MV's effectiveness in handling substantial outliers within the context of feature matching, 3D point cloud registration, and 3D object recognition is confirmed by trials on diverse datasets with varying modalities and complexities. This methodology convincingly bolsters the performance of 3D point cloud registration and 3D object recognition. The code can be downloaded from the GitHub repository: https://github.com/NWPU-YJQ-3DV/2022. Mutual votes cast for one another.
Employing Lyapunov theory, this technical paper characterizes the stabilizability of event-triggered Markovian jump logical control networks (MJLCNs). Currently, adequate but not comprehensive criteria for examining the set stabilizability of MJLCNs are in place. This technical paper provides the necessary and sufficient conditions for complete understanding. The establishment of MJLCNs' set stabilizability, using a Lyapunov function, necessitates and suffices the combination of recurrent switching modes and the desired state set. The value shifts within the Lyapunov function serve as the foundation for establishing the triggering condition and the mechanism for input updates. Ultimately, the efficacy of theoretical findings is showcased through a biological illustration involving the lac operon system in Escherichia coli.
In industrial settings, the articulating crane (AC) is a valuable piece of equipment. The multi-sectioned, articulated arm amplifies nonlinearities and uncertainties, thereby posing a significant obstacle to precise tracking control. In this study, an adaptive prescribed performance tracking control (APPTC) for AC systems is formulated to ensure robust and precise tracking control, exhibiting adaptation to time-variant uncertainties, with unknown bounds lying within prescribed fuzzy sets. To both monitor the desired trajectory and meet the stipulated performance, a state transformation is utilized. APPTC's approach to characterizing uncertainties, grounded in fuzzy set theory, does not involve the application of IF-THEN fuzzy rules. The absence of linearizations and nonlinear cancellations in APPTC ensures its approximation-free nature. A dual effect is observable in the controlled AC's performance. GDC-0068 purchase The Lyapunov analysis, utilizing uniform boundedness and uniform ultimate boundedness, provides a means for assuring the deterministic performance in the control task. Secondly, fuzzy-based performance enhancement is achieved through an optimized design, which locates optimal control parameters via a two-player Nash game formulation. It has been proven in theory that Nash equilibrium exists, and the process of finding it has been explained. The simulation results are presented for the purpose of validation. This first attempt to explore fuzzy alternating current systems centers on the precise tracking control.
This article develops a switching anti-windup strategy applicable to linear, time-invariant (LTI) systems with asymmetric actuator saturation and L2-disturbances. The method relies on switching among multiple anti-windup gains to fully exploit the range of available control inputs. The asymmetrically saturated linear time-invariant system is remodeled into a switched system composed of symmetrically saturated subsystems. A dwell time-based switching rule governs the selection of distinct anti-windup gains. From multiple Lyapunov functions, we deduce sufficient conditions that ensure the regional stability and weighted L2 performance of the closed-loop system. A convex optimization framework is used to design a separate anti-windup gain for each subsystem in the switching anti-windup synthesis. Compared to a single anti-windup gain design, our approach yields less conservative outcomes by leveraging the asymmetric nature of the saturation constraint within the switching anti-windup scheme. Two numerical illustrations, in conjunction with an aeroengine control application (experiments performed on a semi-physical test bench), validate the proposed scheme's superiority and practical implementation.
This article investigates the design of dynamic output feedback controllers for networked Takagi-Sugeno fuzzy systems, taking into account the challenges posed by actuator failures and deception attacks, and employing event-triggered mechanisms. Enzyme Inhibitors Two event-triggered schemes (ETSs) are developed to test the transmission of measurement outputs and control inputs when network communication is active, thereby saving network resources. While the ETS presents advantages, it simultaneously leads to a disconnect between the system's underlying variables and the controlling element. To overcome this problem, a strategy for reconstructing asynchronous premises is explored, which modifies the prior requirement for simultaneous premises between the plant and the controller. In addition, dual consideration is given to two essential factors: actuator failure and deception attacks. The augmented system's mean square asymptotic stability is shown through the application of the Lyapunov stability theorem. Furthermore, a co-design approach for controller gains and event-triggered parameters utilizes linear matrix inequality techniques. Lastly, a demonstration of a cart-damper-spring system and a nonlinear mass-spring-damper mechanical system is presented to confirm the theoretical analysis.
The method of least squares (LS) is a popular and widely adopted technique for linear regression analysis that has the ability to solve any critically, over, or under-determined system of equations. Cybernetic signal processing often utilizes linear regression analysis for effective linear estimation and equalization. Nevertheless, the existing Least Squares (LS) linear regression method unfortunately has a limitation determined by the dataset's dimensionality; this means that an exact LS solution is contingent on the data matrix itself. The burgeoning size of data sets, necessitating tensorial depictions, prevents the development of an exact tensor-based least squares (TLS) solution, due to the absence of a corresponding mathematical framework. Alternative methods, such as tensor decomposition and tensor unfolding, have been proposed to approximate Total Least Squares (TLS) solutions for linear regression problems with tensor-based data, yet these methods are unable to achieve a completely accurate or definitive TLS solution. To tackle the precise calculation of TLS solutions in tensor data, a novel mathematical framework is introduced in this work for the first time. Our proposed scheme's effectiveness in machine learning and robust speech recognition is demonstrated through numerical experiments, alongside a thorough exploration of the resulting memory and computational requirements.
Continuous and periodic event-triggered sliding-mode control (SMC) algorithms, developed in this article, enable path tracking by underactuated surface vehicles (USVs). A continuous path-following control law is developed, leveraging the capabilities of SMC technology. The path-following trajectories of unmanned surface vessels (USVs) have their upper quasi-sliding mode limits defined for the first time. Subsequently, the continuous Supervisory Control and Monitoring (SCM) architecture is extended to accommodate both ongoing and periodically occurring events. Empirical evidence suggests that the boundary layer of a quasi-sliding mode, originating from event-triggered mechanisms, is unaffected by the implementation of hyperbolic tangent functions, when control parameters are selected appropriately. Employing continuous and periodic event-triggered SMC, the system guarantees the sliding variables' transition to and persistence within quasi-sliding modes. In addition, energy usage can be decreased. Methodical stability analysis confirms the USV's ability to adhere to the designated reference path. The simulation results strongly suggest the effectiveness of the suggested control methods.
Cooperative output regulation, resilient to denial-of-service attacks and actuator faults, is the focus of this multi-agent systems article. Departing from conventional RPCORP solutions, the system parameters in this work are agent-unknown, motivating a novel data-driven control mechanism. In order to initiate the solution, the development of resilient distributed observers for each follower becomes necessary to counter DoS attacks. Following this, a resilient communication protocol and a variable sampling interval are introduced to guarantee the immediate availability of neighboring states after attacks subside and to thwart targeted assaults initiated by intelligent attackers. Based on Lyapunov's method and output regulation, a model-based fault-tolerant and resilient controller is constructed. We've developed a data-driven algorithm to learn controller parameters from the gathered data, thereby reducing reliance on system parameter specifications. A rigorous examination demonstrates the closed-loop system's ability to achieve practical cooperative output regulation in a resilient manner. To exemplify the impact of the results, a simulated experiment is presented ultimately.
We are committed to developing and rigorously evaluating a concentric tube robot, whose operation is dependent on MRI data, for the purpose of evacuating intracerebral hemorrhages.
Plastic tubes and customized pneumatic motors formed the foundation of our concentric tube robot hardware fabrication. Utilizing a discretized piece-wise constant curvature (D-PCC) method, a kinematic model for the robot was created, enabling representation of variable curvature along the tube's shape. This was complemented by tube mechanics modeling, factoring in friction, to simulate the torsional deflection of the inner tube. The MR-safe pneumatic motors' operation was directed by a variable gain PID algorithm. sport and exercise medicine Systematic bench-top and MRI tests confirmed the robot hardware's functionality, and MR-guided phantom trials further tested the robot's evacuation performance.
Employing a variable gain PID control algorithm, the pneumatic motor demonstrated a rotational accuracy of 0.032030. The tube tip's positional accuracy, as calculated by the kinematic model, amounted to 139054 mm.