Synthesis and Flight Test of Automatic Landing Controller Using Quantitative Feedback Theory

Abstract

Landing is a challenging flight phase for automatic control of fixed-wing aircraft. For unmanned air vehicles in particular, it is imperative that model uncertainty be considered in the control synthesis. These vehicles tend to have limited sensors and instrumentation yet must achieve sufficient performance in the presence of modeling uncertainties and exogenous inputs such as turbulence. Quantitative feedback theory has been reported in the literature for design of automatic landing control laws, but none of these controllers has been flight-tested. In this paper, quantitative feedback theory is employed to synthesize robust discrete-time controllers for automatic landing of an unmanned air vehicle. A low-cost flight vehicle with standard aileron, rudder, elevator, and throttle controls is used. Dynamic simulation is conducted using uncertain aircraft models and sensor noise profiles derived from flight hardware. Controllers are initially synthesized in deterministic simulations. Control validation is performed using a Monte Carlo analysis of stochastic simulations. Sources of uncertainty considered are sensor noise, model uncertainty, and static winds. Landing-phase simulations presented in this paper indicate a routinely high probability of a successful landing in relatively calm wind conditions. The flight-testing process is discussed, and time histories from two automatic landings are presented. Dynamic responses in flight test are found to be similar to the simulation, but a significant amount of control redesign is still required to achieve adequate experimental performance. The methodology is judged to be a promising candidate for an automatic landing controller for unmanned air vehicles.

Bulletin of the Polish Academy of Sciences: Technical Sciences

“…According to the relationship between the longitudinal position and pitch angle, longitudinal position components z g can be shown by żg = −V cos β cos α sin θ + (V sin β sin φ +V cos β sin α cos φ ) cos θ , (18) assuming that θ = θ trim +e θ , in which θ trim is equilibrium value of pitch angle at the velocity of 70 m/s, and e θ is pitch angle deviation. Substituting it into Eq.…”

“…Likewise, a model reference adaptive control was used to design the ACLS guidance law [14], which can deal with the coupling of all loops and uncertainty of parameters. Some scholars proposed model predictive control [15][16][17], quantitative feedback theory [18], DOB-based neural control [19,20], brainstorm optimization [21], and pigeoninspired optimization [22].…”

Automatic Landing System Design for Unmanned Fixed-Wing Vehicles via Multivariable Active Disturbance Rejection Control

“…The discrete quantitative feedback theory (QFT) controller has been compared with a baseline PID controller for medium-sized UAV automatic landing [12]. However, flight results have been presented using QFT, where a significant number of controls need to be redesigned to achieve adequate experimental performance [13]. Lungu [14] designed an ALS controller that combines backstepping and dynamic inversion for a flying wing UAV subject to wind gusts and measurement sensor errors.…”

Design of Automatic Carrier-Landing Controller Based on Compensating States and Dynamic Inversion

“…Wang et al adopted a trajectory tracking method based on robust fuzzy theory and adaptive neural control [17], [18], in which the stability analysis of the closed-loop system inspires the present paper. Some researchers adopt model predictive control (MPC) [19], quantitative feedback theory [20], brain storm optimization [21], and pigeon− inspired optimization [22]. However, the aforementioned methods only focus on the special problems, such as control precision and uncertainties of landing model, which indicates that they lack the abilities of solving the general problems.…”