Main topic of this thesis is creation of platform for testing stabilization and control algorithms for UAV. For chosen suitable model plane was designed a structure of control and power electronics. Research of suitable algorithms was made and some of them were implemented. For this algorithms gains were designed, using simulation. The whole system was tested and validated in flight.

Over the last few decades, many factors, including increased electrification, have led to a critical need for fast and efficient transient cooling. Thermal management systems (TMSs) are typically designed using steady-state assumptions and to accommodate the most extreme operating conditions that could be encountered, such as maximum expected heat loads. Unfortunately, by designing systems in this manner, closed-loop transient performance is neglected and often constrained. If not constrained, conventional design approaches result in oversized systems that are less efficient under nominal operation. Therefore, it is imperative that \emph{transient} component modeling and subsystem interactions be considered at the design stage to avoid costly future redesigns. Simply put, as technological advances create the need for rapid transient cooling, a new design paradigm is needed to realize next generation systems to meet these demands. <br><br>In this thesis, I develop a new design approach for TMSs called hierarchical control co-design (HCCD). More specifically, I develop a HCCD algorithm aimed at optimizing high-fidelity design and control for a TMS across a system hierarchy. This is accomplished in part by integrating system level (SL) CCD with detailed component level (CL) design optimization. The lower-fidelity SL CCD algorithm incorporates feedback control into the design of a TMS to ensure controllability and robust transient response to exogenous disturbances, and the higher-fidelity CL design optimization algorithms provide a way of designing detailed components to achieve the desired performance needed at the SL. Key specifications are passed back and forth between levels of the hierarchy at each iteration to converge on an optimal design that is responsive to desired objectives at each level. The resulting HCCD algorithm permits the design and control of a TMS that is not only optimized for steady-state efficiency, but that can be designed for robustness to transient disturbances while achieving said disturbance rejection with minimal compromise to system efficiency. Several case studies are used to demonstrate the utility of the algorithm in designing systems with different objectives. Additionally, high-fidelity thermal modeling software is used to validate a solution to the proposed model-based design process. <br>

<p> Motivated by the increasing need to monitor safety-critical systems subject to uncer-<br> tainties, a novel set-membership approach is proposed to estimate the state of a dynamical<br> system with unknown-but-bounded exogenous inputs. By fully utilizing the system struc-<br> tural information, the proposed algorithm can address both computational efficiency and<br> estimation accuracy without requiring restrictive conditions on the system. Particularly,<br> the system is first decomposed into the strongly observable subsystem and the weakly un-<br> observable subsystem. To make full use of the subsystem’s properties, a set-membership<br> observer based on the unknown input observer and an ellipsoidal set-membership observer<br> are designed for the two subsystems, respectively. Then, the resulting set estimates from<br> each subsystem are fused and transformed to obtain the set estimate for the original system,<br> which is guaranteed to bound the actual system state. The conditions for the boundedness<br> of the proposed set estimate are discussed, and the proposed set-membership observer is also<br> tested numerically using illustrative examples.</p>

<p> Within recent years, there has been a renewed interest in developing data-driven learning based algorithms for solving longstanding challenging control problems. This interest is primarily motivated by the availability of ubiquitous data and an increase in computational resources of modern machines.  However, there is a prevailing concern on the lack of provable performance guarantees on data-driven/model-free learning based control algorithms. This dissertation focuses the following key aspects: i) with what facility can state-of-the-art learning-based control methods eke out successful performance for challenging flight control applications such as aerobatic maneuvering?; and ii) can we leverage well-established tools and techniques in control theory to provide some provable guarantees for different types of learning-based algorithms?  </p> <p>To these ends, a deep RL-based controller is implemented, via high-fidelity simulations, for Fixed-Wing aerobatic maneuvering. which shows the facility with which learning-control methods can eke out successful performances and further encourages the development of learning-based control algorithms with an eye towards providing provable guarantees.<br> </p> <p>Two learning-based algorithms are also developed: i) a model-free algorithm which learns a stabilizing optimal control policy for the bilinear biquadratic regulator (BBR) which solves the regulator problem with a biquadratic performance index given an unknown bilinear system; and ii) a model-free inverse reinforcement learning algorithm, called the Model-Free Stochastic inverse LQR (iLQR) algorithm, which solves a well-posed semidefinite programming optimization problem to obtain unique solutions on the linear control gain and the parameters of the quadratic performance index given zero-mean noisy optimal trajectories generated by a linear time-invariant dynamical system. Theoretical analysis and numerical results are provided to validate the effectiveness of all proposed algorithms.</p>

La prochaine génération d’avions civil sera probablement une révolution en termede configuration d’avion, différant largement de l’architecture désormais classique “fuselage- ailes- moteurs sous voilure”. Du point de vue des qualités de vol, la tendance actuelle est d’évoluer versdes avions de moins en moins stables, à la fois en longitudinal et latéral. Il est dès lors probableque les futurs avions ne seront pas directement contrôlables par un humain sans l’apport de lois decommande stabilisantes. Il devient alors nécessaire de considérer l’apport des systèmes de commandesde vol très tôt dans la conception de l’avion, notamment pour le dimensionnement desempennages, gouvernes et actionneurs, contrairement au processus actuel qui ne prend principalementen compte que des critères “boucle ouverte” d’équilibre en phase de conception préliminaire.Plutôt qu’un processus itératif de dimensionnement puis synthèse de lois de commande, nousproposons d’optimiser simultanément les tailles de gouvernes, actionneurs et commandes de volen tenant compte des instabilités longitudinales et latérales, ainsi que des contraintes industriellessur la structure de correcteurs, sur un cas d’application de type aile volante. Ce processus de“co-design” permet de dimensionner des paramètres physiques de l’avion en tenant compte desapports d’une boucle de retour pour contrer des perturbations externes telles que de la turbulenceatmosphérique, permettant un avion plus sûr et optimal. / Next generation of civil transport aircraft is likely to be a radical change in overallconfiguration compared to traditional tube-and-wing design. From a handling qualities perspective,current trend in modern airliners is to evolve towards more and more unstable aircraft, bothfrom longitudinal and lateral-directional point of view. As a consequence future aircraft may notbe controllable by human operator without stabilizing control laws. It then becomes necessaryto consider flight control systems contribution early in the design phase for control surfaces,empennages and actuators sizing, as opposed to traditional way of working dealing only withopen-loop criteria for preliminary sizing. Instead of an iterative process of sizing and controllaws synthesis, we propose to concurrently optimize control surfaces, actuators and flight controllaws taking into account longitudinal and lateral instability as well as industrial structure forcontrollers, for unstable configurations such as Blended Wing-Body (BWB). This “co-design”procedure enables sizing of physical aircraft parameters taking into account benefits from feedbackstabilization for counteracting external disturbance such as atmospheric turbulence, thus leadingto safer and more optimal aircraft configurations.

Model Reference Adaptive Control (MRAC) is a widely studied adaptive control methodology that aims to ensure that a nonlinear plant with significant modeling uncertainty behaves like a chosen reference model. MRAC methods attempt to achieve this by representing the modeling uncertainty as a weighted combination of known nonlinear functions, and using a weight update law that ensures weights take on values such that the effect of the uncertainty is mitigated. If the adaptive weights do arrive at an ideal value that best represent the uncertainty, significant performance and robustness gains can be realized. However, most MRAC adaptive laws use only instantaneous data for adaptation and can only guarantee that the weights arrive at these ideal values if and only if the plant states are Persistently Exciting (PE). The condition on PE reference input is restrictive and often infeasible to implement or monitor online. Consequently, parameter convergence cannot be guaranteed in practice for many adaptive control applications. Hence it is often observed that traditional adaptive controllers do not exhibit long-term-learning and global uncertainty parametrization. That is, they exhibit little performance gain even when the system tracks a repeated command. This thesis presents a novel approach to adaptive control that relies on using current and recorded data concurrently for adaptation. The thesis shows that for a concurrent learning adaptive controller, a verifiable condition on the linear independence of the recorded data is sufficient to guarantee that weights arrive at their ideal values even when the system states are not PE. The thesis also shows that the same condition can guarantee exponential tracking error and weight error convergence to zero, thereby allowing the adaptive controller to recover the desired transient response and robustness properties of the chosen reference models and to exhibit long-term-learning. This condition is found to be less restrictive and easier to verify online than the condition on persistently exciting exogenous input required by traditional adaptive laws that use only instantaneous data for adaptation. The concept is explored for several adaptive control architectures, including neuro-adaptive flight control, where a neural network is used as the adaptive element. The performance gains are justified theoretically using Lyapunov based arguments, and demonstrated experimentally through flight-testing on Unmanned Aerial Systems.

No description available.

<p> This work focuses on solving the general optimal control problems with smart-learning-enabled and theory-supported optimal control (SET-OC) approaches. The proposed SET-OC includes two main directions. Firstly, according to the basic idea of the direct method, the smart-learning-enabled iterative optimization algorithm (SEIOA) is proposed for solving discrete optimal control problems. Via discretization and reformulation, the optimal control problem is converted into a general quadratically constrained quadratic programming (QCQP) problem. Then, the SEIOA is applied to solving QCQPs. To be specific, first, a structure-exploiting decomposition scheme is introduced to reduce the complexity of the original problem. Next, an iterative search, combined with an intersection-cutting plane, is developed to achieve global convergence. Furthermore, considering the implicit relationship between the algorithmic parameters and the convergence rate of the iterative search, deep learning is applied to design the algorithmic parameters from an appropriate amount of training data to improve convergence property. To demonstrate the effectiveness and improved computational performance of the proposed SEIOA, the developed algorithms have been implemented in extensive real-world application problems, including unmanned aerial vehicle path planning problems and general QCQP problems. According to the theoretical analysis of global convergence and the simulation results, the efficiency, robustness, and improved convergence rate of the optimization framework compared to the state-of-the-art optimization methods for solving general QCQP problems are analyzed and verified. Secondly, the onboard learning-based optimal control method (L-OCM) is proposed to solve the optimal control problems. Supported by the optimal control theory, the necessary conditions of optimality for optimal control of the optimal control problem can be derived, which leads to two two-point-boundary-value-problems (TPBVPs). Then, critical parameters are identified to approximate the complete solutions of the TPBVPs. To find the implicit relationship between the initial states and these critical parameters, deep neural networks are constructed to learn the values of these critical parameters in real-time with training data obtained from the offline solutions.  To demonstrate the effectiveness and improved computational performance of the proposed L-OCM approaches, the developed algorithms have been implemented in extensive real-world application problems, including two-dimensional human-Mars entry, powered-descent, landing guidance problems, and fuel-optimal powered descent guidance (PDG) problems. In addition, considering there is no thorough analysis of the properties of the optimal control profile for PDG when considering the state constraints, a rigid theoretical analysis of the fuel-optimal PDG problem with state constraints is further provided. According to the theoretical analysis and simulation results, the optimality, robustness, and real-time performance of the proposed L-OCM are analyzed and verified, which indicates the potential for onboard implementation. </p>

<p>Cyber-physical systems (CPS) are a class of complicated systems integrating cyber components with physical components. Although such a cyber-physical interaction improves the system performance and intelligence, it increases the system complexity and makes the system vulnerable to various types of faults, failures, and cyber-attacks. To assure the security and improve the resiliency of CPS, it is found that the hybrid system model can be a powerful tool in the domain of fault detection and isolation, cyber-attack diagnosis and containment, as well as resilient control and reconfiguration. Several problems are concerned in this dissertation. For situational awareness, \textit{mode discernibility}, which stands for whether the discrete state of a hybrid system can be correctly identified, is characterized and discussed with potential applications to monitoring system design. For CPS vulnerability analysis, the problem of stealthy attack design for systems with switching structures is investigated, which is motivated by the recent literature. To further understand and remedy for the vulnerabilities, the detectability and identifiability for severe cyber-attacks are defined and characterized, which are followed by the discussions on the methodologies for cyber-attack detection and identification. Last but not least, based on the understanding of identifiability, a framework of resilient control design is proposed to mitigate the impact of cyber-attacks, which can be generalized in future to account for additional design criteria.</p>

<p>The use of lower airspace for air taxi and cargo applications opens up exciting prospects for futuristic Unmanned Aircraft Systems (UAS). However, ensuring the safety and security of these UAS within densely populated urban areas presents significant challenges. Most modern aircraft systems, whether unmanned or otherwise, rely on the Global Navigation Satellite System (GNSS) as a primary sensor for navigation. From satellite navigations point of view, the dense urban environment compromises positioning accuracy due to signal interference, multipath effects, etc. Furthermore, civilian GNSS receivers are susceptible to spoofing attacks since they lack encryption capabilities. Therefore, in this thesis, we focus on examining the safety and cybersecurity assurance of UAS in dense urban environments, from both theoretical and experimental perspectives. </p> <p>To facilitate the verification and validation of the UAS, the first part of the thesis focuses on the development of a realistic GNSS sensor emulation using a Gazebo plugin. This plugin is designed to replicate the complex behavior of the GNSS sensor in urban settings, such as multipath reflections, signal blockages, etc. By leveraging the 3D models of the urban environments and the ray-tracing algorithm, the plugin predicts the spatial and temporal patterns of GNSS signals in densely populated urban environments. The efficacy of the plugin is demonstrated for various scenarios including routing, path planning, and UAS cybersecurity. </p> <p>Subsequently, a robust state estimation algorithm for dynamical systems whose states can be represented by Lie Groups (e.g., rigid body motion) is presented. Lie groups provide powerful tools to analyze the complex behavior of non-linear dynamical systems by leveraging their geometrical properties. The algorithm is designed for time-varying uncertainties in both the state dynamics and the measurements using the log-linear property of the Lie groups. When unknown disturbances are present (such as GNSS spoofing, and multipath effects), the log-linearization of the non-linear estimation error dynamics results in a non-linear evolution of the linear error dynamics. The sufficient conditions under which this non-linear evolution of estimation error is bounded are derived, and Lyapunov stability theory is employed to design a robust filter in the presence of an unknown-but-bounded disturbance. </p>