Exponentially stable observer-based controller for VTOL-UAVs without velocity measurements

Abstract

There is a great demand for vision-based robotics solutions that can operate using Global Positioning Systems (GPS), but are also robust against GPS signal loss and gyroscope failure. This paper investigates the estimation and tracking control in application to a Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) in six degrees of freedom (6 DoF). A full state observer for the estimation of VTOL-UAV motion parameters (attitude, angular velocity, position, and linear velocity) is proposed on the Lie Group of SE2 (3) × R 3 = SO (3) × R 9 with almost globally exponentially stable closed loop error signals. Thereafter, a full state observer-based controller for the VTOL-UAV motion parameters is proposed on the Lie Group with a guaranteed almost global exponential stability. The proposed approach produces good results without the need for angular and linear velocity measurements (without a gyroscope and GPS signals) utilizing only a set of known landmarks obtained by a vision-aided unit (monocular or stereo camera). The equivalent quaternion representation on S 3 × R 9 is provided in the Appendix. The observer-based controller is presented in a continuous form while its discrete version is tested using a VTOL-UAV simulation that incorporates large initial error and uncertain measurements. The proposed observer is additionally tested experimentally on a real-world UAV flight dataset.

UWB Ranging and IMU Data Fusion: Overview and Nonlinear Stochastic Filter for Inertial Navigation

“…(39) Quaternion form of the stochastic filter design proposed above is outlined in the Appendix. For the compact form, X ∈ SE 2 (3) describes the estimate of X, U m = u([Ω m ] × , 0 3×1 , a m , 1) ∈ U M , and W = u([w Ω ] × , w V , w a , 1) ∈ U M , refer to the map in (4). For simplicity's sake, in the analysis, P = P y .…”

Adaptive Neural Network Stochastic-Filter-Based Controller for Attitude Tracking With Disturbance Rejection

“…A TTITUDE estimation and tracking control of a rigidbody rotating in three-dimensional (3D) space are indispensable tasks for majority of robotics and aerospace applications. Examples include satellites, rotating radars, unmanned aerial vehicles (UAVs), space telescopes, to name a few [1][2][3][4][5][6]. The research in area of attitude estimation and control has made a great leap forward with the introduction of micro-electro-mechanical systems (MEMS) [7] that allowed for the design of compact low-cost onboard units such as inertial measurement units (IMUs) and magnetic, angular rate, and gravity (MARG) sensors.…”

“…In the above-discussed literature, attitude estimation and tracking control solutions are designed separately. However, the attitude problem is highly nonlinear and coupling a standalone filter design with a standalone controller design cannot guarantee overall stability, especially if 1) the rigid-body is equipped with a low-cost measurement unit and 2) a large initialization error is present between the true and the estimated states [2].…”

An Online Model-Following Projection Mechanism Using Reinforcement Learning

“…The model-following applications involve the regulation of hypersonic aircraft, autonomous vehicles, underactuated systems, and robotic manipulators [3]- [8]. The aforementioned model-following limitations are observed in several solutions such as dual mode predictive control [9], gain scheduling [4], sliding mode surfaces [10], [11], adaptive backstepping [12], [13] and L 1 adaptive control [6], Lyapunov theory-based MRAS [3], [14], model predictive control [7], [15], barrier function-based MRAS [5], means of linear matrix inequalities [16], and feedforward control [17]. The graphical games have been utilized to solve leader-follower control problems for LTI agents interacting using graph topologies [18]- [20].…”

“…𝑑𝜏. Similarly, the best strategy-to-follow is represented by (13). Hence, each strategy is approximated by an actor structure π𝑖 such that π𝑖…”