Abstract
The electric rudder system (ERS) is the executive mechanism of the flight control system, which can make the missile complete the route correction according to the control command. The performance and quality of the ERS directly determine the dynamic quality of the flight control system. However, the transient and static characteristic of ERS is affected by the uncertainty of physical parameters caused by nonlinear factors. Therefore, the control strategy based on genetic algorithm (GA) identification method and finite-time rudder control (FTRC) theory is studied to improve the control accuracy and speed of the system. Differently from the existing methods, in this method, the difficulty of parameter uncertainty in the controller design is solved based on the ERS mathematical model parameter identification strategy. Besides, in this way, the performance of the FTRC controller was verified by cosimulation experiments based on automatic dynamic analysis of mechanical systems (ADAMS) (MSC software, Los Angeles, CA, USA) and matrix laboratory (MATLAB)/Simulink (MathWorks, Natick, MA, USA). In addition, the advantages of the proposed method are verified by comparing with the existing strategy results on the rudder test platform, indicating that the control accuracy is improved by 70% and the steady-state error is reduced by at least 50%.
Highlights
The electric rudder system (ERS) is the position servo mechanism of high-precision control systems such as aircraft, ship, and missile, whose dynamic and static performance directly determines the accuracy and rapidity of the controlled object [1]
In order to overcome the interference of different nonlinear factors in the design of the controller based on the ERS parameter model, research has made a lot of efforts from two aspects
2. of factors, this new overshoot control method based algorithm and influence finite theory. Factors, this paperoperator presents new based on genetic algorithm time theory
Summary
The electric rudder system (ERS) is the position servo mechanism of high-precision control systems such as aircraft, ship, and missile, whose dynamic and static performance directly determines the accuracy and rapidity of the controlled object [1]. In [8], the genetic algorithm is applied to the ERS parameter identification experiment, and the improved algorithm is proved to have good optimization effect by comparing with the classical optimization algorithm such as the least square method. Inspired by parameter identification and special control methods to solve uncertain parameters in ERS controller design, in this paper, firstly, ERS parameter identification algorithm uses linear ordering to perform selection operations, nonlinear uniform crossover operators, Gaussian mutation operators, Energies 2019, 12, x FOR PEER. The finite-time controller is designed by applying the improved genetic ordering to perform selection operations, nonlinear uniform crossover operators, Gaussian mutation algorithm (IGA) to the system model parameter identification. 5, the experimental platform is introduced, and the compared with the existing control strategies to verify the effectiveness of the finite-time rudder effectiveness of identification strategy and control method is verified by experiments.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
