The Experts below are selected from a list of 87 Experts worldwide ranked by ideXlab platform

James Richard Forbes - One of the best experts on this subject based on the ideXlab platform.

  • Nonlinear Estimator Design on the Special Orthogonal Group Using Vector Measurements Directly
    IEEE Transactions on Automatic Control, 2017
    Co-Authors: David Evan Zlotnik, James Richard Forbes
    Abstract:

    The convergence properties of popular nonlinear attitude estimators can be traced to the choice of an attitude error function. This paper considers a nonlinear deterministic Direction Cosine Matrix estimator whose form is derived from an alternate attitude error function. While the resulting estimator shares several properties with those previously presented in the literature, the careful selection of an attitude error function results in an estimator with superior convergence properties. The attitude estimate is propagated using a rate gyroscope measurement and corrected using two or more vector measurements. Simulation and experimental results are presented that highlight the desirable properties of the proposed estimator.

  • Direction Cosine Matrix based attitude control subject to actuator saturation
    Iet Control Theory and Applications, 2015
    Co-Authors: James Richard Forbes
    Abstract:

    Set-point attitude control of a rigid body explicitly preventing actuator saturation is considered. The attitude control approach developed does not employ any sort of Direction-Cosine-Matrix (DCM) parameterisation, such as Euler angles or quaternions. Rather, the DCM is used directly within the feedback control algorithm. Together a proportional control term and an angular velocity control term make up the attitude controller. The angular velocity control is composed of a strictly positive real system subject to a special input non-linearity. The specific form of the proportional control and angular velocity control ensure control torques are below the saturation level of the on-board actuators. Two controller synthesis methods are considered. The first uses the linearised system, the solution to the linear quadratic regulator problem, and the Kalman–Yakubovich–Popov lemma to design the controller. The second employs a simple low-pass filter that is guaranteed to stabilise the closed-loop system; tuning the low-pass filter is also considered. Numerical simulation results demonstrate effective closed-loop control in the presence of plant disturbances and sensor noise.

Rajesh Rajamani - One of the best experts on this subject based on the ideXlab platform.

  • Direction Cosine Matrix estimation with an inertial measurement unit
    Mechanical Systems and Signal Processing, 2018
    Co-Authors: Yan Wang, Rajesh Rajamani
    Abstract:

    Abstract Estimating attitude using an inexpensive MEMS inertial measurement unit has many applications in smart phones, wearable sensors, rehabilitation medicine and robots. Traditional approaches to attitude estimation from the aerospace world focus on the use of either Euler angles or quaternions. These approaches suffer from disadvantages including singularities and nonlinear models. This paper proposes a method to estimate the Direction Cosine Matrix (DCM) which encapsulates attitude information, instead of Euler angles or quaternions. The DCM does not suffer from singularities and also has linear dynamics. A rigorous DCM estimation algorithm, that incorporates automatic magnetometer bias calibration and satisfaction of an inherent orthonormal property of the DCM, is developed. The validity of the developed algorithm is demonstrated through experimental results with estimation of attitude on a 5-DOF robot. The estimation results are compared with values computed from encoders on the robot as well as with results from previously published algorithms.

Jussi Collin - One of the best experts on this subject based on the ideXlab platform.

  • the Direction Cosine Matrix algorithm in fixed point implementation and analysis
    International Conference on Acoustics Speech and Signal Processing, 2019
    Co-Authors: Alexandre Meirhaeghe, Jani Boutellier, Jussi Collin
    Abstract:

    Inertial navigation allows tracking and updating the position and orientation of a moving object based on accelerometer and gyroscope data without external positioning aid, such as GPS. Therefore, inertial navigation is an essential technique for, e.g., indoor positioning. As inertial navigation is based on integration of acceleration vector components, computation errors accumulate and make the position and orientation estimate drift. Even though maximum computation precision is desired, also efficiency needs consideration in the age of Internet-of-Things, to enable deployment of inertial navigation based applications to the smallest devices. This work formulates the Direction Cosine Matrix update algorithm, a central component for inertial navigation, in fixed-point and analyzes its precision and computation load compared to a regular floating-point implementation. The results show that the fixed-point version maintains very high precision, while requiring no floating point hardware for operation. The paper presents execution time results on three very different embedded processors.

Woo Chang Jung - One of the best experts on this subject based on the ideXlab platform.

  • quaternion based local frame alignment between an inertial measurement unit and a motion capture system
    Sensors, 2018
    Co-Authors: Woo Chang Jung
    Abstract:

    : Local frame alignment between an inertial measurement unit (IMU) system and an optical motion capture system (MCS) is necessary to combine the two systems for motion analysis and to validate the accuracy of IMU-based motion data by using references obtained through the MCS. In this study, we propose a new quaternion-based local frame alignment method where equations of angular velocity transformation are used to determine the frame alignment orientation in the form of quaternion. The performance of the proposed method was compared with those of three other methods by using data with different angular velocities, noises, and alignment orientations. Furthermore, the effects of the following three factors on the estimation performance were investigated for the first time: (i) transformation concept, i.e., angular velocity transformation vs. angle transformation; (ii) orientation representations, i.e., quaternion vs. Direction Cosine Matrix (DCM); and (iii) applied solvers, i.e., nonlinear least squares method vs. least squares method through pseudoinverse. Within our limited test data, we obtained the following results: (i) the methods using angular velocity transformation were better than the method using angle transformation; (ii) the quaternion is more suitable than the DCM; and (iii) the applied solvers were not critical in general. The proposed method performed the best among the four methods. We surmise that the fewer number of components and constraints of the quaternion in the proposed method compared to the number of components and constraints of the DCM-based methods may result in better accuracy. Owing to the high accuracy and easy setup, the proposed method can be effectively used for local frame alignment between an IMU and a motion capture system.

Yan Wang - One of the best experts on this subject based on the ideXlab platform.

  • Direction Cosine Matrix estimation with an inertial measurement unit
    Mechanical Systems and Signal Processing, 2018
    Co-Authors: Yan Wang, Rajesh Rajamani
    Abstract:

    Abstract Estimating attitude using an inexpensive MEMS inertial measurement unit has many applications in smart phones, wearable sensors, rehabilitation medicine and robots. Traditional approaches to attitude estimation from the aerospace world focus on the use of either Euler angles or quaternions. These approaches suffer from disadvantages including singularities and nonlinear models. This paper proposes a method to estimate the Direction Cosine Matrix (DCM) which encapsulates attitude information, instead of Euler angles or quaternions. The DCM does not suffer from singularities and also has linear dynamics. A rigorous DCM estimation algorithm, that incorporates automatic magnetometer bias calibration and satisfaction of an inherent orthonormal property of the DCM, is developed. The validity of the developed algorithm is demonstrated through experimental results with estimation of attitude on a 5-DOF robot. The estimation results are compared with values computed from encoders on the robot as well as with results from previously published algorithms.