Carrier-based aircraft endow aircraft carriers with powerful combat capabilities but also bring about safety issues for carrier-based aircraft landing.Therefore,it is necessary to study the accuracy,speed and orbit-ch...Carrier-based aircraft endow aircraft carriers with powerful combat capabilities but also bring about safety issues for carrier-based aircraft landing.Therefore,it is necessary to study the accuracy,speed and orbit-changing ability of carrier-based aircraft to follow ideal glide trajectories.Based on the control strategy of the US military’s‘magic carpet’technology,with the E-2C as the target,decoupling the trajectory angle control and angle of attack control,a double-layer dynamic inverse landing flight trajectory incremental modal control method is designed.The simulation results show that the designed control law can accurately track the glide command,while maintaining the angle of attack and velocity,and has good control performance;provide the correction capability of carrier-based aircraft to correct back to−3.5°under different trajectory angle states,as well as the correction capability and boundary when there are different altitude deviations during the final landing phase.展开更多
Gravitational search algorithm(GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm(IGSA) to impr...Gravitational search algorithm(GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm(IGSA) to improve the performance of the GSA, and first applies it to the field of dynamic neural network identification. The IGSA uses trial-and-error method to update the optimal agent during the whole search process. And in the late period of the search, it changes the orbit of the poor agent and searches the optimal agent s position further using the coordinate descent method. For the experimental verification of the proposed algorithm,both GSA and IGSA are testified on a suite of four well-known benchmark functions and their complexities are compared. It is shown that IGSA has much better efficiency, optimization precision, convergence rate and robustness than GSA. Thereafter, the IGSA is applied to the nonlinear autoregressive exogenous(NARX) recurrent neural network identification for a magnetic levitation system.Compared with the system identification based on gravitational search algorithm neural network(GSANN) and other conventional methods like BPNN and GANN, the proposed algorithm shows the best performance.展开更多
基金supported by the Aeronautical Science Foundation of China under grant 20230007003002.
文摘Carrier-based aircraft endow aircraft carriers with powerful combat capabilities but also bring about safety issues for carrier-based aircraft landing.Therefore,it is necessary to study the accuracy,speed and orbit-changing ability of carrier-based aircraft to follow ideal glide trajectories.Based on the control strategy of the US military’s‘magic carpet’technology,with the E-2C as the target,decoupling the trajectory angle control and angle of attack control,a double-layer dynamic inverse landing flight trajectory incremental modal control method is designed.The simulation results show that the designed control law can accurately track the glide command,while maintaining the angle of attack and velocity,and has good control performance;provide the correction capability of carrier-based aircraft to correct back to−3.5°under different trajectory angle states,as well as the correction capability and boundary when there are different altitude deviations during the final landing phase.
基金supported by National Natural Science Foundationof China(No.2011ZX05021-003)Science Foundation of ChinaUniversity of Petroleum
文摘Gravitational search algorithm(GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm(IGSA) to improve the performance of the GSA, and first applies it to the field of dynamic neural network identification. The IGSA uses trial-and-error method to update the optimal agent during the whole search process. And in the late period of the search, it changes the orbit of the poor agent and searches the optimal agent s position further using the coordinate descent method. For the experimental verification of the proposed algorithm,both GSA and IGSA are testified on a suite of four well-known benchmark functions and their complexities are compared. It is shown that IGSA has much better efficiency, optimization precision, convergence rate and robustness than GSA. Thereafter, the IGSA is applied to the nonlinear autoregressive exogenous(NARX) recurrent neural network identification for a magnetic levitation system.Compared with the system identification based on gravitational search algorithm neural network(GSANN) and other conventional methods like BPNN and GANN, the proposed algorithm shows the best performance.
