다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

This article aims to develop an effective control method that can improve the convergence rate over the existing adaptive nonsingular integral terminal sliding mode control (ANITSMC) method for the trajectory tracking control of autonomous underwater vehicles (AUVs). To achieve this goal, an adaptive fast nonsingular integral terminal sliding mode control (AFNITSMC) method is proposed. First, considering that the existing nonsingular integral terminal sliding mode (NITSM) has slow convergence rate in the region far from the equilibrium point, a fast NITSM (FNITSM) is proposed, which guarantees fast transient convergence both at a distance from and at a close range of the equilibrium point, and therefore increases the convergence rate over the existing NITSM. Then, using this FNITSM and adaptive technique, an AFNITSMC method is designed for AUVs. It yields local finite-time convergence of the velocity tracking errors to zero and then local exponential convergence of the position tracking errors to zero, without requiring any a priori knowledge of the upper bounds of the uncertainties and disturbances. Compared with the existing ANITSMC method, the salient feature of the proposed AFNITSMC method is that it provides AUV dynamics a faster convergence rate. Finally, simulation results demonstrate the efficiency of the proposed AFNITSMC method and its superiority over the existing ANITSMC method.

Trajectory Tracking Control of AUVs via Adaptive Fast Nonsingular Integral Terminal Sliding Mode Control

This paper investigates the leader–follower formation problem of multiple underactuated autonomous surface vessels in the presence of model uncertainties and environmental disturbances. Specially, the formation is defined in the body-fixed coordinates of the leader vessel and velocities of the leader are unavailable to followers. A novel robust adaptive formation control scheme based on the minimal learning parameter (MLP) algorithm and the disturbance observer (DOB) is presented. To address related formation configurations and unknown velocities of the leader, adaptive programming of the virtual vessel is introduced. By the neural networks (NNs) technique, the DOB is constructed and the formation controller is developed with different MLP-based adaptive laws. Under the proposed controller, it is shown that the desired formation can be achieved only with the position and yaw angle of the leader. And formation errors are guaranteed to be semiglobal uniformly ultimately bounded. Compared with existing results, the NNs-based DOB can compensate disturbances effectively with less model information. Meanwhile, the formation controller and the DOB can share the same set of NNs with smaller computational effort, where only two parameters need to be learned online for each of them. Simulations and comparison results are provided to illustrate the effectiveness of theoretical results.

Robust adaptive formation control of underactuated autonomous surface vessels based on MLP and DOB

A path planning strategy unified with a COLREGS collision avoidance function based on deep reinforcement learning and artificial potential field

In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

Design of three exponentially convergent robust controllers for the trajectory tracking of autonomous underwater vehicles

Vessels with a dynamic positioning system (DPS) are widely applied in ocean resource exploration. Because of the inaccuracy and coupling of the vessel dynamic model, it is important to design a controller that performs well in an oceanic environment. The active disturbance rejection controller (ADRC) is introduced in this study to control the vessel movement and positioning in the DPS. The merit of the ADRC is that it does not need an accurate plant and disturbance model. In the proposed method, an adaptive hybrid biogeography-based optimization (BBO) and differential evolution (DE) are developed. The orthogonal learning (OL) mechanism is employed to achieve adaptive switching to different searching mechanisms between BBO and DE. The proposed adaptive hybrid BBO-DE (AHBBODE) algorithm is then used to optimize the parameters of ADRC; these parameters are not easy to determine by using the trial and error method. Finally, the proposed method is compared with the BBO- and DE-based methods. The results show that better performance is obtained by the proposed method.

Active disturbance rejection controller design for dynamically positioned vessels based on adaptive hybrid biogeography-based optimization and differential evolution.

Defeng Wu

Papers

A path planning strategy unified with a COLREGS collision avoidance function based on deep reinforcement learning and artificial potential field

Design of three exponentially convergent robust controllers for the trajectory tracking of autonomous underwater vehicles

Active disturbance rejection controller design for dynamically positioned vessels based on adaptive hybrid biogeography-based optimization and differential evolution.

An energy optimal thrust allocation method for the marine dynamic positioning system based on adaptive hybrid artificial bee colony algorithm

Nonsingular fixed-time terminal sliding mode trajectory tracking control for marine surface vessels with anti-disturbances