Experiments on various datasets, incorporating diverse nuisances and modalities, involving feature matching, 3D point cloud registration, and 3D object recognition, demonstrate that the MV approach is remarkably resilient to substantial outliers under demanding conditions, leading to substantial improvements in 3D point cloud registration and 3D object recognition accuracy. The GitHub repository, containing the code, is located at this address: https://github.com/NWPU-YJQ-3DV/2022. A vote with mutual support.

Markovian jump logical control networks (MJLCNs)' event-triggered set stabilizability is analyzed in this technical paper, which employs Lyapunov theory. While the current evaluation of MJLCNs' set stabilizability proves sufficient, this technical paper provides the critical necessary and sufficient conditions for confirmation. A Lyapunov function, encompassing recurrent switching modes and the desired state set, is employed to establish, in a necessary and sufficient manner, the set stabilizability properties of MJLCNs. Following that, the triggering condition and the method for updating input values are established with consideration for changes in the Lyapunov function. Finally, the practical application of theoretical results is exemplified by the biological phenomenon of the lac operon in the bacterium Escherichia coli.

Articulating cranes (ACs) are employed across a spectrum of industrial operations. The articulated multi-section arm contributes to the presence of nonlinearities and uncertainties, consequently making precise tracking control a considerable challenge. For AC systems, this study introduces an adaptive prescribed performance tracking control (APPTC) method, enabling robust and precise tracking control by adapting to time-varying uncertainties, the unknown bounds of which are defined within prescribed fuzzy sets. Specifically, a state transformation procedure is used to track the desired trajectory and concurrently satisfy the required performance. APPTC's utilization of fuzzy set theory to portray uncertainties obviates the need for IF-THEN fuzzy rules. APPTC's approximation-free characteristic stems from the non-existence of linearizations or nonlinear cancellations. A dual effect is observable in the controlled AC's performance. Bak protein Deterministic performance in the fulfillment of the control task is assured through Lyapunov analysis, using the concepts of uniform boundedness and uniform ultimate boundedness. The second enhancement to fuzzy-based performance comes from an optimal design, that locates the best control parameters via the establishment of a two-player Nash game. While the existence of Nash equilibrium is theoretically validated, its acquisition process is also expounded. The results of the simulation are offered for validation. An initial investigation into precise tracking control for fuzzy alternating current systems is presented in this work.

A switching anti-windup approach is presented in this article for linear, time-invariant (LTI) systems under the constraints of asymmetric actuator saturation and L2-disturbances. This approach's core idea is to completely utilize the control input range by switching among different anti-windup gains. The asymmetrically saturated linear time-invariant system undergoes a transformation into a switched system comprising symmetrically saturated subsystems. Switching between distinct anti-windup gains is regulated by a dwell time rule. Sufficient conditions guaranteeing regional stability and weighted L2 performance of the closed-loop system are established via the utilization of multiple Lyapunov functions. The anti-windup switching synthesis, wherein a unique anti-windup gain is allocated to each subsystem, is cast within a convex optimization problem's structure. The switching anti-windup design presented here, in contrast to a single anti-windup gain approach, produces less conservative results by fully exploiting the asymmetric character of the saturation constraint. Two numerical examples, along with an aeroengine control application (experiments conducted on a semi-physical testbed), highlight the proposed scheme's substantial practicality and superior performance.

Event-triggered dynamic output feedback controller design for Takagi-Sugeno fuzzy systems subject to actuator failures and deception attacks in networked systems is the subject of this article. Organic bioelectronics Two event-triggered schemes (ETSs) are proposed to investigate the transmission of measurement outputs and control inputs under the constraints of network communication resources. Though the ETS yields advantages, it simultaneously causes a discrepancy between the system's initial parameters and the controller's actions. This problem necessitates an asynchronous premise reconstruction method to address the limitations imposed by the previous requirement of synchronous plant and controller premises. Subsequently, two crucial factors, actuator failure and deception attacks, are accounted for simultaneously. Employing the Lyapunov stability theorem, the mean square asymptotic stability conditions of the augmented system are then determined. Additionally, controller gains and event-triggered parameters are co-created through the application of linear matrix inequality techniques. Ultimately, a cart-damper-spring system and a nonlinear mass-spring-damper mechanical system are showcased to validate the theoretical examination.

Least squares (LS) methodology is a widely used and highly popular approach for linear regression analysis, capable of solving systems that are critically, over, or under-determined. Linear regression analysis is easily implemented for tasks of linear estimation and equalization in signal processing applications, especially within cybernetics. Yet, the current linear regression technique employing least squares (LS) is unfortunately restricted by the dimensionality of the dataset; in essence, the precise least squares solution solely depends on the data matrix. As datasets expand in dimension, demanding tensorial representation, an exact tensor-based least squares (TLS) solution is unavailable, owing to the absence of an appropriate mathematical structure. Some alternative techniques, including tensor decomposition and tensor unfolding, have been devised to approximate Total Least Squares (TLS) solutions within the context of linear regression problems utilizing tensor data. Nevertheless, these approaches are unable to produce the accurate or precise TLS solution. This work endeavors to pioneer a novel mathematical framework for precisely solving TLS problems encompassing tensor data. The practicality of our novel approach in the context of machine learning and robust speech recognition is highlighted through numerical experiments, which also assess the associated memory and computational overhead.

Path-following of underactuated surface vehicles (USVs) is addressed in this article through the development of continuous and periodic event-triggered sliding-mode control (SMC) algorithms. Based on the principles of SMC, a control law for continuous path-following is engineered. Path following by unmanned surface vessels (USVs) now has its upper quasi-sliding mode boundaries definitively established for the first time. The proposed continuous Supervisory Control and Monitoring (SCM) system subsequently incorporates both continuous and periodic event-triggering mechanisms. When employing event-triggered mechanisms and selecting appropriate control parameters, hyperbolic tangent functions demonstrably do not affect the boundary layer of the quasi-sliding mode. By employing continuous and periodic event-triggered SMC strategies, the sliding variables are guaranteed to reach and maintain quasi-sliding modes. On top of that, energy consumption can be reduced. Methodical stability analysis confirms the USV's ability to adhere to the designated reference path. Simulation results affirm the effectiveness of the proposed control methodologies.

This paper explores the resilient practical cooperative output regulation problem (RPCORP) in multi-agent systems, specifically regarding the effects of denial-of-service attacks and actuator faults. This system, fundamentally different from existing RPCORP solutions, considers unknown system parameters for each agent, leading to the introduction of a novel data-driven control method. Resilient distributed observers for each follower, strategically designed to counter DoS attacks, represent the solution's starting point. Then, a highly resilient communication approach and a variable sampling timeframe are implemented to guarantee immediate access to the neighbor's state upon the end of attacks, and to circumvent planned attacks launched by sophisticated attackers. Furthermore, a model-based controller, resistant to faults and resilient to disturbances, is constructed using Lyapunov's stability theorem and the principles of output regulation. To decouple controller parameter determination from system parameters, we've devised a novel data-driven algorithm trained on accumulated data. The rigorous analysis reveals the closed-loop system's ability to achieve resilient practical cooperative output regulation. To conclude, a simulation example is utilized to exemplify the strength of the findings.

We intend to create and assess a magnetic resonance imaging (MRI)-conditional concentric tube robot for extracting blood clots from intracerebral hemorrhages.
Utilizing plastic tubes and bespoke pneumatic motors, we constructed the concentric tube robot hardware. The kinematic model of the robot was developed employing a discretized piece-wise constant curvature (D-PCC) approach, specifically tailored to capture the variable curvature of the tube. Tube mechanics modeling, incorporating friction, were further included to address the torsional deflection of the inner tube. MR-safe pneumatic motors were controlled via a variable gain PID algorithm. inappropriate antibiotic therapy Rigorous bench-top and MRI experiments confirmed the robot hardware, culminating in MR-guided phantom trials that evaluated the robot's evacuation effectiveness.
Using a variable gain PID control algorithm, the pneumatic motor's rotational accuracy was precisely 0.032030. The positional accuracy of the tube tip, as determined by the kinematic model, reached 139054 mm.