2022
DOI: 10.1049/cth2.12406
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Air–ground trajectory tracking for autonomous mobile robot based on model predictive hybrid tracking control and multiple harmonics time‐varying disturbance observer

Abstract: This paper studies a model predictive hybrid tracking control scheme under a multiple harmonics time-varying disturbance observer for a discrete-time dynamics nonholonomic autonomous mobile robot (AMR) with disturbance. To solve the robust tracking control problem of the AMR and unmanned aerial vehicle (UAV) air-ground cooperative, a hybrid tracking control strategy combined with improved model predictive control (MPC) method is presented. First, a time-varying air-ground cooperative tracking control model bas… Show more

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Cited by 4 publications
(4 citation statements)
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References 32 publications
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“…We can can applied the supertwisting algorithm for the system (11). Firstly, the following assumption can be given.…”
Section: B X1(t ) and X2(t ) Stabilization'smentioning
confidence: 99%
See 2 more Smart Citations
“…We can can applied the supertwisting algorithm for the system (11). Firstly, the following assumption can be given.…”
Section: B X1(t ) and X2(t ) Stabilization'smentioning
confidence: 99%
“…In order to stabilise the (11) and using the terminal sliding variable as (12), the supertwisting algorithm can be designed as…”
Section: B X1(t ) and X2(t ) Stabilization'smentioning
confidence: 99%
See 1 more Smart Citation
“…Within the broad and prevailing applications of vehicle swarms, including air-ground unmanned vehicle cooperation [1], unmanned aerial vehicle coordinated collision avoidance [2], distributed tracking of competitive autonomous underwater vehicles [3] and multiple spacecraft flight formation [4], the desired orbit tracking and formation motion control problem, that is, multiple unmanned vehicles (MUVs) tracking a level of orbits and forming into certain formation, has received much attention. In the literature, many results of formation control have been given based on the traditional control theory [5][6][7].…”
Section: Introductionmentioning
confidence: 99%