This approach demonstrates effective control of unmanned aerial vehicles in fixed-time scenarios, suggesting neural networks improve tracking performance while addressing constraints.
This paper investigates the fixed-time tracking control problem for underactuated quadrotor unmanned aerial vehicle systems subject to mass parameter uncertainties and user-defined time constraints. For the parameter uncertainties inherent in the system, the approximation capability of neural networks is exploited for compensation. Combined with the backstepping technique, this work proposes a new adaptive control strategy to ensure that the error variable converges within a small region near zero within a fixed time, and the designed controller effectively avoids singularity issues. Furthermore, a unified constraint framework with a shift function is introduced into the controller design, thereby providing a unified framework for user-defined time constraints that can flexibly handle different constraint scenarios without altering the control architecture. Finally, simulations are conducted to validate the effectiveness of the proposed method.
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Wang et al. (2026) studied this question.
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