Active Disturbance Rejection Based Robust Trajectory Tracking Controller Design in State Space

This paper proposes a new Active Disturbance Rejection based robust trajectory tracking controller design method in state space. It can compensate not only matched but also mismatched disturbances. Robust state and control input references are generated in terms of a fictitious design variable, namely differentially flat output, and the estimations of disturbances by using Differential Flatness and Disturbance Observer. Two different robust controller design techniques are proposed by using Brunovsky canonical form and polynomial matrix form approaches. The robust position control problem of a two mass-spring-damper system is studied to verify the proposed robust controllers.Comment: Accepted by ASME Journal of Journal of Dynamic Systems, Measurement, and Control in 201

Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation

Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning

Aeronautical Engineering: A continuing bibliography with indexes (supplement 177)

This bibliography lists 469 reports, articles and other documents introduced into the NASA scientific and technical information system in July 1984

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Application of metaheuristic and deterministic algorithms for aircraft reference trajectory optimization

Aircraft reference trajectory is an alternative method to reduce fuel consumption, thus the pollution released to the atmosphere. Fuel consumption reduction is of special importance for two reasons: first, because the aeronautical industry is responsible of 2% of the CO2 released to the atmosphere, and second, because it will reduce the flight cost. The aircraft fuel model was obtained from a numerical performance database which was created and validated by our industrial partner from flight experimental test data. A new methodology using the numerical database was proposed in this thesis to compute the fuel burn for a given trajectory. Weather parameters such as wind and temperature were taken into account as they have an important effect in fuel burn. The open source model used to obtain the weather forecast was provided by Weather Canada. A combination of linear and bi-linear interpolations allowed finding the required weather data. The search space was modelled using different graphs: one graph was used for mapping the different flight phases such as climb, cruise and descent, and another graph was used for mapping the physical space in which the aircraft would perform its flight. The trajectory was optimized in its vertical reference trajectory using the Beam Search algorithm, and a combination of the Beam Search algorithm with a search space reduction technique. The trajectory was optimized simultaneously for the vertical and lateral reference navigation plans while fulfilling a Required Time of Arrival constraint using three different metaheuristic algorithms: the artificial bee’s colony, and the ant colony optimization. Results were validated using the software FlightSIM®, a commercial Flight Management System, an exhaustive search algorithm, and as flown flights obtained from flightaware®. All algorithms were able to reduce the fuel burn, and the flight costs

Safe planning and control via L1-adaptation and contraction theory

Autonomous robots that are capable of operating safely in the presence of imperfect model knowledge or external disturbances are vital in safety-critical applications. The research presented in this dissertation aims to enable safe planning and control for nonlinear systems with uncertainties using robust adaptive control theory. To this end we develop methods that (i) certify the collision-risk for the planned trajectories of autonomous robots, (ii) ensure guaranteed tracking performance in the presence of uncertainties, and (iii) learn the uncertainties in the model without sacrificing the transient performance guarantees, and (iv) learn incremental stability certificates parameterized as neural networks. In motion planning problems for autonomous robots, such as self-driving cars, the robot must ensure that its planned path is not in close proximity to obstacles in the environment. However, the problem of evaluating the proximity is generally non-convex and serves as a significant computational bottleneck for motion planning algorithms. In this work, we present methods for a general class of absolutely continuous parametric curves to compute: the minimum separating distance, tolerance verification, and collision detection with respect to obstacles in the environment. A planning algorithm is incomplete if the robot is unable to safely track the planned trajectory. We introduce a feedback motion planning approach using contraction theory-based L1-adaptive (CL1) control to certify that planned trajectories of nonlinear systems with matched uncertainties are tracked with desired performance requirements. We present a planner-agnostic framework to design and certify invariant tubes around planned trajectories that the robot is always guaranteed to remain inside. By leveraging recent results in contraction analysis and L1-adaptive control we present an architecture that induces invariant tubes for nonlinear systems with state and time-varying uncertainties. Uncertainties caused by large modeling errors will significantly hinder the performance of any autonomous system. We adapt the CL1 framework to safely learn the uncertainties while simultaneously providing high-probability bounds on the tracking behavior. Any available data is incorporated into Gaussian process (GP) models of the uncertainties while the error in the learned model is quantified and handled by the CL1 controller to ensure that control objectives are met safely. As learning improves, so does the overall tracking performance of the system. This way, the safe operation of the system is always guaranteed, even during the learning transients. The tracking performance guarantees for nonlinear systems rely on the existence of incremental stability certificates that are prohibitively difficult to search for. We leverage the function approximation capabilities of deep neural networks for learning the certificates and the associated control policies jointly. The incremental stability properties of the closed-loop system are verified using interval arithmetic. The domain of the system is iteratively refined into a collection of intervals that certify the satisfaction of the stability properties over the interval regions. Thus, we avoid entirely rejecting the learned certificates and control policies just because they violate the stability properties in certain parts of the domain. We provide numerical experimentation on an inverted pendulum to validate our proposed methodology