Neural Network Control of Underactuated Surface Vehicles With Prescribed Trajectory Tracking Performance

Fuzzy adaptive fault-tolerant control for an unmanned surface vehicle with prescribed tracking performance

Unmanned surface vehicles (USVs), as a type of marine robotic systems, are widely used in various applications such as maritime surveillance, environmental monitoring, and cargo transportation. This article addresses the trajectory tracking control issue for an USV subject to model uncertainties and actuator faults. A logarithm barrier Lyapunov functions based predefined tracking control scheme is proposed to regulate the position error of the USV into predefined performance region. Then, to ensure the predefined transient and steady state tracking performance of the USV in the presence of actuator faults, we propose an adaptive fuzzy fault-tolerant controller to address the actuator faults. Additionally, to deal with the uncertainties arising from the USV system model, fuzzy logic systems are utilized to estimate the unknown hydrodynamic parameters. Based on the Lyapunov stability criterion, it can be demonstrated that all the closed-loop signals are bounded. Finally, the validity of the developed control scheme is demonstrated from simulation results.

H∞ controller design for Nabla discrete fractional-order systems

This article aims to provide several criteria with sufficient conditions for Nabla discrete fractional-order systems to satisfy a certain H∞ performance index. Firstly, the instability region is approximated by a series of union sets of fan-shaped domains, and the mathematical expression of each fan-shaped domain is given. Secondly, by using the above approximation method and the generalized Kalman-Yakubovič-Popov lemma, some criteria with linear matrix inequality (LMI) formulations for the boundedness of the H∞ norm of the transfer function matrix for Nabla discrete FOSs are derived. Thirdly, an LMI-based H∞ state feedback controller design method is provided by using the projection lemma, which is aimed to stabilize the system and optimize its H∞ performance index simultaneously. Finally, the effectiveness of the proposed approach is verified by the accurate numerical solutions and system state responses obtained from two simulation examples.

Angle Rigidity-Based Communication-Free Adaptive Formation Control for Nonlinear Multiagent Systems With Prescribed Performance

Angle-constrained formation control has garnered significant attention owing to the advantage of interedge angles invariant under translation, rotation, and scaling. However, most existing approaches addressing this problem are applicable only to single- or double-integrator dynamics, which are often impractical in real-world scenarios. In this article, an angle rigidity-based adaptive formation control framework is introduced for nonlinear multiagent systems subject to mismatched uncertainties. The proposed control framework integrates a prescribed performance control approach with a recursive backstepping procedure, offering several key advantages: the capability to handle unmatched system uncertainties, the preservation of angle rigidity throughout the formation process, and the assurance that the triangulated formation shape is asymptotically achieved without risking collisions between neighboring agents. Furthermore, since the control input of each agent only requires local information related to its neighbors, which can be obtained locally from its own sensors, the proposed control method can be deployed in a communication-free environment. The effectiveness of the proposed control algorithms is validated by extensive numerical simulation.

Low-Complexity Tracking Control of Unknown Strict-Feedback Systems With Quantitative Performance Guarantees

This article is concerned with the prescribed performance tracking control problem for the strict-feedback systems with unknown nonlinearities and unmatched disturbances. The challenge lies in the realization of a complete performance specification for trajectory tracking in the sense of quantitatively regulating the peak value, overshoot, settling time, and accuracy while ensuring that the initial condition holds naturally. To this end, an error transformation, equipped with a shifting function, is introduced and incorporated with a new-type barrier function. Then, a class of performance functions is exploited to quantify the settling times and steady-state bounds of the intermediate errors. Moreover, to improve the flexibility of formulating performance specifications for the tracking error, a pair of asymmetric performance boundaries are further designed. With their combination, a novel robust prescribed performance control (PPC) approach is proposed in this article. It not only achieves the quantitative performance guarantees but also preserves the unique simplicity of PPC, evading the needs for function approximation, parameter identification, disturbance estimation, derivative calculation, or command filtering. The above theoretical findings are confirmed via three simulation studies.

Convolutional Neural Network-Based Lane-Change Strategy via Motion Image Representation for Automated and Connected Vehicles

The lane-change decision-making module of automated and connected vehicles (ACVs) is one of the most crucial and challenging issues to be addressed. Motivated by human beings' underlying driving paradigm and the convolutional neural network's (CNN) dramatic capability of extracting features and learning strategies, this article proposes a CNN-based lane-change decision-making method via the dynamic motion image representation. Human drivers take proper driving maneuvers after they subconsciously construct the dynamic traffic scene representation in their brains, so this study first proposes the dynamic motion image representation method to reveal informative traffic situations in the motion-sensitive area (MSA), which provides a full view of surrounding cars. Then, this article develops a CNN model to extract the underlying features and learn driving policies from labeled datasets of MSA motion images. Besides, a safety-constrained layer is added to avoid vehicle collisions. We build a simulation platform based on the simulation of urban mobility (SUMO) to collect traffic datasets and test our proposed method. In addition, real-world traffic datasets are also involved to further investigate the proposed method's performance. The rule-based strategy and reinforcement learning (RL)-based method are used to compare with our approach. All results demonstrate that the proposed method performs lane-change decision-making much better than prevailing methods, which suggests our scheme has huge potential to accelerate the deployment of ACVs and is worth further study.

Double-Branch Multi-Scale Contextual Network: A Model for Multi-Scale Street Tree Segmentation in High-Resolution Remote Sensing Images

Street trees are of great importance to urban green spaces. Quick and accurate segmentation of street trees from high-resolution remote sensing images is of great significance in urban green space management. However, traditional segmentation methods can easily miss some targets because of the different sizes of street trees. To solve this problem, we propose the Double-Branch Multi-Scale Contextual Network (DB-MSC Net), which has two branches and a Multi-Scale Contextual (MSC) block in the encoder. The MSC block combines parallel dilated convolutional layers and transformer blocks to enhance the network's multi-scale feature extraction ability. A channel attention mechanism (CAM) is added to the decoder to assign weights to features from RGB images and the normalized difference vegetation index (NDVI). We proposed a benchmark dataset to test the improvement of our network. Experimental research showed that the DB-MSC Net demonstrated good performance compared with typical methods like Unet, HRnet, SETR and recent methods. The overall accuracy (OA) was improved by at least 0.16% and the mean intersection over union was improved by at least 1.13%. The model's segmentation accuracy meets the requirements of urban green space management.