Abstract
This paper presents an attitude control scheme combined with adaptive dynamic programming (ADP) for reentry vehicles with high nonlinearity and disturbances. Firstly, the nonlinear attitude dynamics is divided into inner and outer loops according to the time scale separation and the cascade control principle, and a general sliding mode control method is employed to construct the main controllers for the double loops. Considering the shortage of main controllers in handling nonlinearity and sudden disturbances, an ADP structure is introduced into the outer attitude loop as an auxiliary. And the ADP structure utilizes neural network estimators to minimize the cost function and generate optimal signals through online learning, so as to compensate defect of the main controllers’ adaptability speed and accuracy. Then, the stability is analyzed by the Lyapunov method, and the parameter selection strategy of the ADP structure is derived to guide implementation. In addition, this paper puts forward skills to speed up ADP training. Finally, simulation results show that the control strategy with ADP possesses stronger adaptability and faster response than that without ADP for the nonlinear vehicle system.
Highlights
Attitude control for reentry vehicles has been a hotspot in the field of aerospace. e complex operating conditions and the high nonlinearity of vehicles themselves bring great challenges to attitude control
To increase the performance of the main controller of the outer loop, the adaptive dynamic programming (ADP) controller acts as an auxiliary and adopts an actiondependent structure such as ADHDP
Combining the hottest reinforcement learning at present, this paper presents an ADP-based attitude control methodology for reentry vehicles, applying the ADP to the threechannel attitude control
Summary
Attitude control for reentry vehicles has been a hotspot in the field of aerospace. e complex operating conditions and the high nonlinearity of vehicles themselves bring great challenges to attitude control. E complex operating conditions and the high nonlinearity of vehicles themselves bring great challenges to attitude control Around these focuses, researchers continue to explore and ameliorate control schemes, developing a series of available control technologies. ADP can be associated with traditional methods, such as nonlinear filter [20] and sliding mode control [21], to implement a data-driven ADHDP auxiliary control scheme for the speed and altitude system of an air-breathing hypersonic vehicle [21]. Most of the literature rarely mentions the internal weight convergence, parameter selection, training speed, and other issues of ADP based on critic-action networks, but these are problems to be concerned about.
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