Journal
IEEE ACCESS
Volume 10, Issue -, Pages 55102-55111Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3176644
Keywords
Uncertainty; Attitude control; Adaptive control; Artificial neural networks; Backstepping; System dynamics; Payloads; Unknown system dynamics estimator; adaptive control; quadrotor attitude control; backstepping
Categories
Funding
- National Key Research and Development Program of China [2019YFF0301802, 2019YFB2004802]
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This paper introduces an adaptive control scheme combined with an unknown system dynamics estimator for attitude control of quadrotors, effectively addressing parametric uncertainties and external disturbances. The unique feature lies in the separate handling of uncertainties and disturbances, significantly reducing the learning load of USDE and avoiding the use of high gains in feedback loops.
This paper presents an adaptive control scheme via combining with an unknown system dynamics estimator (USDE) for attitude control of quadrotors subject to both parametric uncertainties and external disturbances. The presented controller is designed by effectively merging an adaptive control with an USDE via backstepping, where the extraneous uncertainties are addressed by USDE, and the adaptive law driven by tracking errors accounts for identifying unknown inertial moment constants. The unique characteristic is that a reinforced attitude controlling can be realized benefiting from the separate handling of parametric uncertainties and disturbances via an adaptive updating and a concise disturbance observation, significantly releasing the learning load of USDE and a high gain can be avoided in feedback loops. Meanwhile, Lyapunov analysis demonstrates that all error variables are uniformly ultimately bounded in the closed-loop system. Eventually, simulations substantiate the utility of suggested control strategy.
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