Abstract
Efficiency and safety are vital for aviation operations in order to improve the combat capacity of aircraft carrier. In this article, the theory of apprenticeship learning, as a kind of artificial intelligence technology, is applied to constructing the method of automated scheduling. First, with the use of Markov decision process frame, the simulative model of aircrafts launching and recovery was established. Second, the multiplicative weights apprenticeship learning algorithm was applied to creating the optimized scheduling policy. In the situation with an expert to learn from, the learned policy matches quite well with the expert’s demonstration and the total deviations can be limited within 3%. Finally, in the situation without expert’s demonstration, the policy generated by multiplicative weights apprenticeship learning algorithm shows an obvious superiority compared to the three human experts. The results of different operation situations show that the method is highly robust and well functional.
Highlights
The capability of carrier-borne aircrafts launching and recovery is significant for an aircraft carrier’s combat capacity[1,2] It is one of the main concerns that the designers have to deal with in the stage of carrier conceptual design
The program of multiplicative weights AL (MWAL) algorithm application in carrier-borne aircrafts aviation operation scheduling was developed in MATLAB 2012b and performed on a Lenovo Think Centre PC (3.4-GHz Intel I7 CPU, 16-GB RAM, Windows 7 64-bit operation system)
Considering the complexity of the carrier-borne aircrafts aviation operation scheduling, the MWAL algorithm application in this article is of high precision
Summary
The capability of carrier-borne aircrafts launching and recovery is significant for an aircraft carrier’s combat capacity[1,2] It is one of the main concerns that the designers have to deal with in the stage of carrier conceptual design. In 1997, the USS Nimitz and Carrier Airwing Nine conducted surge operations and the highest SGR of Nimitz reached 975 in 4 days.[1,2] To analyze the bottleneck of SGR in surge operations, Zheng et al.[8] established a simulation model using Monte Carlo method to simulate the actual surge operations conducted in 1997, achieving relatively accurate results These theoretical and simulation-based solutions mentioned above could give reliable results almost the same as the data gained in surge operations, were lack of robustness to nonstationary stochastic disturbances such as aircraft malfunction and deck equipment malfunction. The policy should be optimized for safety, efficiency, and better robustness to different types of uncertainty inherent on the flight deck and in the air based on large amount of information
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