Synchronization of a chaotic gyroscopic system under settling time constraints

A simple and easy-to-implement method that guarantees the effective active synchronization of a chaotic gyroscopic system within a specified settling time limit is presented. A closed-form expression is given for the determination of the appropriate synchronizing control signal. The method is successfully validated through simulations for various initial conditions of the gyroscopic system

Optimal robust control of aeroelastic system vibrations

"A method for global and robust stabilization of aeroelastic wing vibrations based on optimal feedback control concepts is described in the present paper using Lyapunov stability theory. The method consists in decomposing the system model into a stabilizable linear part and a nonlinear part that satisfies sector-bound inequality; then a control law is designed to guarantee the global stabilization of the system and a specified robustness degree of the closed-loop dynamics. The validation of the method on aeroelastic wing section demonstrates better control performances over existing methods. The main contribution of the proposed method is that it allows one to design a linear controller that globally stabilizes a highly nonlinear system up to a specified degree of robustness without assuming any stability condition about the linear part, or matching conditions about the nonlinear uncertainties, contrarily to existing methods about optimal robust control

Generating Time Optimal Trajectory from Predefined 4D waypoint Networks

The main purpose of this paper is to develop a trajectory optimization method to generate optimal trajectories that minimize aircraft total trip time between the initial and final waypoint in predefined 4D waypoint networks. In this paper, the 4D waypoint networks only consist of waypoints for climb, cruise and descent approach without the take-off and landing approach phases. The time optimal trajectory is generated for three different lengths of flights (short, medium, and long-haul flight) for two different commercial aircraft and considering zero wind condition. The Results about the presented applications show that by flying a time optimal trajectory, which was found by applying a single source shortest path algorithm (Dijkstra’s algorithm), can lead to the reduction of average travel time by 2.6% with respect to the total trip time.info:eu-repo/semantics/publishedVersio

Optimal Robust Nonlinear LQG/LTR Control with Application to Longitudinal Flight Control

As part of the development of a new 4D Autopilot System for Unmanned Aerial Aircrafts (UAVs), i.e. a time-dependent robust trajectory generation and control algorithm, this work addresses the problem of optimal path finding based on the aircraft’s own sensors data output, that may be unreliable due to noise on data acquisition and/or transmission under certain circumstances. Although several filtering methods, such as the Kalman-Bucy Filter or the LQG/LTR, are available, the utter complexity of the new control system, together with the robustness and reliability required of such a system on an UAV for airworthiness certifiable autonomous flight, required the development of a proper robust filter for a nonlinear system, as a way of further mitigate errors propagation to the control system and improve its performance. As such, a new nonlinear LQG/LTR algorithm, validated through computational simulation testing, is proposed on this paper. This research work was conducted in the Laboratory of Avionics and Control of the Department of Aerospace Sciences (DCA) at the Faculty of Engineering of the University of Beira Interior and supported by the Aeronautics and Astronautics Research Group (AeroG) of the Associated Laboratory for Energy, Transports and Aeronautics (LAETA).info:eu-repo/semantics/publishedVersio

Nonlinear Time-varying Parameter Estimation from Noisy Measurements

Online parameter estimation for time-varying systems is a fundamental part of adaptive control, real-time system monitoring and prediction. A well-known framework for dealing with such a task is the Kalman filtering. Meanwhile Kalman filtering may be cumbersome for some time-critical systems and inappropriate for systems whose stochastic characteristics are not Gaussian. To overcome these shortcomings, a parameter estimation algorithm devised from Sutton’s dynamic learning rate techniques and based on a learning window and forgetting factor criterion has been used. In doing so, the proposed algorithm avoids the need for heuristic choices of the initial conditions and noise covariance matrices required by the Kalman filtering. The performance of the proposed method is demonstrated successfully on a lateral-directional flight dynamics parameter estimation process for an unmanned aerial vehicle through computational simulation.info:eu-repo/semantics/publishedVersio

Synchronization of a chaotic gyroscopic system under settling time constraints

A simple and easy-to-implement method that guarantees the effective active synchronization of a chaotic gyroscopic system within a specified settling time limit is presented. A closed-form expression is given for the determination of the appropriate synchronizing control signal. The method is successfully validated through simulations for various initial conditions of the gyroscopic system

Spline parameterization based nonlinear trajectory optimization along 4D waypoints

Flight trajectory optimization has become an important factor not only to reduce the operational costs (e.g.,, fuel and time related costs) of the airliners but also to reduce the environmental impact (e.g.,, emissions, contrails and noise etc.) caused by the airliners. So far, these factors have been dealt with in the context of 2D and 3D trajectory optimization, which are no longer efficient. Presently, the 4D trajectory optimization is required in order to cope with the current air traffic management (ATM). This study deals with a cubic spline approximation method for solving 4D trajectory optimization problem (TOP). The state vector, its time derivative and control vector are parameterized using cubic spline interpolation (CSI). Consequently, the objective function and constraints are expressed as functions of the value of state and control at the temporal nodes, this representation transforms the TOP into nonlinear programming problem (NLP). The proposed method is successfully applied to the generation of a minimum length optimal trajectories along 4D waypoints, where the method generated smooth 4D optimal trajectories with very accurate results.info:eu-repo/semantics/publishedVersio

Optimal Fuel Saving in 4D Waypoint Networks

The purpose of this work is to develop a trajectory optimization method that generates a fuel optimal trajectory from a predefined 4D waypoint networks, where the arrival time is specified for each waypoint in the network. A single source shortest path algorithm is presented to generate the optimal flight trajectory that minimizes fuel burn. Generating such trajectories enables the airlines to cope with increasing fuel costs and to reduce aviation induced climate change, as emission is directly related to the amount of fuel burn. Two case studies were considered and the simulation results showed that flying a fuel optimal trajectory based on the proposed algorithm leads to a reduction of average fuel consumption on international flights by 2-4% compared with the conventional trip fuel.info:eu-repo/semantics/publishedVersio

Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios.info:eu-repo/semantics/publishedVersio

Guidance and Robust Control of a Double-Hull Autonomous Underwater Vehicle

The aim of this paper is to present, discuss and evaluate two linear control solutions for an Autonomous Underwater Vehicle (AUV). As guidance solution, a waypoint following and station-keeping algorithm is presented. Then a PID design is proposed, through the decoupling of the linear system into three lightly interactive subsystems. A Linear Quadratic Regulator (LQR) design is also presented, based on the division of the linear system into longitudinal and lateral subsystems. A control allocation law is also presented to deal with the underactuation problems. Both controllers proved robust for this operating point although, regarding performance, and, for the performed simulation, the LQR controller proved more responsive.info:eu-repo/semantics/publishedVersio