Small Neighborhood Research Articles

A Novel Bipartite Consensus Tracking Control for Multiagent Systems Under Sensor Deception Attacks.

This article presents a novel adaptive bipartite consensus tracking strategy for multiagent systems (MASs) under sensor deception attacks. The fundamental design philosophy is to develop a hierarchical algorithm based on shortest route technology that recasts the bipartite consensus tracking problem for MASs into the tracking problem for a single agent and eliminates the need for any global information of the Laplacian matrix. As the sensors suffer from malicious deception attacks, the states cannot be measured accurately, we thus construct a novel dynamic estimator to estimate the actual states, which, together with a new coordinate transformation involving the attacked and estimated state variables, allows a distributed security control scheme to be developed, in which the singularity of the adaptive iterative process involved in existing works is completely avoided. Furthermore, the Nussbaum functions are included in the controller to account for the influence of the unknown control gains caused by sensor deception attacks. It is shown that the distributed consensus tracking errors converge to a small neighborhood of the origin, and all the signals in the closed-loop system remain bounded. Simulation on a forced damped pendulums (FDPs) is conducted to demonstrate and verify the effectiveness of the proposed strategy.

Convex Temporal Convolutional Network-Based Distributed Cooperative Learning Control for Multiagent Systems.

Due to its great efficiency, scalability, and inclusivity, distributed cooperative learning control has gotten a lot of attention. For complex uncertain multiagent systems, it is challenging to model the uncertainties and exploit the cooperative learning ability of the systems. To address these issues, we proposed a novel convex temporal convolutional network-based distributed cooperative learning control for uncertain discrete-time nonlinear multiagent systems. A new concept of using a convex temporal convolutional network (CTCNet) is proposed for estimating the uncertain agent dynamics in a cooperative way. Unlike previous methods that require adjustment of network weights for different control tasks, the proposed CTCNet can map the high-dimensional input-output space into a deep space spanned by basis features that represent the inherent properties of the system, so it has good robustness for different tasks. Consequently, to improve the control performance, a CTCNet-based distributed cooperative learning control method that shares learned knowledge through the communication topology among adaptive laws of CTCNet is proposed. Furthermore, the asymptotic convergence of system tracking errors to an arbitrarily small neighborhood of the origin is strictly proved. Finally, the simulation results are given to illustrate that our suggested method has higher control accuracy, stronger robustness, and anti-interference ability than the existing methods.

Cooperative robust output regulation of linear uncertain multi‐agent systems: A dynamic event‐triggered approach

AbstractIn the article, the cooperative robust output regulation problem of multi‐agent systems with general linear uncertain dynamics is investigated by event‐triggered control. Recently, the similar problem is solved practically by a distributed event‐triggered control law where the tracking error of the system tends to some small neighborhood of the origin asymptotically. In comparison, we develop a distributed event‐triggered output feedback control law together with a class of dynamic event‐triggered mechanisms to solve this problem exactly. In particular, we first design a class of dynamic event‐triggered mechanisms. Then, based on the internal model principle, we propose a distributed event‐triggered output feedback control law. By rigorous proofs, we show that under the proposed distributed control law together with the dynamic event‐triggered mechanism, the cooperative robust output regulation problem is solved exactly, which means that the tracking errors of all followers converge to zero asymptotically while prohibiting the Zeno behavior. Finally, we use an example to illustrate our control approach.

Thermodynamic Quantities of Morse Fluids in the Supercritical Region

The critical point parameters for liquid alkali metals (sodium and potassium) are calculated accounting for the non-Gaussian order parameter fluctuations and the Morse interaction potential. The behavior of the isothermal compressibility, density fluctuations, and thermal expansion for sodium is studied in the supercritical temperature region. A significant increase in the isothermal compressibility and the density fluctuations near the critical point indicates a substantial density sensitivity to tiny pressure fluctuations. The thermal expansion coefficient for various fixed pressure values shows a typical gas decrease with increasing supercritical temperature. The Widom line separating the gaseous and liquid structures of the fluid at temperatures above the critical one is represented. Note that our calculations are valid in a small neighborhood of the critical point, which is problematic for theoretical and experimental studies.

Finite-Time Adaptive Dynamic Surface Asymptotic Tracking Control of Uncertain Multi-Agent Systems with Unknown Control Gains

In this work, the finite-time asymptotic tracking control problem of uncertain multi-agent systems with unknown control gains is studied. For the unknown control gain of each subsystem in multi-agent systems, we consider using the Nussbaum gain function techniques to handle them. To deal with the unknown uncertain nonlinear dynamics, the radial basis function neural network is introduced in each step of the dynamic surface control design. In addition, a nonlinear compensating term with the estimation of an unknown bounded parameter is designed to avoid repeated differentiation of each virtual control law. Then, based on the neural network control method, dynamic surface control technique, and finite-time control theory, an adaptive neural network finite-time dynamic surface control law is finally designed. Using stability analysis, it is proven that the presented adaptive control law can guarantee all signals of the closed-loop system semi-global practical finite-time stable, and the tracking error of each follower agent can converge to a small neighborhood of zero in finite time. Finally, a class of single-link robot systems is provided to illustrate the effectiveness of the designed control law.

Robust trajectory tracking control for cable-driven parallel robots with model uncertainty

This paper is concerned with robust trajectory tracking control for cable-driven parallel robots (CDPRs) with uncertain dynamics. A new accurate trajectory tracking controller (ATTC) is proposed such that the pose of the mobile platform of CDPRs can accurately track a desired trajectory. To further alleviate the input chattering phenomenon, a practical trajectory tracking controller (PTTC) is designed to guarantee the trajectory tracking error is converged to a small neighborhood of the origin. Finally, an illustrative example on a 3-Cable 3-DOF CDPR is provided to evaluate the performance of both ATTC and PTTC, including a comparison study against a typical synchronization controller, which verifies the effectiveness of the proposed controllers.

ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations

User-based KNN recommender systems ( UserKNN ) utilize the rating data of a target user’s k nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors, since the recommendations could expose the neighbors’ rating data to other users or malicious parties. To reduce this risk, existing work applies differential privacy by adding randomness to the neighbors’ ratings, which unfortunately reduces the accuracy of UserKNN . In this work, we introduce ReuseKNN , a novel differentially private KNN-based recommender system. The main idea is to identify small but highly reusable neighborhoods so that (i) only a minimal set of users requires protection with differential privacy and (ii) most users do not need to be protected with differential privacy since they are only rarely exploited as neighbors. In our experiments on five diverse datasets, we make two key observations. Firstly, ReuseKNN requires significantly smaller neighborhoods and, thus, fewer neighbors need to be protected with differential privacy compared with traditional UserKNN . Secondly, despite the small neighborhoods, ReuseKNN outperforms UserKNN and a fully differentially private approach in terms of accuracy. Overall, ReuseKNN leads to significantly less privacy risk for users than in the case of UserKNN .

Classification of Fish Species Using Multispectral Data from a Low-Cost Camera and Machine Learning

This work creates a fish species identification tool combining a low-cost, custom-made multispectral camera called MultiCam and a trained classification algorithm for application in the fishing industry. The objective is to assess, non-destructively and using reflectance spectroscopy, the possibility of classifying the spectra of small fish neighborhoods instead of the whole fish for situations where fish are not completely visible, and use the classification to estimate the percentage of each fish species captured. To the best of the authors’ knowledge, this is the first work to study this possibility. The multispectral imaging device records images from 10 horse mackerel, 10 Atlantic mackerel, and 30 sardines, the three most abundant fish species in Portugal. This results in 48,741 spectra of 5 × 5 pixel regions for analysis. The recording occurs in twelve wavelength bands from 390 nm to 970 nm. The bands correspond to filters with the peculiarity of being highpass to keep the camera cost low. Using a Teflon tape white reference is also relevant to control the overall cost. The tested machine learning algorithms are k-nearest neighbors, multilayer perceptrons, and support vector machines. In general, the results are better than random guessing. The best classification comes from support vector machines, with a balanced accuracy of 63.8%. The use of Teflon does not seem to be detrimental to this result. It seems possible to obtain an equivalent accuracy with ten cameras instead of twelve.

Design of a Novel NNs Learning Tracking Controller for Robotic Manipulator with Joints Flexibility

The precise tracking control problem for the robotic manipulator with flexible joints, subjected to system uncertainties and external disturbances, is addressed. A novel control scheme is presented that does not use link velocity measurements and high-order derivatives of the link states. The control scheme employs neural networks-based observers to estimate both motor velocity and link velocity. By using the virtually applied torque, the link controller is designed based on rigid link dynamics, and the motor controller is designed using the dynamic surface control technique. The proposed control scheme can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The simulation results confirm our theoretical analysis, and a comparison study demonstrates the advantages of the proposed control scheme compared to the standard DSC method.

Global fixed-time adaptive fuzzy path following control for unmanned surface vehicles subject to lumped uncertainties and actuator saturation

A global fixed-time control strategy is presented for addressing the path following problem of underactuated unmanned surface vehicles (USVs) with lumped uncertainties and actuator saturation in this paper. Firstly, a novel fixed-time dual adaptive line-of-sight (FTDALOS) guidance law is proposed, which provides the desired heading angle and surge velocity. Subsequently, considering the complex marine environment, a novel fixed-time adaptive fuzzy controller (FTAFC) is developed. Additionally, to solve the issues of lumped uncertainties and actuator saturation, a fixed-time adaptive fuzzy control strategy and a fixed-time dynamic auxiliary saturation system are proposed. It is demonstrated that the proposed strategy can drive the USV along the desired path smoothly and rapidly, and all errors of the closed-loop system can converge to arbitrary small neighborhoods of the origin within fixed-time intervals. Finally, the effectiveness and superiority of FTDALOS guidance law and FTAFC are verified by theoretical analysis and simulation studies.

Distributed Robust Nash Equilibrium Seeking for Mixed-Order Games by a Neural-Network-Based Approach

In practical applications, decision makers with heterogeneous dynamics may be engaged in the same decision-making process. This motivates us to study distributed Nash equilibrium seeking for games in which players are mixed-order (first-and second-order) integrators influenced by unknown dynamics and external disturbances in this article. To solve this problem, we employ an adaptive neural network to manage unknown dynamics and disturbances, based on which a distributed Nash equilibrium seeking algorithm is developed by further adapting concepts from gradient-based optimization and multiagent consensus. By constructing appropriate Lyapunov functions, we analytically prove the convergence of the reported method. Theoretical investigations suggest that players’ actions would be steered to an arbitrarily small neighborhood of the Nash equilibrium, which is also testified by simulations.

Relay-Switching-Based Fixed-Time Tracking Controller for Nonholonomic State-Constrained Systems: Design and Experiment

Dear Editor, This paper investigates the fixed-time trajectory tracking controller design for nonholonomic chained systems with static state constraints. Firstly, a fixed-time tracking control law is given to carry out relay switching, which divides the controller development process into two stages. For the goal of designing fixed-time tracking controller in the second stage and avoiding the explosion of complexity, fixed-time filters are introduced. Under these preparations, a fixed-time tracking controller is elaborated by applying barrier-Lyapunov-function-based backstepping. In addition, a new tracking controller is also proposed to guarantee that the tracking error system is bounded before switching. It has been proved that the resulting closed-loop tracking error system states converge to a small neighborhood around zero within a fixed time and output states are kept in the predefined constrained regions.

Resilient Model Free Adaptive Distributed LFC for Multi-Area Power Systems Against Jamming Attacks.

This article is concerned with distributed resilient load frequency control (LFC) for multi-area power interconnection systems against jamming attacks. First, considering uncertainties and high dimension nonlinearity, the model-free adaptive control (MFAC) model is adopted for the power system, in which only input and output (I/O) data are used. Second, jamming attacks are modeled in a stochastic process, and a multistep predictive compensation algorithm is developed to mitigate the impact of jamming attacks. Then, the distributed MFAC protocol with predictive compensation algorithm is designed such that the frequency tracking errors under the predictive compensation algorithm of multi-area power interconnection systems converge consensually into a small neighborhood of origin in the mean square sense. Simulation results show the effectiveness of the approach.

Observer-Based Finite-Time Fault-Tolerant Control for Nonstrict-Feedback Nonlinear Systems With Multiple Uncertainties

In the fault-tolerant control (FTC) tasks of nonstrict-feedback nonlinear systems, unmeasurable states, disturbance, and actuator faults are recognized as the main factors that obstacle the effective controller design and, thus, the tracking performance improvement. To tackle these obstructions, a fuzzy observer is introduced to address the difficulties of the unmeasurable states involving nonstrict-feedback nonlinear systems by benefiting from the approximation property of fuzzy logic systems. Owing to the newly employed damping term in the intermediate control law being utilized to compensate for the possibly unlimited number of faults, the proposed FTC strategy is able to deal with actuator faults properly without imposing tighter requirements on the fault mechanism. To reach fast transient performance, stability of finite time is reached by exploiting the backstepping method. The investigated strategy ensures that all the responses of the systems are semiglobal practical finite-time stable. Meanwhile, the tracking error converges to a small neighborhood of the origin within finite time. In addition, to demonstrate its effectiveness, the provided approach is applied to the position tracking of a robotic system, which shows anticipated control performances in spite of various uncertainties.

Fixed-Time Stable Proximal Dynamical System for Solving MVIPs

In this paper, a novel modified proximal dynamical system is proposed to compute the solution of a mixed variational inequality problem (MVIP) within a fixed time, where the time of convergence is finite and is uniformly bounded for all initial conditions. Under the assumptions of strong monotonicity and Lipschitz continuity, it is shown that a solution of the modified proximal dynamical system exists, is uniquely determined, and converges to the unique solution of the associated MVIP within a fixed time. Furthermore, the fixed-time stability of the modified projected dynamical system continues to hold, even if the assumption of strong monotonicity is relaxed to that of strong pseudomonotonicity. Finally, it is shown that the solution obtained using the forward-Euler discretization of the proposed modified proximal dynamical system converges to an arbitrarily small neighborhood of the solution of the associated MVIP within a fixed number of time steps, independent of the initial conditions.

Command-Filtered Finite-Time Fuzzy Adaptive Fault-Tolerant Control of Output-Constrainted Robotic Manipulators With Unknown Dead-Zones

This article presents a barrier Lyapunov functions-based (BLFs) adaptive fuzzy finite-time fault-tolerant control scheme for the manipulator with actuator faults and unknown dead-zones. Firstly, by constructing time-varying BLFs, the system output will be constrained in time-varying regions. Secondly, an adaptive method is designed to estimate the degree of actuator faults and to compensate for the effect of dead-zones. Thirdly, the command filtered backstepping with error compensation mechanism is introduced to approximate the virtual control laws, which can not only solve the “explosion of complexity” problem, but also improve the control accuracy. The control method can ensure that all signals of the closed-loop system are finite-time bounded, the tracking errors of joint positions converge to a small neighborhood of the origin, and meets the requirements of time-varying constraints. Finally, a rigid manipulator is used to verify the effectiveness of the scheme.

On the nucleation of cracks near stress sources with weak singularities

Analytical expressions are obtained for the configuration force and the value of elastic energy relaxation at the nucleation of a microcrack in a small neighborhood of an arbitrary singular stress source. When analyzing the conditions of crack nucleation at sources with weak divergences of the stress fields, ideas about the instantaneous nucleation of a crack of finite length are used. Simultaneous fulfillment of stress and energy conditions is considered as a criterion for the nucleation of such a crack. Within the framework of these representations, in the configuration space of the system parameters (geometric characteristics and strength of mesodefects, the value of external stress), the regions in which crack nucleation is possible in the case of a combined mesodefect, which is a superposition of the dipole of wedge disclinations and planar shear mesodefect, are determined. It is shown that the nucleation of the crack is significantly facilitated by the instability of the shear mesodefect.

Stabilization of delayed fractional‐order T‐S fuzzy systems with input saturations and system uncertainties

AbstractBy adopting the Takagi–Sugeno (T‐S) fuzzy theory, this paper focuses on the adaptive control of fractional‐order time‐delay systems involving unknown parameters and input saturations. T‐S fuzzy systems with “IF‐THEN” rules are employed to describe fractional‐order nonlinear systems. The influence of input saturation is handled by designing an auxiliary system. By using a norm conversion, the system's time‐delay term is converted into a non‐delayed form. Adaptive updating rules are devised to evaluate uncertainties of the system, and a new control approach is proposed. The devised controller can not only guarantee the boundedness of variables involved but also make state variables converge into a sufficiently small neighborhood of the origin, even in the presence of uncertainties, saturation functions, and system parameters. Finally, numerical studies are given to verify the correctness of the designed scheme.

Fixed-Time Adaptive Chaotic Control for Permanent Magnet Synchronous Motor Subject to Unknown Parameters and Perturbations

It is well known that the permanent magnet synchronous motor (PMSM) exhibits chaotic characteristics when its parameters fall within a certain range, which can lead to system instability. This article proposes an adaptive control strategy for achieving the fixed-time chaotic stabilization of PMSM, even in the presence of unknown parameters and perturbations. The developed controller is synthesized by combining a parametric adaptive mechanism with a fixed-time control technique. The stability analysis demonstrates that the system states under the developed controller can converge to small neighborhoods around the equilibrium point within a fixed time. Thanks to the adoption of the parametric adaptive mechanism, the developed controller is not only insensitive to unknown parameters but also robust against perturbations. Finally, simulated studies are conducted to verify and emphasize the effectiveness of the developed control strategy.

Nussbaum gain adaptive fuzzy control for switched nonlinear systems with predefined output and full time‐varying states constraints

AbstractIn this paper, the problem of adaptive fuzzy tracking control for a class of uncertain switched nonlinear systems with unknown control direction is studied. Aiming at the problem, an adaptive control scheme with Nussbaum gain technology is constructed by using the average dwell time (ADT) method and the backstepping method to overcome the unknown control direction, and time‐varying asymmetric barrier Lyapunov functions (ABLFs) are adopted to ensure the full‐state constraints satisfaction. The proposed control scheme guarantees that all closed‐loop signals remain bounded under a class of switching signals with ADT, while the output tracking error converges to a small neighborhood of the zero. An important innovation of this design method is that the unknown control direction, asymmetric time‐varying full state constraints, and predefined time‐varying output requirements are simultaneously considered in uncertain switched nonlinear systems for the first time. We set a moment in advance, and make the systems comply with the constraint conditions before running the moment by the shift function nested in the first time‐varying ABLF. Finally, a simulation example verifies the effectiveness of the proposed scheme.