Lateral autonomous carrier-landing control with high-dimension landing risks consideration

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>This paper aims to propose a novel model predictive control (MPC) with time varying weights to develop a lateral control law in an automatic carrier landing system (ACLS), which minimizes landing risk and improves flight quality.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>First, a nonlinear mathematic model of an F/A-18 aircraft during lateral landing is established. Then the landing model is linearized in the form of state deviations on the equilibrium points. Second, landing risk windows are proposed and a high-dimensional landing risk model is addressed through a back propagation (BP) neural network. The trained samples are acquired based on a pilot behavior model. Third, time varying weights created from the lateral landing risk are introduced into the performance function of MPC. Optimal solution is solved quicker and some state deviations are focused on and eliminated. Fourth, the algebraic inequalities are substituted by the linear matrix inequalities (LMIs), which are easily calculated by the computers.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>On a semi-physical platform, the proposed method compares with a traditional MPC algorithm and a modified MPC with an additional term. The test results indicate that the proposed algorithm brings about an excellent landing performance as well as an ability of eliminating landing risk.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Practical implications</jats:title> <jats:p>The landing phase of a carrier-based aircraft is one of the most dangerous and complicated stages, and the algorithm proposed by this paper plays a vital role in the lateral landing.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>This paper establishes a lateral landing risk model, which considers not only the current landing state but also the future touchdown point. This lateral landing risk is integrated into the time varying weights of the MPC algorithm so that the state deviations and landing risk can be both reduced in the rolling optimization.</jats:p> </jats:sec>

Bulletin of the Polish Academy of Sciences: Technical Sciences

“…The ACLS control law including Eqs. ( 10), ( 15), (17), and ( 23) is an exponential stable error system. The initial state errors e * (0) and the first order derivative ė * (0) are all 0.…”

“…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].…”

Robust fault-tolerant preview control for automatic landing of carrier-based aircraft

“…The sliding mode control was used to design the ACLS (Duan et al ., 2022a; 2022b) due to its insensitivity to the external varying environment. To improve the flight quality, the automatic control system was designed based on model predictive control with time-varying weights (Wang et al ., 2020). Faults caused by actuators are common in practical engineering systems, which seriously affect the safety of aircraft landing.…”