Dong, Z;
Lu, S;
Hu, Z;
Liu, W;
Ding, Y;
Liu, Y;
(2026)
A novel digital twin model based bio-heuristic sliding mode control algorithm for trajectory tracking control of USV in the presence of complex marine environment disturbance.
Robotics and Autonomous Systems
, 195
, Article 105219. 10.1016/j.robot.2025.105219.
(In press).
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Abstract
The trajectory tracking control problem of unmanned surface vessel (USV) under the complex marine environmental disturbance is discussed in this paper, and a novel digital twin model based bio-heuristic sliding mode control (SMC) algorithm integrated with a radial basis function neural network (RBFNN) disturbance compensation module is proposed. An adaptive forgetting factor, which varies with the prediction errors of state variables, is introduced and integrated into the recursive least squares (RLS) algorithm. Meanwhile, a digital twin model of USV is established by applying the proposed adaptive forgetting factor recursive least squares (AFF-RLS) algorithm, and utilizing state variable data and control commands. An improved bio-heuristic approximation function is presented to approach the virtual velocity control laws, avoiding abrupt change and jittering of the SMC algorithm designed based on the digital twin model. Set the angular and linear velocity variables as inputs, environment disturbances and modeling errors compensation as outputs, the RBFNN based integrated control compensator is presented, where the minimum parameter learning method is implemented by replacing the adjustment of neural network weights with parameter estimation to reduce redundant parameters. The effectiveness of the proposed algorithm is validated through extensive simulation experiments, demonstrating its robustness in real marine environment disturbance.
| Type: | Article |
|---|---|
| Title: | A novel digital twin model based bio-heuristic sliding mode control algorithm for trajectory tracking control of USV in the presence of complex marine environment disturbance |
| DOI: | 10.1016/j.robot.2025.105219 |
| Publisher version: | https://doi.org/10.1016/j.robot.2025.105219 |
| Language: | English |
| Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.) |
| Keywords: | Unmanned surface vessel (USV), Trajectory tracking control, Adaptive forgetting factor recursive least square (AFF RLS), Digital twin model, Sliding mode control (SMC), Radial basis function neural network (RBFNN) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10217282 |
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