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Robust Adaptive Control of Automatic Guidance of Farm Vehicles in the Presence of Sliding
Abstract : High-precision autofarming is rapidly becoming a reality with the requirements of agricultural applications. Lots of research works have been focused on the automatic guidance control of farm vehicles, satisfactory results have been reported under the assumption that vehicles move without sliding. But unfortunately the pure rolling constraints are not always satisfied especially in agriculture applications where the working conditions are rough and not expectable. In this paper the problem of path following control of autonomous farm vehicles in presence of sliding is addressed. To take sliding effects into account, a vehicle-oriented kinematic model is constructed in which sliding effects are introduced as additive unknown parameters of the ideal kinematic model. Based on backstepping method a stepwise procedure is proposed to design an adaptive controller in which time-invariant sliding effects are learned and compensated by parameter adaptations. It is theoretically proven that for the farm vehicles subject to sliding, the lateral deviation can be stabilized near zero and the orientation errors converge into a neighborhood near the origin. To be more robust to disturbances including external noises and unmodeled time-varying sliding components, the adaptive controller is refined by integrating Variable Structure Controllers (VSC) or projection mappings. Simulation results show that the proposed robust adaptive controllers can reject sliding effects and guarantee high path-following accuracy.
Cited literature [23 references]
https://hal.inria.fr/hal-02467172
Contributor : Philippe Martinet <>
Submitted on : Wednesday, February 5, 2020 - 7:59:35 AM
Last modification on : Tuesday, April 20, 2021 - 11:20:06 AM
Long-term archiving on: : Wednesday, May 6, 2020 - 3:30:24 PM
Contributor : Philippe Martinet <>
Submitted on : Wednesday, February 5, 2020 - 7:59:35 AM
Last modification on : Tuesday, April 20, 2021 - 11:20:06 AM
Long-term archiving on: : Wednesday, May 6, 2020 - 3:30:24 PM
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