Autonomous take-off and landing of a tethered aircraft: a simulation study

The problem of autonomous launch and landing of a tethered rigid aircraft for airborne wind energy generation is addressed. The system operates with ground-based power conversion and pumping cycles, where the tether is repeatedly reeled in and out of a winch installed on the ground and linked to an electric motor/generator. In order to accelerate the aircraft to take-off speed, the ground station is augmented with a linear motion system composed by a slide translating on rails and controlled by a second motor. An onboard propeller is used to sustain the forward velocity during the ascend of the aircraft. During landing, a slight tension on the line is kept, while the onboard control surfaces are used to align the aircraft with the rails and to land again on them. A model-based, decentralized control approach is proposed, capable to carry out a full cycle of launch, low-tension flight, and landing again on the rails. The derived controller is tested via numerical simulations with a realistic dynamical model of the system, in presence of different wind speeds and turbulence, and its performance in terms of landing accuracy is assessed. This study is part of a project aimed to experimentally verify the launch and landing approach on a small-scale prototype.Comment: This is the longer version of a paper submitted to the 2016 American Control Conference 2016, with more details on the simulation parameter

High-Performance Small-Scale Solvers for Moving Horizon Estimation

In this paper we present a moving horizon estimation (MHE) formulation suitable to easily describe the quadratic programs (QPs) arising in constrained and nonlinear MHE. We propose algorithms for factorization and solution of the underlying Karush-Kuhn-Tucker (KKT) system, as well as the efficient implementation techniques focusing on small-scale problems. The proposed MHE solver is implemented using custom linear algebra routines and is compared against implementations using BLAS libraries. Additionally, the MHE solver is interfaced to a code generation tool for nonlinear model predictive control (NMPC) and nonlinear MHE (NMHE). On an example problem with 33 states, 6 inputs and 15 estimation intervals execution times below 500 microseconds are reported for the QP underlying the NMHE. 1

Optimization based solutions for control and state estimation in non-holonomic mobile robots: stability, distributed control, and relative localization

Interest in designing, manufacturing, and using autonomous robots has been rapidly growing during the most recent decade. The main motivation for this interest is the wide range of potential applications these autonomous systems can serve in. The applications include, but are not limited to, area coverage, patrolling missions, perimeter surveillance, search and rescue missions, and situational awareness. In this thesis, the area of control and state estimation in non-holonomic mobile robots is tackled. Herein, optimization based solutions for control and state estimation are designed, analyzed, and implemented to such systems. One of the main motivations for considering such solutions is their ability of handling constrained and nonlinear systems such as non-holonomic mobile robots. Moreover, the recent developments in dynamic optimization algorithms as well as in computer processing facilitated the real-time implementation of such optimization based methods in embedded computer systems. Two control problems of a single non-holonomic mobile robot are considered first; these control problems are point stabilization (regulation) and path-following. Here, a model predictive control (MPC) scheme is used to fulfill these control tasks. More precisely, a special class of MPC is considered in which terminal constraints and costs are avoided. Such constraints and costs are traditionally used in the literature to guarantee the asymptotic stability of the closed loop system. In contrast, we use a recently developed stability criterion in which the closed loop asymptotic stability can be guaranteed by appropriately choosing the prediction horizon length of the MPC controller. This method is based on finite time controllability as well as bounds on the MPC value function. Afterwards, a regulation control of a multi-robot system (MRS) is considered. In this control problem, the objective is to stabilize a group of mobile robots to form a pattern. We achieve this task using a distributed model predictive control (DMPC) scheme based on a novel communication approach between the subsystems. This newly introduced method is based on the quantization of the robots’ operating region. Therefore, the proposed communication technique allows for exchanging data in the form of integers instead of floating-point numbers. Additionally, we introduce a differential communication scheme to achieve a further reduction in the communication load. Finally, a moving horizon estimation (MHE) design for the relative state estimation (relative localization) in an MRS is developed in this thesis. In this framework, robots with less payload/computational capacity, in a given MRS, are localized and tracked using robots fitted with high-accuracy sensory/computational means. More precisely, relative measurements between these two classes of robots are used to localize the less (computationally) powerful robotic members. As a complementary part of this study, the MHE localization scheme is combined with a centralized MPC controller to provide an algorithm capable of localizing and controlling an MRS based only on relative sensory measurements. The validity and the practicality of this algorithm are assessed by realtime laboratory experiments. The conducted study fills important gaps in the application area of autonomous navigation especially those associated with optimization based solutions. Both theoretical as well as practical contributions have been introduced in this research work. Moreover, this thesis constructs a foundation for using MPC without stabilizing constraints or costs in the area of non-holonomic mobile robots

Rotational start-up of tethered airplanes based on nonlinear MPC and MHE

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Rotational start-up of tethered airplanes based on nonlinear MPC and MHE

The idea of Airborne Wind Energy (AWE) is to generate power by flying a tethered airfoil across the windflow. Tethered flight is a fast, strongly nonlinear, unstable and constrained process, motivating control approaches based on fast Nonlinear Model Predictive Control (NMPC) and state estimation approaches based on Moving Horizon Estimation (MHE). In particular, the start-up phase of AWE systems is an involved procedure, and starting and landing using NMPC has not been investigated yet. In this paper, a control strategy for starting-up AWE systems is proposed, based on a rotating carousel that is currently built at the KU Leuven. A computationally efficient 6-DOF control model for a small-scale, rigid airfoil is presented. We present and investigate a control scheme based on receding-horizon Nonlinear Model Predictive Control to track reference trajectories and Moving Horizon Estimation to estimate the actual system state and parameters. The MHE shceme is able to estimate also the wind speed, given no direct wind measurement