Development of dynamic model and control techniques for microelectromechanical gyroscopes

In this thesis we investigate the effects of stiffness, damping and temperature on the performance of a MEMS vibratory gyroscope. The stiffness and damping parameters are chosen because they can be appropriately designed to synchronize the drive and sense mode resonance to enhance the sensitivity and stability of MEMS gyroscope. Our results show that increasing the drive axis stiffness from its tuned value by 50%, reduces the sense mode magnitude by ~27% and augments the resonance frequency by ~21%. The stiffness and damping are mildly sensitive to typical variations in operating temperature. The stiffness decreases by 0.30%, while the damping increases by 3.81% from their initial values, when the temperature is raised from -40 to 60C. Doubling the drive mode damping from its tuned value reduces the oscillation magnitude by 10%, but ~0.20% change in the resonance frequency. The predicted effects of stiffness, damping and temperature can be utilized to design a gyroscope for the desired operating condition

Research on parallel nonlinear control system of PD and RBF neural network based on U model

The modelling problem of nonlinear control system is studied, and a higher generality nonlinear U model is established. Based on the nonlinear U model, RBF neural network and PD parallel control algorithm are proposed. The difference between the control input value and the output value of the neural network is taken as the learning target by using the online learning ability of the neural network. The gradient descent method is used to adjust the PD output value, and ultimately track the ideal output. The Newton iterative algorithm is used to complete the transformation of the nonlinear model, and the nonlinear characteristic of the plant is reduced without loss of modelling precision, consequently, the control performance of the system is improved. The simulation results show that RBF neural network and PD parallel control system can control the nonlinear system. Moreover, the control system with Newton iteration can improve the control effect and anti-interference performance of the system

Pose Estimation and Segmentation for Rehabilitation

The global population is getting older and the aging demographic is increasing demands on health-care industry. This will drive the demand for post stroke, joint replacement, and chronic disease management rehabilitation. Currently physiotherapists rely on mostly subjective and observational tools for patient assessment and progress tracking. This thesis proposes methods to enable the use of non-intrusive, small, wearable, wireless sensors to estimate the pose of the lower body during rehabilitation and extract objective performance measures useful for therapists. Two different kinematic models of the human lower body are introduced. The first approach expresses the body position and orientation in the world frame using three prismatic and revolute joints, while the second switches the model's base between the right and the left ankle during gait. An Extended Kalman Filter (EKF) is set up to estimate the joint angles, velocities, and accelerations of the models using measurements from inertial measurement units. The state update model assumes constant joint acceleration and is linear. Measurement prediction, relating the joint positions, velocities and accelerations to the measured angular velocity and linear acceleration at each IMU, is done using forward kinematics, using one of the two proposed kinematic models. The approach is validated on healthy participant gait using motion capture studio data for ground truth comparison. The prismatic and revolute model achieves better Cartesian position accuracy in the swing leg due to a shorter kinematic chain, while the switching base model improves the stance leg Cartesian estimate and does not allow measurement noise to accumulate as drift in global position, knee joint angle root mean squared errors (RMSE) of 6.1 and 5.6 degrees are attained respectively by the models. Next the Rhythmic Extended Kalman Filter (R-EKF) algorithm is developed to improve pose estimation. It learns a model of rhythmic movement over time based on harmonic Fourier series and removes the constant acceleration assumption. The estimated phase and frequency of the motion also allow the proposed approach to segment the motion into repetitions and extract useful features such as gait symmetry, step length, and mean joint movement and variance. The algorithm is shown to outperform the EKF in simulation, on healthy participant data, and stroke patient monthly assessments. For the healthy participant marching dataset, the R-EKF improves joint acceleration and velocity estimates over regular EKF by 40% and 37% respectively, estimates joint angles with 2.4 degree RMSE, and segments the motion into repetitions with 96% accuracy. While the proposed R-EKF effectively segments rhythmic rehabilitation movement such as gait, not all rehabilitation motions are rhythmic or may have uneven delays between repetitions by regimen design or due to fatigue. For such motions a time-series segmentation as data point classification algorithm is proposed. Common dimensionality reduction and classification techniques are applied to estimated joint angle data to classify each time-step as a segment or non-segment point. The algorithm is tested on five common rehabilitation exercises performed by healthy participants and achieves a segmentation accuracy of 82%

Symptom analysis of Parkinson’s disease utilizing machine learning methods

While monitoring Parkinson’s disease progression or observing the everchanging severity stage of the disease, the patients are keeping symptom diaries and making regular visits to the neurologist clinic for evaluation. The diaries are based on patients own memories which tend to be unreliable in addition to the burdensome clinical appointments. Therefore, the research is focused on automatizing the burden with the help of machine learning classifiers. These classifiers are trained to either recognize the current severity stage of a patient or make a prediction about future outcomes, such as the progression rate of the disease or a freezing of gait event. The data on which the classifiers are trained with is gathered via wearable sensors that attain several gait parametrics from different walking tasks or daily activities conducted. This thesis presents several studies conducted during the years of 2020–2023 which aim to develop a machine learning algorithm to classify the correct state of the patient according to the disease stage, or predict medical outcomes before their occurring. Their performance metrics are evaluated, especially regarding their accuracy, sensitivity and specificity results. Additionally, this thesis introduces background of gait analysis and machine learning methods. The changes in gait that Parkinson’s disease inflicts are discussed alongside the clinical criteria used in evaluating the changes and patient’s condition. This thesis is a literature review, which aims to find the best possible machine learning algorithms for symptom analysis of Parkinson’s disease. It concludes that comprehensive conclusions are difficult to draw, since the algorithm performance can be analysed with several different metrics. Even though most of the algorithms gained adequate results, the research still includes several limitations to solve before the algorithm can be validated for clinical use as a symptom monitoring system.Parkinsonin taudin etenemisen seuranta perustuu potilaiden omiin oirepäiväkirjamerkintöihin. Lisäksi taudin jatkuvasti muuttuvaa vakavuusastetta seurataan säännöllisesti neurologin klinikalla. Päiväkirjat perustuvat potilaan omiin muistikuviin, jotka ovat yleensä epäluotettavia ja klinikalla käynti raskasta. Siksi tutkimus keskittyy taakan automatisointiin koneoppimismenetelmien avulla. Nämä algoritmit koulutetaan joko tunnistamaan taudin nykyinen vakavuusaste tai ennustamaan tulevia tuloksia, kuten taudin etenemisnopeutta tai kävelykyvyn jäätymistä. Tietoja, joilla koneoppimisalgoritmeja koulutetaan, kerätään puettavien sensoreiden avulla. Nämä keräävät dataa useista eri kävelyparametreista, jotka saadaan talteen erilaisia kävelytestejä hyödyntäen. Tässä työssä esitellään useita vuosina 2020–2023 tehtyjä tutkimuksia, joiden tarkoituksena on kehittää koneoppimisalgoritmeja, jotka luokittelevat potilaan oikeaan vakavuusastekategoriaan tai ennustavat lääketieteellisiä tuloksia ennen niiden ilmenemistä. Algoritmien suorituskykymittareita arvioidaan erityisesti tarkkuuden, herkkyyden ja spesifisyyden suhteen. Lisäksi työssä taustoitetaan kävelyanalyysin periaatteita, puettavia sensoreita sekä yleisimpiä koneoppimismenetelmiä, joita tutkimukset ovat käyttäneet. Parkinsonin taudin myötä kävelyyn kohdistuvia muutoksia käsitellään ja potilaan tilan arvioinnissa käytettyjä kliinisiä kriteerejä esitellään. Tämä työ on kirjallisuuskatsaus, jonka tavoitteena on löytää parhaat mahdolliset koneoppimisalgoritmit Parkinsonin taudin oireiden analysointiin. Tuloksista voidaan päätellä, että kattavaa johtopäätöstä on vaikea tehdä, koska algoritmien suorituskykyä voidaan analysoida useilla eri mittareilla. Vaikka suurin osa algoritmeista saivatkin onnistuneita tuloksia, tutkimukset sisälsivät silti useita rajoituksia, jotka ovat ratkaistava ennen kuin algoritmi voidaan validoida kliiniseen käyttöön oireiden seurantajärjestelmänä

Design and implementation of a soft computing-based controller for a complex mechanical system

Soft-Computing basierende Regler beinhalten Algorithmen, die im Bereich des Maschinellen Lernens einzuordnen sind. Diese Regler sind in der Lage eine geeignete Steuerungsstrategie durch direkte Interaktion mit einer dynamischen Regelstrecke zu entwerfen. Sowohl klassische als auch moderne Reglerentwurfsmethoden hangen von der Genauigkeit des verwendeten dynamischen Systemmodells ab, was insbesondere bei steigender Komplexitat des Systems und auftretenden Modellunsicherheiten nicht mehr uneingeschrankt gewahrleistet werden kann. Die Ziele von Soft- Computing basierenden Reglern sind die Verbesserung der Gute des Regelverhaltens und eine geeignete Anpassung der Regler ohne eine mathematische Modellbildung auf Grundlage von physikalischen Gesetzen. Im Rahmen dieser Arbeit werden funf Algorithmen zur Modellbildung und Regelung dynamischer Systeme untersucht, welche auf dem Mehrschichten-Perzeptron-Netzwerk (Multi-Layer Perceptron network, MLP), auf der Methode der Support Vector Machine (SVM), der Gau-Prozesse, der radialen Basisfunktionen (Radial Basis Functions, RBF) sowie der Fuzzy-Inferenz-Systeme basieren. Im Anschluss an die Darstellung der zugrunde liegenden mathematischen Zusammenhange dieser Methoden sowie deren Hauptanwendungsfelder im Bereich der Modellbildung und Regelung dynamischer Systeme wird eine systematische Evaluierung der funf Methoden diskutiert. Anhand der Verwendung quantitativer Gutekennziern werden diese Methoden fur die Verwendung in der Modellbildung und Regelung dynamischer Systeme vergleichbar gegenubergestellt. Basierend auf den Ergebnissen der Evaluierung wird der SVM-basierte Algorithmus als Kernalgorithmus des Soft-Computing basierenden Reglers verwendet. Der vorgestellte Regler besteht aus zwei Hauptteilen, wobei der erste Teil aus einer Modellfunktion der dynamischen Regelstrecke und einem SVM-basierten Beobachter besteht, und der zweite Teil basierend auf dem Systemmodell eine geeignete Regelstrategie generiert. Die Verikation des SVM-basierten Regleralgorithmus erfolgt anhand eines FEM-Modells eines dynamischen elastischen Balken bzw. einseitig eingespannten elastischen Balkens. Dieses Modell kann z. B. als Ersatzmodell fur das mechanische Verhalten eines exiblen Roboterarms oder einer Flugzeugtrag ache verwendet werden. Der Hauptteil der Modellfunktion besteht aus einem automatischen Systemidentikationsalgorithmus, der auch die Integration eines systematischen Modellbildungsansatzes fur dynamische Systeme ermoglicht.Die Ergebnisse des SVM-basierten Beobachter zeigen ahnliches Verhalten zum Kalman- Bucy Beobachter. Auch die Sensitivitatsanalyse der Parameter zeigt eine bessere Gute der SVM-basierten Beobachter im Vergleich mit den Kalman-Bucy Beobachtern. Im Anschluss wird der SVM-basierte Regler zur Schwingungsregelung des Kragtragers verwendet. Hierbei werden vergleichbare Ergebnisse zum LQR-Regler erzielt. Eine experimentelle Validierung des SVM basierten Reglers erfolgt an Versuchsst anden eines elastischen Biegebalkens sowie eines invertierten Biegebalkens. Die Zustandsbeobachtung fuhrt zu vergleichbaren Ergebnissen verglichen mit einem Kalman-Bucy Beobachter. Auch die Modellbildung des elastischen Balkens fuhrt zu guten Ubereinstimmungen. Die Regelgute des Soft-Computing basierenden Reglers wurde am Versuchsstand des invertierten Biegebalkens experimentell erprobt. Es wird deutlich, dass Ergebnisse im Rahmen der erforderlichen Vorgaben erzielt werden konnen.The focus of this thesis is to obtain a soft computing-based controller for complex mechanical system. soft computing based controllers are based on machine learning algorithm that able to develop suitable control strategies by direct interaction with targeted dynamic systems. Classical and modern control design methods depend on the accuracy of the system dynamic model which cannot be achieved due to the dynamic system complexity and modeling uncertainties. A soft computing-based controller aims to improve the performance of the close loop system and to give the controller adaptation ability as well as to reduce the need for mathematical modeling based on physical laws. In this work ve dierent softcomputing algorithms used in the eld of modeling and controlling dynamic systems are investigated.These algorithms are Multi-Layer Perceptron(MLP) network, Support Vector Machine (SVM),Gaussian process, Radial Basis Function (RBF), and Fuzzy Inference System (FIS). The basic mathematical description of each algorithm is given. Additionally, the most recent applications in modeling and controlling of dynamic system are summarized. A systematic evaluation of the ve algorithms is proposed. The goal of the evaluation is to provide quantitative measure of the performance of soft computing algorithms when used in modeling and controlling a dynamic system. Based on the evaluation, the SVM algorithm is selected as the core learning algorithm for the soft computing based controller. The controller has two main units. The rst unit has two functions of modeling dynamic system and obtaining a SVM-based observer. The second unit is in charge of generating suitable control strategy based on the dynamic model obtained. The verication of the controller using SVM algorithm is done using an elastic cantilever beam modeled using Finite Element Method (FEM). An elastic cantilever beam can be considered as a representation of exible single-link manipulator or aircraft wing. In the core of the modeling unit, an automatic system identication algorithm which allows a systematic modeling approach of dynamic systems is implemented. The results show that the system dynamic model using SVM algorithm is accurate with respect to the FEM model. As for the SVM-based observer the results show that it has good estimation in comparison with to dierent Kalman-Bucy observers. The sensitivity to parameters variations analysis shows that the SVM-based observer has better performance than Kalman-Bucy observer. The SVM based controller is used to control the vibration of the cantilever beam; the results show that the model reference controller using SVM has a similar performance to LQR controller. The validation of the controller using SVM algorithm is carried out using the elastic cantilever beam test rig and the inverted cantilever beam test rig. The states estimation using SVM-based observer of the elastic cantilever beam test rig is successful and accurate compared to a Kalman-Bucy observer. Modeling of the elastic cantilever beam using the SVM algorithm shows good accuracy. The performance of controller is tested on the inverted cantilever beam test rig. The results show that required performance objective can be realized using this control strategy

Adaptive backstepping control of quadrotors with neural-network

A quadrotor is a type of unmanned aerial vehicles. It has been widely used in aerial photography. The quadrotor has the capability of vertical takeoff and landing, which is very useful in small or narrow areas. The mechanical structure of a quadrotor is also simple, which makes it easy to produce and maintain. It is a strong candidate for a future means of transportation. In practical applications, it is commonly controlled by a proportional integral derivative controller. In this thesis, two nonlinear controllers are designed to control the attitude and the position of a quadrotor by using the backstepping technique. The attitude is estimated by a nonlinear attitude estimator, which is based on a nonlinear explicit complementary filter. It uses data from a six axis inertial measurement unit and a three axis magnetometer to calculate the estimated attitude. To avoid the singularity problem like "gimbal lock" in Euler angle attitude representation, the unit quaternion attitude representation is applied in the controller derivation. However, the Euler angle representation is easier for people to imagine the actual attitude of a quadrotor. To make it more readable, the results of the experiments are converted to the Euler angle representation. During the derivation of a backstepping controller, a neural-network is applied to estimate the nonlinear terms in the system. The universal approximation theorem is the principle for the estimation of nonlinear terms. Besides, a two step controller is derived by modifying the backstepping controller with four steps. The two step controller is developed by an adaptive method for both the nonlinear terms and the moment of inertia. Analysis shows the boundedness of the closed-loop system with both controllers. Finally, the proposed controllers are tested on a true quadrotor system. Experimental results show the effectiveness of the two proposed controllers. Also, comparison between two controllers are carried out. In addition, some future works are discussed

Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

Scalable Control Strategies and a Customizable Swarm Robotic Platform for Boundary Coverage and Collective Transport Tasks

abstract: Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities. To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201

All over the place localization system

The MAP-i Doctoral Programme in Informatics, of the Universities of Minho, Aveiro and PortoA localização é normalmente obtida utilizando um sistema de navegação baseado num ambiente estruturado. No entanto, estes sistemas não funcionam ou são difíceis de serem implantados em ambientes densos. Assim, considerando que as pessoas se deslocam geralmente a pé, neste trabalho é proposto um Sistema de Navigação Inercial para Pedestres (PINS). Nesta tese são identificadas as principais vantagens e desvantagens dos PINS, bem como, os algoritmos que estão na base destes sistemas. O objetivo é fornecer uma perspectiva abrangente sobre o que é necessário para desenvolver um PINS e quais os problemas encontrados mais frequentemente durante o seu desenvolvimento. São também identificados e comparados os sistemas e tecnologias mais importantes da literatura. Duas unidades de medição inercial foram desenvolvidas, sendo que os sensores inerciais foram combinados com sensores de força para melhorar a detecção das diferentes fases (fase de apoio e fase de balanço) da marcha humana, assim como, para ter uma informação mais precisa sobre a força de contacto. É muito importante que a fase de apoio seja devidamente detectada. Assim três diferentes algoritmos, utilizando diferentes sensores e métodos de fusão sensorial, são explicados e avaliados. A marcha humana representa um padrão que é repetido ao longo do tempo, o qual é aprendido utilizando algoritmos de aprendizagem com base nos dados obtidos pelas diferentes fontes de informação para realizar uma caracterização do passo. Esta caracterização leva a uma melhoria no desempenho do sistema, uma vez que os erros sistemáticos podem ser aprendidos, para depois serem corrigidos em tempo real. Como neste sistema existe mais do que uma fonte de informação, além das técnicas de fusão sensorial, são também aplicadas técnicas de fusão de informação. Depois dos dados serem obtidos com o equipamento desenvolvido, e do passo ser caracterizado com os dados aprendidos, são aplicados os algoritmos que fazem a estimativa do deslocamento. A arquitetura proposta é avaliada em quatro cenários de utilização real, dentro de um edifício, envolvendo diferentes tipos de caminhadas. Esta arquitectura levou a uma melhoria significativa da precisão da estimativa do deslocamento.Nowadays location information is typically obtained using a navigation system based on a structured environment. However, these systems do not work or are very difficult to be deployed in dense environments. Thus, considering that persons are usually on foot, in this work is proposed a Pedestrian Inertial Navigation System (PINS). In this thesis are identified the main advantages/disadvantages about PINS, as well as, the algorithms that are the base of this type of systems. It is provided a good insight about what is necessary to create a PINS and the problems that are encountered during its development. To complement these insights the fundamentals about Human Gait are presented, along with the main sensor and information fusion strategies used in this type of system. Also, the most important systems and technologies are identified and compared. Two inertial measurement units were developed, where the inertial sensors were combined with force sensors to improve the detection of different phases (stance and swing phase) of the human gait, as well as, to have proper information about the contact force. The stance phase is very important to be properly detected, therefore, three different algorithms using different sensors and sensor fusion methods are explained and evaluated. The human gait cycle represents a pattern that is a repeatable over time. Thus, this pattern is learned using machine learning algorithms, which are applied to the data obtained from the different data sources to perform a step characterization. This characterization leads to an improvement on the system’s performance, since the systematic errors can be learned to then be corrected in real-time. Since there is more than one source of information, besides sensor fusion techniques, it was also implemented an information fusion strategy. After collecting the data with the developed hardware and characterize the step according to the learned data, it is demonstrated the developed displacement estimation architecture. The proposed architecture and algorithms are evaluated through four real use case scenarios in a typical indoor environment involving different types of walking paths. This architecture led to a significant improvement on the displacement estimation accuracy.This work is funded by the ERDF (European Regional Development Fund) through the COMPETE Programme and by the Portuguese Government through the FCT (Portuguese Foundation for Science and Technology) within the doctoral grant SFRH/BD/70248/2010