Design and implementation of linear-quadratic-Gaussian stability augmentation autopilot for unmanned air vehicle

Abstract

The linear-quadratic-Gaussian (LQG) control synthesis has the advantage of dealing with the uncertain linear systems disturbed by additive white Gaussian noise while having incomplete system state information available for control-loop feedback. This paper hence explores the feasibility of designing and implementing a stability augmentation autopilot for fixed-wing unmanned air vehicles using the LQG approach. The autopilot is composed of two independently designed LQG controllers which control the longitudinal and lateral motions of the aircraft respectively. The corresponding linear models are obtained through a system identification routine which makes use of the combination of two well-established identification methods, namely the subspace method and prediction error method. The two identification methods complement each other well and this paper shows that the proposed system identification scheme is capable of attaining satisfactory state-space models. A complete autopilot design procedure is devised and it is shown that the design process is simple and effective. Resulting longitudinal and lateral controllers are successfully verified in computer simulations and actual flight tests. The flight test results are presented in the paper and they are found to be consistent with the simulation results.

Design and Implementation of an Optimal Energy Control System for Fixed-Wing Unmanned Aerial Vehicles

“…The doublet input is a two-sided pulse, resulting with a symmetrical signal, which has higher energy and wider frequency bandwidth compared to pulse input. The time step of doublet input can be approximated by: (19) where ∆t DBLT is the time step of doublet input and T osc is the period of aircraft oscillation. The input design is a part of the iterative process of system identification, until an adequate model is developed and finally the model is successfully with the implementation of the controller.…”

“…LQG regulator state-space model can be formed with the combination of Kalman estimator state-space model and LQR optimal feedback gain. For more details of LQG regulator design for a small UAV refer to studies [18,19].…”

A linear-quadratic-Gaussian approach for automatic flight control of fixed-wing unmanned air vehicles

“…This means that in its core, the LQR algorithm is an automated way of finding an appropriate state-feedback controller. Its biggest pitfall, however, is the requirement of the real-time full-state measurement which is often unavailable in practice (7) . In conjunction with that, the linear-quadratic-Gaussian (LQG) controller is an extension of the LQR where the unmeasured states are estimated using an optimal observer, i.e.…”

“…The feature makes it ideal to serve as an automatic flight controller for UAVs. In fact, Lee et al (7) have shown that the LQG works very well as the baseline stability augmentation autopilot for a 30kg fixed-wing UAV which handles the inner-loop control of the system.…”

“…In light of the above discussions, this paper attempts to demonstrate the feasibility of realizing the automatic flight controllers for the Spoonbill UAV by adopting the LQG design methodology and further extend the controllers to include the outer-loop control. The Spoonbill UAV (7,8) is an experimental research platform under development by the Remotely Piloted Vehicle and Microsatellite Research Laboratory (RMRL) of National Cheng Kung University, Taiwan. The UAV system was built under the context of Spoonbill project where the UAV is supposed to perform an automatic crosssea demonstration flight over a distance of approximately 44km by August 2009.…”