Ahmad Bani Younes

A review on the platform design, dynamic modeling and control of hybrid UAVs

This article presents a review on the platform design, dynamic modeling and control of hybrid Unmanned Aerial Vehicles (UAVs). For now, miniature UAVs which have experienced a tremendous development are dominated by two main types, i.e., fixed-wing UAV and Vertical Take-Off and Landing (VTOL) UAV, each of which, however, has its own inherent limitations on such as flexibility, payload, axnd endurance. Enhanced popularity and interest are recently gained by a newer type of UAVs, named hybrid UAV that integrates the beneficial features of both conventional ones. In this paper, a technical overview of the recent advances of the hybrid UAV is presented. More specifically, the hybrid UAVs platform design together with the associated technical details and features are introduced first. Next, the work on hybrid UAVs flight dynamics modeling is then categorized and explained. As for the flight control system design for the hybrid UAV, several flight control strategies implemented are discussed and compared in terms of theory, linearity and implementation.

New Solutions for the Perturbed Lambert Problem Using Regularization and Picard Iteration

A new approach for solving two-point boundary value problems and initial value problems using the Kustaanheimo–Stiefel transformation and Modified Chebyshev–Picard iteration is presented. The first contribution is the development of an analytical solution to the elliptic Keplerian Lambert problem based on Kustaanheimo–Stiefel regularization. This transforms the nonlinear three-dimensional orbit equations of motion into four linear oscillators. The second contribution solves the elliptic Keplerian two-point boundary value problem and initial value problem using the Kustaanheimo–Stiefel transformation and Picard iteration. The Picard sequence of trajectories represents a contraction mapping that converges to a unique solution over a finite domain. Solving the Keplerian two-point boundary value problem in Kustaanheimo–Stiefel variables increases the Picard domain of convergence from about one-third of an orbit (Cartesian variables) to over 95% of an orbit (Kustaanheimo–Stiefel variables). These increases in ...

Attitude Error Kinematics

E XACT analytical attitude error kinematic equations for most of the known attitude representations are derived. Consequently, these attitude error kinematicmodels hold for arbitrarily large relative rotations and rotation rates. These attitude errors represent instantaneous departures from a general, smooth reference attitude motion. Numerical integrations of these kinematic equations are performed to validate the machine error accuracy for each attitude representation. Singularities and constraints are discussed for minimum and nonminimum attitude parameter representations, respectively. Applications are expected in estimation for general rotational dynamics as well as for attitude tracking errors. Many attitude representations are available for modeling problems in science and engineering [1–5]. Nonlinearity of the representation of a given physical motion and location of geometric singularities are dependant on three things: 1) the actual motion, 2) the attitude representation, and 3) the particular choice of a moving reference axis. Selecting the appropriate representation is highly linked with the kind of the problem being considered. Themost popular ones are: a) the direction cosine matrix (DCM), b) the principal axis and angle, c) the Euler parameters (quaternion), a nonsingular four component unit-vector, d) the classical Rodrigues parameters (CRPs), a threecomponent vector (minimal parametrization), e) the modified Rodrigues parameters (MRPs) [6,7], a three-component vector, and f) the Cayley–Klein parameters, a complex unitary 2 × 2 matrix. Definitions, characteristics, and transformations between these representations can be found in many references [1–4,8,9]. For applications requiring large and rapid rotational motions there exists a need for developing attitude error kinematic models that exactly describe arbitrary large rotational motions. The main contribution of this work is to develop exact large motion error kinematic differential equations for attitude error relative to a general smooth reference trajectory in a unified presentation. Several attitude parameterizations are compared by solving a nonlinear spacecraft tracking problem. Markley [8] has considered different attitude error representations for estimating the state of a maneuvering spacecraft. He has clarified the relationship between the four-component quaternion representation of attitude and the multiplicative extendedKalman filter. Crassidis et al. [10] investigated a variable-structure control strategy for maneuvering vehicles. In their work, they used a feedback linearizing technique and added an additional term to the spacecraft maneuvers to deal with model uncertainties, which they demonstrated always provides an optimal response. Ahmed et al. [11] extended previous work to consider adaptive asymptotic tracking during maneuvers while estimating inertia properties. They used a Lyapunov argument to generate an unconditionally robust control law with respect to their assumed parametric uncertainty. Bani Younes et al. [12,13] considered generalized optimal control formulations that handle nonlinear system dynamics and enable the development of control gain sensitivities to handle plant model uncertainties during maneuvers. Sharma and Tewari [14] introduced MRPs for parameterization of the orientation error. Theydefined the attitude error as an additive quantity. Theirwork is extended by retaining a rigorous nonlinear MRP-based error equation. Schaub et al. [15] developed a new penalty function for optimal control formulation of spacecraft attitude control problems. This function returns the same scalar penalty for a given physical attitude regardless of the attitude coordinate choice. This role of various performance indices, in conjunction with various coordinate choices, will be considered in a future paper. Junkins [16] discussed the link between designing a good controller and the choice of coordinates to represent the attitude kinematics. He linearized the attitude error equations by defining the departure motion as an additive error from a nominal trajectory. Normally, the position error is described by the distance between the two vectors, which represents the current position state and a prescribed reference position state. However, the error in orientation cannot be rigorously represented as an addition error because of the nonlinear behavior of underlying kinematical descriptions [17]. This work presents attitude variable representations that account for the coupled nonlinear error kinematics for departure from a general motion. No approximations are introduced in the description of the vehicle attitude motion. The resulting expressions have been optimized to obtain the most compact and computationally efficient forms.

A Survey of Attitude Error Representations

Several attitude error representations are developed for describing the tracking orientation error kinematics. Compact forms of attitude error equation are derived for each case. The attitude error is initially de ned as the quaternion (rotation) error between the current and the estimated orientation. The nonlinear kinematic models are valid for arbitrarily large relative rotations and rotation rates. These modes have been developed for supporting the development of nonlinear spacecraft maneuver formulations. All of the kinematic formulations assume that a reference state has been de ned. These results are expected to be broadly useful for generalizing extended Kalman ltering formulations. The bene ts of paper are discussed.

An Investigation of State Feedback Gain Sensitivity Calculations

*† ‡ § A nonlinear feedback control strategy is presented where the feedback control is augmented with feedback gain sensitivity partial derivatives for handling model uncertainties. Applications to optimal feedback and robust control theories are presented. The OCEA (Object Oriented Coordinate Embedding) computational differentiation method is used for generating the partial derivatives. An array.of.arrays data structure is introduced for (1) tracking the derivative terms arising during a derivation of the tensor necessary conditions, and (2) assembling a generalized state space model for integrating the state and tensor differential equation models. Both scalar and vector applications are presented. Sensitivity calculations are developed for open.loop and feedback solutions for optimal control and robust control problem formulations. The pre. calculation of the sensitivity gains significantly reduces the computational effort required for implementing the handling of real.time plant parameter variations and equation of motion nonlinearities. The methodology is demonstrated on a general tracking problem which uses Taylor expansions of the gain calculations for handling nonlinear parameter and model errors. Several examples are presented that demonstrate the impact of nonlinear response behaviors, as well as the effectiveness of the generalized sensitivity enhanced feedback control strategy. A nonlinear spacecraft attitude tracking problem is presented for demonstrating the effectiveness of the proposed approach. The methodology of this paper is expected to be broadly useful for applications in science and engineering.

Inferring microbial interaction networks from metagenomic data using SgLV-EKF algorithm

BackgroundInferring the microbial interaction networks (MINs) and modeling their dynamics are critical in understanding the mechanisms of the bacterial ecosystem and designing antibiotic and/or probiotic therapies. Recently, several approaches were proposed to infer MINs using the generalized Lotka-Volterra (gLV) model. Main drawbacks of these models include the fact that these models only consider the measurement noise without taking into consideration the uncertainties in the underlying dynamics. Furthermore, inferring the MIN is characterized by the limited number of observations and nonlinearity in the regulatory mechanisms. Therefore, novel estimation techniques are needed to address these challenges.ResultsThis work proposes SgLV-EKF: a stochastic gLV model that adopts the extended Kalman filter (EKF) algorithm to model the MIN dynamics. In particular, SgLV-EKF employs a stochastic modeling of the MIN by adding a noise term to the dynamical model to compensate for modeling uncertainties. This stochastic modeling is more realistic than the conventional gLV model which assumes that the MIN dynamics are perfectly governed by the gLV equations. After specifying the stochastic model structure, we propose the EKF to estimate the MIN. SgLV-EKF was compared with two similarity-based algorithms, one algorithm from the integral-based family and two regression-based algorithms, in terms of the achieved performance on two synthetic data-sets and two real data-sets. The first data-set models the randomness in measurement data, whereas, the second data-set incorporates uncertainties in the underlying dynamics. The real data-sets are provided by a recent study pertaining to an antibiotic-mediated Clostridium difficile infection. The experimental results demonstrate that SgLV-EKF outperforms the alternative methods in terms of robustness to measurement noise, modeling errors, and tracking the dynamics of the MIN.ConclusionsPerformance analysis demonstrates that the proposed SgLV-EKF algorithm represents a powerful and reliable tool to infer MINs and track their dynamics.

High-Order Uncertainty Propagation Enabled by Computational Differentiation

Modeling and simulation for complex applications in science and engineering develop behavior predictions based on mechanical loads. Imprecise knowledge of the model parameters or external force laws alters the system response from the assumed nominal model data. As a result, one seeks algorithms for generating insights into the range of variability that can be the expected due to model uncertainty. Two issues complicate approaches for handling model uncertainty. First, most systems are fundamentally nonlinear, which means that closed-form solutions are not available for predicting the response or designing control and/or estimation strategies. Second, series approximations are usually required, which demands that partial derivative models are available. Both of these issues have been significant barriers to previous researchers, who have been forced to invoke computationally intensive Monte-Carlo methods to gain insight into a system’s nonlinear behavior through a massive sampling process. These barriers are overcome by introducing three strategies: (1) Computational differentiation that automatically builds exact partial derivative models; (2) Map initial uncertainty models into instantaneous uncertainty models by building a series-based state transition tensor model; and (3) Compute an approximate probability distribution function by solving the Liouville equation using the state transition tensor model. The resulting nonlinear probability distribution function (PDF) represents a Liouville approximation for the stochastic Fokker-Planck equation. Several applications are presented that demonstrate the effectiveness of the proposed mathematical developments. The general modeling methodology is expected to be broadly useful for science and engineering applications in general, as well as grand challenge problems that exist at the frontiers of computational science and mathematics.

Satellite attitude estimation in a novel operational environment

Space missions require on-board sensors for computing inputs for control applications spanning orbit determination; guidance, navigation, and control; rendezvous and grappling; and determination of relative pose and angular rates. To achieve high performance Satellite applications, a detailed knowledge regarding the orientation of sensor packages relative to the core Satellite is required. An initial calibration of the sensing systems is performed on-ground to minimize the error sources in the signal processing that allows the sensor systems to recover mission critical data in real time. This work presents recent experimental work performed at Khalifa University Spacecraft Platform for Astronautic and Celestial Emulation (SPACE) Laboratory for calibrating hardware sensor platforms in an operationally relevant ground-based environment. Gaussian nonlinear differential correction is used to recover the critical system parameters.

Camera sensor calibration approach for space control applications

Recent space science missions, including spacecraft navigation, orbit determination, and sensing systems that mimic the actual conditions in space have a considerable success depends on accurate trajectory tracking methods. Since ground-based calibration efforts can be corrupted during launch environments it is necessary to develop on-orbit calibration methods to maximize sensor performance. Future space systems will require more advanced navigation and control systems to avoid these potential problems. In this work, a camera sensor calibration approach is presented to conduct mission-oriented researches, and get hands on experience in conducting experiments for sensing, guidance, dynamics, and control of aerospace and space operations in a high-accurate environment.

Semi-Analytic Probability Density Function for System Uncertainty

In general, the behavior of science and engineering is predicted based on nonlinear math models. Imprecise knowledge of the model parameters alters the system response from the assumed nominal mode...