An adaptive fixed-time sliding mode control method based on neural network is proposed for uncertain manipulator systems with actuator input dead zone. Based on the adaptive neural network and the minimum parameter learning method, the adaptive neural network sliding mode controller of the closed loop system of the manipulator is designed to effectively compensate the input dead zone of the manipulator, and only one parameter is updated online. Based on the fixed-time stability theory, it is proved that the controller can not only achieve the reachability of the sliding mode surface, but also ensure that the position tracking error signal of the closed-loop system converges to any small neighborhood with zero as the center in a fixed time, and then exponentially converges to zero. Finally, the simulation results verify the effectiveness of the proposed control method, and improve the transient and steady-state performance of the manipulator's tracking control to a certain extent.
GUO Chenghao
,
LIU Zhen
,
JIANG Baoping
. Fixed Time Sliding Mode Control of Manipulator System with Input Dead Zone[J]. Complex Systems and Complexity Science, 2026
, 23(4)
: 152
-160
.
DOI: 10.13306/j.1672-3813.2026.04.018
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