Quand des facteurs humains interviennent dans une large majorité des accidents de la route, l’amélioration de la sécurité routière passe par l’introduction de systèmes d’assistance, afin d’aider le conducteur dans les tâches de conduites les plus complexes. Les systèmes de conduites « by-wire », en facilitant le partage des tâches de conduites entre le conducteur et les systèmes d’assistance, représentent une avancée majeure vers une automatisation progressive de la conduite.Cependant, le déploiement de ces systèmes est freiné pour des questions de sûreté de fonctionnement, et nécessite la mise en œuvre d’outils de diagnostique pour détecter et corriger d'éventuelles défaillances. Dans le cadre de cette thèse, nous évaluons un algorithme de détection et de localisation des fautes compatible avec une architecture « by-wire », basé sur l’approche multi-modèles interagissants. Cette méthode nécessite l'estimation probabiliste de l’état du véhicule, pour laquelle différents observateurs non linéaires sont comparés. Pour cette démarche, l’accent est mis sur la validation expérimentale des résultats qui a nécessité l’équipement d’un véhicule de test.Une fois que la faute est localisée, nous étudions les différentes stratégies de contrôle du véhicule en fonction des actionneurs encore disponibles.Cette étude montre que les effets d'une défaillance sur les directions découplées sont particulièrement difficiles à corriger. / The improvement of the road safety implies to increase the place of driving assistance systems for road vehicles. Paving the road of the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms to detect occurring faults. In a first step, we evaluate a fault detection and isolation algorithm, based on the interacting multiple models approach. The method relies on a probabilistic estimation of the vehicle state, for which different non-linear observer schemes are compared. The experimental validation required the preparation of a test vehicle.Then, when a fault is identified, the optimal back-up control strategies are investigated according to the availability of actuators.Thus study points out that faults on steer-by-wire systems are particularly difficult to treat.

Machine tools are essential components of modern manufacturing. They are com posed of various mechanical, hydraulic, and electrical systems such as the spindle, tool changer, cooling system, and the linear and rotary feed drives. Due to their com plexity, high cost, and importance to the manufacturing process it is recommended to implement some sort of condition monitoring and predictive maintenance to ensure that they remain reliable and high performing. One way of potentially implement ing predictive maintenance and condition monitoring is digital twins. Digital twins enable the real-time, accurate, and complex modeling and monitoring of mechanical systems. They utilize data collected from the system to constantly update their mod els which can be used for monitoring of the systems state and future predictions. This work presents a digital twin workbench of a machine tool feed drive. The workbench enables the collection and analysis of large, varied, high-frequency data which can be used to construct a digital twin of the feed drive. A digital twin can enable many other useful functionalities. Some of these functionalities include condition moni toring, modeling, control, visualization, and simulation. These functionalities can enable maximum asset performance and are key in implementing effective predictive maintenance. The main contributions of this work are the following: The design and iv construction of a machine tool feed drive which implements a novel external distur bance force method. A new method of fault detection in ball screws using interacting multiple models which was shown to provide accurate estimates of levels of preloads in a ball screw driven feed drive. A digital twin based modeling strategy and analysis of the data generated by the system including system modeling and observations on modeling difficulties. / Thesis / Master of Applied Science (MASc) / Digital twins enable the real-time, accurate, and complex modeling and monitoring of mechanical systems. Machine tools are essential components of modern manufac turing. They are composed of various mechanical, hydraulic, and electrical systems such as the spindle, tool changer, cooling system, and linear and rotary feed drives. This work presents the design of a workbench of a machine tool linear feed drive, a fault detection strategy, and a digital twin modeling solution. The workbench enables the collection and analysis of large, varied, high-frequency data which can be used to construct a digital twin of the feed drive. A digital twin can enable many other useful functionalities. Some of these functionalities include condition monitoring, modeling, control, visualization, and simulation. These functionalities can enable maximum asset performance and are key in implementing effective predictive maintenance.

To develop and maintain a modern combat aircraft it is important to have simple, yet accurate, threat models to support early stages of functional development. Therefore this thesis develops and evaluates a model of an active radio frequency (RF) seeker for a missile with data-link capability. The highly parametrized MATLAB-model consists of a pulse level radar model, a tracker using either interacting multiple models (IMM) or particle filters, and a guidance law. Monte Carlo simulations with the missile model indicate that, under the given conditions, the missile performs well (hit rate>99%) with both filter types, and the model is relatively insensitive to lost data-link transmissions. It is therefore under normal conditions not worthwhile to use the more computer intense particle filter today, however when the data-link degrades the particle filter performs considerably better than the IMM filter. Analysis also indicate that the measurements generated by the radar model are neither independent, white nor Gaussian. This contradicts the assumptions made in this, and many other radar applications. However, the performance of the model suggests that the assumptions are acceptable approximations of actual conditions, but further studies within this are recommended to verify this.

In this thesis, a new guidance method for fighter aircrafts and a new guidance method for missiles are developed. Also, guidance and control systems of the aircraft and the missile used are designed to simulate the generic engagement scenarios between the missile and the aircraft. Suggested methods have been tested under excessive simulation studies. The aircraft guidance method developed here is a rule based missile evasion method. The main idea to develop this method stems from the maximization of the miss distance for an engagement scenario between a missile and an aircraft. To do this, an optimal control problem with state and input dependent inequality constraints is solved and the solution method is applied on different problems that represent generic scenarios. Then, the solutions of the optimal control problems are used to extract rules. Finally, a method that uses the interpolation of the extracted rules is given to guide the aircraft. The new guidance method developed for missiles is formulated by modifying the classical proportional navigation guidance method using the position estimates. The position estimation is obtained by utilization of a Kalman based filtering method, called interacting multiple models.

<p>To develop and maintain a modern combat aircraft it is important to have simple, yet accurate, threat models to support early stages of functional development. Therefore this thesis develops and evaluates a model of an active radio frequency (RF) seeker for a missile with data-link capability. The highly parametrized MATLAB-model consists of a pulse level radar model, a tracker using either interacting multiple models (IMM) or particle filters, and a guidance law. </p><p>Monte Carlo simulations with the missile model indicate that, under the given conditions, the missile performs well (hit rate>99%) with both filter types, and the model is relatively insensitive to lost data-link transmissions. It is therefore under normal conditions not worthwhile to use the more computer intense particle filter today, however when the data-link degrades the particle filter performs considerably better than the IMM filter. Analysis also indicate that the measurements generated by the radar model are neither independent, white nor Gaussian. This contradicts the assumptions made in this, and many other radar applications. However, the performance of the model suggests that the assumptions are acceptable approximations of actual conditions, but further studies within this are recommended to verify this.</p>

Electric vehicles have received substantial attention in the past few years since they provide a more sustainable, efficient, and greener transportation alternative in comparison to conventional fossil-fuel powered vehicles. Lithium-Ion batteries represent the most important component in the electric vehicle powertrain and thus require accurate monitoring and control. Many challenges are still facing the mass market production of electric vehicles; these challenges include battery cost, range anxiety, safety, and reliability. These challenges can be significantly mitigated by incorporating an efficient battery management system. The battery management system is responsible for estimating, in real-time, the battery state of charge, state of health, and remaining useful life in addition to communicating with other vehicle components and subsystems. In order for the battery management system to effectively perform these tasks, a high-fidelity battery model along with an accurate, robust estimation strategy must work collaboratively at various power demands, temperatures, and states of life. Lithium ion batteries are considered in this research. For these batteries, electrochemical models represent an attractive approach since they are capable of modeling lithium diffusion processes and track changes in lithium concentrations and potentials inside the electrodes and the electrolyte. Therefore, electrochemical models provide a connection to the physical reactions that occur in the battery thus favoured in state of charge and state of health estimation in comparison to other modeling techniques. The research presented in this thesis focuses on advancing the development and implementation of battery models, state of charge, and state of health estimation strategies. Most electrochemical battery models have been verified using simulation data and have rarely been experimentally applied. This is because most electrochemical battery model parameters are considered proprietary information to their manufacturers. In addition, most battery models have not accounted for battery aging and degradation over the lifetime of the vehicle using real-world driving cycles. Therefore, the first major contribution of this research is the formulation of a new battery state of charge parameterization strategy. Using this strategy, a full-set of parameters for a reduced-order electrochemical model can be estimated using real-world driving cycles while accurately calculating the state of charge. The developed electrochemical model-based state of charge parameterization strategy depends on a number of spherical shells (model states) in conjunction with the final value theorem. The final value theorem is applied in order to calculate the initial values of lithium concentrations at various shells of the electrode. Then, this value is used in setting up constraints for the optimizer in order to achieve accurate state of charge estimation. Developed battery models at various battery states of life can be utilized in a real-time battery management system. Based on the developed models, estimation of the battery critical surface charge using a relatively new estimation strategy known as the Smooth Variable Structure Filter has been effectively applied. The technique has been extended to estimate the state of charge for aged batteries in addition to healthy ones. In addition, the thesis introduces a new battery aging model based on electrochemistry. The model is capable of capturing battery degradation by varying the effective electrode volume, open circuit potential-state of charge relationship, diffusion coefficients, and solid-electrolyte interface resistance. Extensive experiments for a range of aging scenarios have been carried out over a period of 12 months to emulate the entire life of the battery. The applications of the proposed parameterization method combined with experimental aging results significantly improve the reduced-order electrochemical model to adapt to various battery states of life. Furthermore, online and offline battery model parameters identification and state of charge estimation at various states of life has been implemented. A technique for tracking changes in the battery OCV-R-RC model parameters as battery ages in addition to estimation of the battery SOC using the relatively new Smooth Variable Structure Filter is presented. The strategy has been validated at both healthy and aged battery states of life using driving scenarios of an average North-American driver. Furthermore, online estimation of the battery model parameters using square-root recursive least square (SR-RLS) with forgetting factor methodology is conducted. Based on the estimated model parameters, estimation of the battery state of charge using regressed-voltage-based estimation strategy at various states of life is applied. The developed models provide a mechanism for combining the standalone estimation strategy that provide terminal voltage, state of charge, and state of health estimates based on one model to incorporate these different aspects at various battery states of life. Accordingly, a new model-based estimation strategy known as the interacting multiple model (IMM) method has been applied by utilizing multiple models at various states of life. The method is able to improve the state of charge estimation accuracy and stability, when compared with the most commonly used strategy. This research results in a number of novel contributions, and significantly advances the development of robust strategies that can be effectively applied in real-time on-board of a battery management system. / Thesis / Doctor of Philosophy (PhD)