3D path-following control of robotic penguin: an ETFLMPC approach

Pan, Jie; Li, Hongfei; Zhou, Ziye; Yu, Junzhi

doi:10.1007/s11071-021-06365-8

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…In order to accomplish above tasks, fast and efficient underwater path following control has crucial effects. Compared with traditional propeller-propulsion autonomous underwater vehicles (AUVs), bionic underwater vehicles inspired by biology have the advantages of superior efficient and maneuverable performances [1][2][3][4][5]. To this end, bionic underwater vehicles are more suitable for performing complex underwater tasks compared with traditional AUVs.…”

Section: Introductionmentioning

confidence: 99%

Steering characteristics and path following control of a bionic underwater vehicle with multiple locomotion modes

Wang,

Zhou,

Wang

et al. 2024

Preprint

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The research on the path following control of the bionic underwater vehicle with high maneuverability and propulsive performance is a crucial issue. This paper investigates the planer path following control task of a bionic underwater vehicle with superior maneuverability. The steering characteristics of the sinusoidal offset and unilateral asymmetric steering signals are analyzed. The experimental results demonstrate that the two signals separately have smaller steering radius and power consumption in the low-frequency and relatively high-frequency range, and are consequently suitable for distinct scenarios. Furthermore, a switching nonlinear model predictive control strategy is proposed, which regulates the state error weighting values according to the real-time yaw angle error. The control strategy realizes autonomous switching of multiple locomotion modes to enhance the swimming speed of the bionic underwater vehicle and fulfills the purpose of improving the task-completing efficiency. The simulation and experimental results indicate that the bionic underwater vehicle achieved 3.49 and 2.92 times enhancement in swimming speed performance at straight and steering target path following control tasks, respectively. The proposed methods as well as obtained results can provide universal inspiration for the multiple motion-based path following control of autonomous underwater vehicles.

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Section: Introductionmentioning

confidence: 99%

Steering characteristics and path following control of a bionic underwater vehicle with multiple locomotion modes

Wang,

Zhou,

Wang

et al. 2024

Preprint

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Adaptive path-following control for parafoil dynamic systems with wind disturbance and rate constraint

Guo

Xing

et al. 2023

Nonlinear Dyn

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Koopman operator-based multi-model for predictive control

Ławryńczuk

2024

Nonlinear Dyn

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This work describes a new model structure developed for prediction in Model Predictive Control (MPC). The model has a multi-model structure in which independent sub-models are employed for the consecutive sampling instants. The model lifts process states into a high-dimensional space in which a linear process description is applied. Depending on the influence of the manipulated variables on lifted states, three general model versions are described and model identification algorithms are derived. As a result of the multi-model structure, model parameters are found analytically from computationally uncomplicated least squares problems using the Extended Dynamic Mode Decomposition algorithm, but the evolution of states over the horizon used in MPC is taken into account. Next, the MPC algorithm for the described model is derived. It requires solving online simple quadratic optimisation tasks. The effectiveness of three considered model configurations and three versions of the lifting functions is examined for a nonlinear DC motor benchmark. Their impact on model accuracy, complexity, possible control accuracy and MPC calculation time is thoroughly discussed. Finally, a more complex polymerisation reactor process is considered to showcase the practical applicability of the presented approach to modelling and MPC.

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3D path-following control of robotic penguin: an ETFLMPC approach

Cited by 5 publications

References 30 publications

Steering characteristics and path following control of a bionic underwater vehicle with multiple locomotion modes

Steering characteristics and path following control of a bionic underwater vehicle with multiple locomotion modes

Adaptive path-following control for parafoil dynamic systems with wind disturbance and rate constraint

Koopman operator-based multi-model for predictive control

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