilium. Roles of different ion channels during a photoreceptor light response are analyzed, and the relations between changes in ion concentration and response currents are assessed. Methods are developed for computing the membrane potential from ion concentrations and relating the material and electrical resistances. The steady state under different conditions can be uniquely defined with only three measured values. The model can effectively describe the rod photoreceptor response to different light stimuli. Model simulation of the a-wave for progressive narrowing of the connecting cilium corresponds well with published literature on hereditary retinal degeneration of Abyssinian cats. Reductions in amplitude and changes in the a-wave waveform are observed in different stages of the disease. Changes in the receptor response amplitude may not be measurable till the conductance of the connecting cilium is reduced to a comparable magnitude of the ion channels. The model can provide quantitative information of ionic activities, changes in ion concentrations and membrane voltage in the outer segment and the inner compartment. The ionic environment is found to be different between the outer segment and the inner compartment. During receptor response, changes in the outer segment appear to be stronger and quicker than those in the inner compartment. Reductions in the connecting cilium transport can reset the dark resting state.Includes bibliographical references (pages 87-91)

Bond graph modelling of exergy in integrated energy systems

Ph. D. Thesis.Integrated municipal or district energy systems are one facet of the effort to support sustainable energy systems that work towards reducing anthropogenic climate change emissions. Current energy systems — including electricity, heat, and cooling — operate mostly independently, under the control of domain-distinct industries and regulatory bodies. Operating these separate systems in a cooperative or integrated manner promises improvements in efficiency, the ability of networks to absorb renewable energy sources and storage, emissions reductions and community-based benefits. The nature of district energy systems is that they cannot easily be modified or built upon without severe disruption to the communities they serve, so assessments of their behaviour and performance caused by potential changes must be modelled. This thesis investigates what methods can model integrated energy systems and develops a bond graph-based approach to constructing a fully-integrated system model. Although energy based methods for integrated energy system modelling exist, this thesis demonstrates that exergy can form the basis of integrated energy system models. Exergy being a measure of the usefulness of energy allows the equivalence of energy domains in a single model form, permitting development of a genuine, physically-founded integrated energy system model. An integrated model of a residential district supplied by heat and electrical networks, based on a real UK urban area, is demonstrated in OpenModelica using the developed modelling approach. The concept of an exergy storage device is introduced to provide a mechanism for mediating energy flows between the networks. The model is used to evaluate the performance of the test network, using trial cases to investigate how transferring exergy between energy domains through the mediating storage affects the overall system energy and exergy efficiencies. Operational regimes that transfer energy from the electrical to the thermal sub-system using the mediating storage are found to improve the exergy efficiency of the system.Newcastle University, Siemen

Development of adaptive transducer based on biological sensory mechanism

textAn adaptive sensor concept and prototype has been developed based on a sensing element which is analogous to and inspired by the arrangement of outer hair cells and inner hair cells between the basilar membrane and tectorial membrane which form the organ of corti in mammalian cochlea. The bio-inspired design was supported by development of a bond graph model of the electromotility (active response) of outer hair cells. Outer hair cells perform like actuators and simulation results using this model are compared with physiological data found in the literature to verify its characteristic response. Insight gained from the model is used to develop a sensor structure analogous to the organ of corti and designed to measure acceleration. A piezoelectric bimorph was selected as the transducer basis, and a bond graph model of the bimorph in an accelerometer configuration was formulated to aid control design and simulation. There is no published data regarding the type of information transmitted among the inner hair cells, outer hair cells, and brain. Consequently, a controller intended to adjust the adaptation process similar to what might exist in the cochlear system has been developed for the sensor and based on a model referenced adaptive control algorithm. Simulations verify that the algorithm can successfully control and enhance performance of the sensor. Practicability of the design is evaluated by a series of experiments on a prototype. This study focused on using a controller structure that was programmed, implemented, and tested using programmable logic based on FPGA technology. The experiments evaluated how well the adaptive sensor could meet a specified performance requirement. Implementation issues that arise, such as the need for differentiators in the adaptive controller or internal propagation of vibration within the sensor structure, hinder the tuning ability. Nevertheless, the trends indicate that the algorithm can meet the desired performance if certain limitations can be overcome. Finally, recommendations have been made for expansion of the research in such fields as an alternative structure for tuning, sensor networking, and reference sensor configuration.Mechanical Engineerin

Failure Diagnosis and Prognosis of Safety Critical Systems: Applications in Aerospace Industries

Many safety-critical systems such as aircraft, space crafts, and large power plants are required to operate in a reliable and efficient working condition without any performance degradation. As a result, fault diagnosis and prognosis (FDP) is a research topic of great interest in these systems. FDP systems attempt to use historical and current data of a system, which are collected from various measurements to detect faults, diagnose the types of possible failures, predict and manage failures in advance. This thesis deals with FDP of safety-critical systems. For this purpose, two critical systems including a multifunctional spoiler (MFS) and hydro-control value system are considered, and some challenging issues from the FDP are investigated. This research work consists of three general directions, i.e., monitoring, failure diagnosis, and prognosis. The proposed FDP methods are based on data-driven and model-based approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the remaining useful life (RUL) of the faulty components accurately and efficiently. In this regard, two dierent methods are developed. A modular FDP method based on a divide and conquer strategy is presented for the MFS system. The modular structure contains three components:1) fault diagnosis unit, 2) failure parameter estimation unit and 3) RUL unit. The fault diagnosis unit identifies types of faults based on an integration of neural network (NN) method and discrete wavelet transform (DWT) technique. Failure parameter estimation unit observes the failure parameter via a distributed neural network. Afterward, the RUL of the system is predicted by an adaptive Bayesian method. In another work, an innovative data-driven FDP method is developed for hydro-control valve systems. The idea is to use redundancy in multi-sensor data information and enhance the performance of the FDP system. Therefore, a combination of a feature selection method and support vector machine (SVM) method is applied to select proper sensors for monitoring of the hydro-valve system and isolate types of fault. Then, adaptive neuro-fuzzy inference systems (ANFIS) method is used to estimate the failure path. Similarly, an online Bayesian algorithm is implemented for forecasting RUL. Model-based methods employ high-delity physics-based model of a system for prognosis task. In this thesis, a novel model-based approach based on an integrated extended Kalman lter (EKF) and Bayesian method is introduced for the MFS system. To monitor the MFS system, a residual estimation method using EKF is performed to capture the progress of the failure. Later, a transformation is utilized to obtain a new measure to estimate the degradation path (DP). Moreover, the recursive Bayesian algorithm is invoked to predict the RUL. Finally, relative accuracy (RA) measure is utilized to assess the performance of the proposed methods

Passive based control on a kuka arm

L'interazione in maniera sicura e compliante è una caratteristica sempre più richiesta per i sistemi robotici. La modellazione di sistemi eseguita tramite l'uso di sistemi port-Hamiltoninani permette di comprendere cosa avviene a livello energetico durante l'interazione e aiuta nella progettazinoe di un controllore tale che il comportamento del sistema controllato sia passivo e sicuro durante essa. Ciò sfocia nel cosiddetto Controllore Intrinsicamente Passivo (IPC). Dal momento che questo un controllo impone la rigidezza desiderata al sistema controllato, è possibile, tra le altre cose, replicare il comportamento del dispositivo RCC (Centro Remoto di Complianza) e di migliorarlo in modo tale che durante l'azione di peg-in-hole il buco sia meno sollecitato dal robot