FLIGHT CONTROL RESEARCH LABORATORY UNMANNED AERIAL SYSTEM WIND SHEAR ON-LINE IDENTIFICATION

An algorithm to perform the on line identification of the wind shear components suitable for the UAS characteristics has been implemented. The mathematical model of aircraft and wind shear in the augmented state space has been built without any restrictive assumption on the dynamic of wind shear. Because of the strong velocity variations typical of wind shear induce severe accelerations on the aircraft the wind shear effects have been modeled as external forces and moments applied on the aircraft. The identification problem addressed in this work has been solved by using the Filter error method approach. An Extended Kalman Filter has been developed to propagate state. It has been tuned by using a database of measurements through off-line identification of the process noise covariance matrix. Afterwards the implemented EKF has been employed to estimate onboard either aircraft state or turbulence, with significant savings in terms of time and computing resources. Robustness of implemented algorithm has been verified by means of several tests. The obtained results show the feasibility of the tuned up algorithm. In fact it is possible, by using a few numbers of low cost sensors, to estimate with a noticeable accuracy the augmented state vector. Besides a very short computation time is required to performthe augmented state estimation even by using low computation power

Un algorithme pour la planification de trajectoire basé sur le calcul ensembliste

National audienceNous proposons dans cet article une solution originale et garantie à la "planification de trajectoire" en utilisant une approche ensembliste. Le système est capable de traiter des tâches compliquées dans un environnement encombré d'obstacles, avec pour seule entrée la géométrie de l'e nvironnement, un point de départ et un point d'arrivée. La tâche de planification est résolue en combinant le calcul par intervalle et la théorie des graphes. La méthode est illustrée sur un cas idéalisé dans lequel le robot est choisi circulaire et les obstacles composés de figures géométriques simples

The Performance of Observer-based Residuals for Detecting Intermittent Faults: The Limitations

AbstractIn this paper a broad nonlinear system is considered. Attention is focused upon both performance of a high-gain observer-based residual and the investigation of residual effectiveness for detecting faults in actuators/components. Residual performances for different fault positions and various system complexities are compared. Both qualitative and quantitative evidence for selected fault positions indicated the performance and the effectiveness of the residuals decrease by ascending the system complexity. The poor performance of residuals in the more complex system may cause No Fault Found (NFF). The methods may be extended to the more general class of nonlinear systems and different observers. Efficiency of the proposed approach is demonstrated through the intermittent failure case in a vehicle suspension system

An adaptive autopilot design for an uninhabited surface vehicle

An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.EPSRC (Engineering and Physical Sciences Research Council

Control predictivo basado en partículas y simulaciones de Montecarlo

El Control Predictivo basado en Modelo es, desde su aparición, una de las estrategias de control avanzado más populares y con mayor relevancia en la industria en la actualidad. Continuamente se publican nuevos resultados y avances, consiguiendo cada vez un mejor rendimiento del controlador o proporcionando garantías que antes no existían. Por esta razón, enfocamos el presente trabajo en el estudio de una posible estrategia de Control Predictivo basado en Métodos de Montecarlo o filtros de partículas, la cual tiene el potencial de hacer frente a diferentes tipos de perturbaciones presentes en los sistemas a controlar. En los algoritmos desarrollados a lo largo del proyecto aparecen los conceptos de partícula y escenario, los cuáles tienen una gran importancia en estas páginas. Cada uno de ellos hace referencia a una característica concreta de los algoritmos y de los problemas a resolver. A modo de resumen: • Las partículas hacen referencia a una solución potencial del problema de optimización. Es decir, se utilizan para resolver un problema matemático con un método alternativo a otros más convencionales como pueden ser los gradenciales. • Los escenarios son una realización hipotética y posible de las perturbaciones durante la operación del sistema. Por tanto, el controlador tiene en cuenta algunos de los escenarios posibles para tomar las decisiones respecto al sistema incierto. Los algoritmos se prueban en distintos sistemas con características diferentes así como a diferentes tipos de perturbación, con el objetivo de evaluar la bondad de estos. En el capítulo 5 se presentan todos los ensayos realizados así como los resultados obtenidos.Universidad de Sevilla. Máster en Ingeniería Industria

A Geometric Approach to Trajectory Planning for Underactuated Mechanical Systems

In the last decade, multi-rotors flying robots had a rapid development in industry and hobbyist communities thanks to the affordable cost and their availability of parts and components. The high number of degrees of freedom and the challenging dynamics of multi-rotors gave rise to new research problems. In particular, we are interested in the development of technologies for an autonomous fly that would al- low using multi-rotors systems to be used in contexts where the presence of humans is denied, for example in post-disaster areas or during search-and-rescue operations. Multi-rotors are an example of a larger category of robots, called \u201cunder-actuated mechanical systems\u201d (UMS) where the number of actuated degrees of freedom (DoF) is less than the number of available DoF. This thesis applies methods com- ing from geometric mechanics to study the under-actuation problem and proposes a novel method, based on the Hamiltonian formalism, to plan a feasible trajectory for UMS. We first show the application of a method called \u201cVariational Constrained System approach\u201d to a cart-pole example. We discovered that it is not possible to extend this method to generic UMS because it is valid only for a sub-class of UMS, called \u201csuper-articulated\u201d mechanical system. To overcome this limitation, we wrote the Hamilton equations of the quad- rotor and we apply a numerical \u201cdi- rect method\u201d to compute a feasible trajectory that satisfies system and endpoint constraints. We found that by including the system energy in the multi-rotor states, we are able to compute maneuvers that cannot be planned with other methods and that overcome the under-actuation constraints. To demonstrate the benefit of the method developed, we built a custom quad- rotor and an experimental setup with different obstacles, such as a gap in a wall and we show the correctness of the trajectory computed with the new method