Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

A Supervised Neural Network Control for Magnetorheological Damper in an Aircraft Landing Gear

This paper adopts an intelligent controller based on supervised neural network control for a magnetorheological (MR) damper in an aircraft landing gear. An MR damper is a device capable of adjusting the damping force by changing the magnetic field generated in electric coils. Applying an MR damper to the landing gears of an aircraft could minimize the impact at landing and increase the impact absorption efficiency. Various techniques proposed for controlling the MR damper in aircraft landing gears require information on the damper force or the mass of the aircraft to determine optimal parameters and control commands. This information is obtained by estimation with a model in a practical operating environment, and the accompanying inaccuracies cause performance degradation. Machine learning-based controllers have also been proposed to address model dependency but require a large number of drop test data. Unlike simulations, which can conduct a large number of virtual drop tests, the cost and time are limited in the actual experimental environment. Therefore, a neural network controller with supervised learning is proposed in this paper to simulate the behavior of a proven controller only with system states. The experimental data generated by applying the hybrid controller with the exact mass and force information, which has demonstrated high performance among the existing techniques, are set as the target for supervised learning. To verify the effectiveness of the proposed controller, drop test experiments using the intelligent controller and the hybrid controller with and without exact information about aircraft mass and force are executed. The experimental results from the drop tests of a landing gear show that the proposed controller maintains superior performance to the hybrid controller without using explicit damper models or any information on the aircraft mass or strut force.

Steering angle prediction YOLOv5-based end-to-end adaptive neural network control for autonomous vehicles

The combination of steering angle prediction and control of autonomous vehicles (AVs) is a challenging task. To improve the real-time steering angle prediction accuracy and the effectiveness of steering control, a novel steering angle prediction YOLOv5-based end-to-end adaptive neural network control for AVs is proposed. Firstly, since most of the lane line datasets are simulated images and lack of diversity, a novel lane dataset derived from the real roads are made manually to train the You Only Look Once version 5 (YOLOv5) network model. To improve the detection accuracy of the network model, the Generalized Intersection over Union (GIoU) of the bounding box regression loss function is updated to a Complete Intersection over Union (CIoU) with a better convergence effect. Furthermore, the neural network-based controller and disturbance observer are proposed to effectively control the steering angle predicted by YOLOv5 and estimate the lumped uncertainty. Meanwhile, a composite adaptive updating law is constructed by utilizing the tracking error and modeling error to improve steering performance. Finally, the system stability is proved by Lyapunov theory and the effectiveness of the proposed method is verified with experiments.