Predicting incident radiographic knee osteoarthritis through quantitative meniscal lesion parameters: data from the osteoarthritis initiative.

BACKGROUND: This study investigates the potential of novel meniscal parameters as predictive factors for incident radiographic knee osteoarthritis (ROA) over a span of four years, as part of the Osteoarthritis Initiative (OAI) study. OBJECTIVES: Quantitative measurements of meniscal parameters alteration could serve as predictors of OA's occurrence and progression. METHODS AND MATERIALS: A nested matched case-control study design was used to select participants from OAI study. Case knees (n = 178) were defined as those with incident ROA (Kellgren Lawrence Grade (KLG) 0 or 1 at baseline (BL), evolving into KLG 2 or above by year 4). Control knees were matched one-to-one by sex, age and radiographic status with case knees. The mean distance from medial-to-lateral meniscal lesions [Mean(MLD)], mean value of tibial plateau width [Mean(TPW)] and the mean of the relative percentage of the medial-to-lateral meniscal lesions distance [Mean(RMLD)] were evaluated through coronal T2-weighted turbo spin echo (TSE) MRI at P-0 (visit when incident ROA was found on radiograph), P-1(one year prior to P-0) and baseline, respectively. Using the imaging data of one patient, the mechanism was investigated by finite element analysis. RESULTS: Participants were on average 60.22 years old, predominantly female (66.7%) and overweight (mean BMI: 28.15). Mean(MLD) and Mean(RMLD) were significantly greater for incident knees compared to no incident knees at baseline, P-1 and P-0. [Mean(MLD), Mean(RMLD); (42.56-49.73) mean ± (7.70-9.52) mm SD vs. (38.14-40.78) mean ± (5.51-7.05)mm SD; (58.61-68.95) mean ± (8.52-11.40) mm SD vs. (52.52-56.35) mean ± (6.53-7.85)mm SD, respectively]. Baseline Mean(MLD) and Mean(RMLD), [Adjusted OR, 95%CI: 1.11(1.07 to 1.16) and 1.13(1.09 to 1.17), respectively], were associated with incident ROA during 4 years, However, Mean(TPW) [Adjusted OR, 95%CI: 0.98(0.94 to 1.02)] was not associated with incident ROA during 4 years. While Mean(TPW) at P-1 and P-0 was not associated with the risk of incident ROA, Mean(MLD) and Mean(RMLD) at P-1 and P-0 were significantly positively associated with the risk of incident ROA. CONCLUSIONS: The meniscal parameters alteration could be an important imaging biomarker to predict the occurrence of ROA.

Important-Data-Based DoS Attack Mechanism and Resilient H∞ Filter Design for Networked T-S Fuzzy Systems.

This article is concerned with the security problems for networked Takagi-Sugeno (T-S) fuzzy systems with asynchronous premise constraints. The primary objective of this article is twofold. First, a novel important-data-based (IDB) denial-of-service (DoS) attack mechanism is proposed from the perspective of the adversary for the first time to reinforce the destructive effect of the DoS attacks. Different from most existing DoS attack models, the proposed attack mechanism can utilize the information of packets, evaluate the importance degree of packets, and only attack the most "important" ones. As such, a larger system performance degradation can be expected. Second, corresponding to the proposed IDB DoS mechanism, a resilient H∞ fuzzy filter is designed from the defender's point of view to alleviate the negative effect of the attack. Furthermore, since the defender does not know the attack parameter, an algorithm is designed to estimate it. In a word, a unified attack-defense framework is developed in this article for networked T-S fuzzy systems with asynchronous premise constraints. With the help of the Lyapunov functional method, sufficient conditions are successfully established to compute the desired filtering gains and ensure the H∞ performance of the filtering error system. Finally, two examples are exploited to demonstrate the destructiveness of the proposed IDB DoS attack and the usefulness of the developed resilient H∞ filter.

A novel stability criterion and precise control for linear time delay systems based on regional configuration of the poles.

This paper presents the concept of region stability and provides criteria for region stability of linear time delay systems, which can reveal the dynamic and steady-state performance of the systems more precisely. Corresponding design schemes for stabilization and tracking control that can accurately control various performance of time delay systems have also been explored. First, in the light of the connection between the poles and the dynamic properties of the system, the concept of region stability is given to describe the finer dynamic behavior of time delay systems. The criteria for the region stability are also presented. Second, the region stabilization methods are investigated, which can ensure that the system satisfies a certain dynamic performance by setting the eigenvalues in a certain convex region. Third, a precise tracking control of the linear time delay systems is addressed as an application of region stabilization. It can control the steady state performance and transient response of the tracking signal more precisely. Finally, three instances are provided to display the superiority of the new method for the performance indexes of the linear time delay systems.

Predefined Accuracy Adaptive Tracking Control for Nonlinear Multiagent Systems With Unmodeled Dynamics.

This article focuses on an adaptive dynamic surface tracking control issue of nonlinear multiagent systems (MASs) with unmodeled dynamics and input quantization under predefined accuracy. Radial basis function neural networks (RBFNNs) are employed to estimate unknown nonlinear items. A dynamic signal is established to handle the trouble introduced by the unmodeled dynamics. Moreover, the predefined precision control is realized with the aid of two key functions. Unlike the existing works on nonlinear MASs with unmodeled dynamics, to avoid the issue of "explosion of complexity," the dynamic surface control (DSC) method is applied with the nonlinear filter. By using the designed controller, the consensus errors can gather to a precision assigned a priori. Finally, the simulation results are given to demonstrate the effectiveness of the proposed strategy.

Secure output consensus for multi-agent systems under edge-based event-trigger against denial-of-service.

This paper is concerned with the secure output consensus problem for the heterogeneous multi-agent systems under the event-triggered scheme in the presence of the denial-of-service attack. Without detecting the attack, the hold-input controller update strategy is adopted when some transmission data may be lost due to the effect of the attack. Based on the tolerable duration of the attack, a novel edge-based event-triggered scheme is developed. The scheme can avoid continuous communication and exclude Zeno behavior. With the aid of the switched system theory, output consensus is preserved. An example shows the effectiveness.

An effective multi-model based nonlinear control for USC power plant.

Energy efficiency optimization for the ultra supercritical (USC) boiler-turbine unit is a major concern in the field of power generation. In order to deal with the nonlinearity and slow dynamic response problems, a new nonlinear control method is proposed which integrates internal model control (IMC) and generalized predictive control (GPC) into a unified framework. Specifically, through a long short-term memory (LSTM) neural network based IMC, the system achieves rapid convergence to the vicinity of the desired setpoint, significantly enhancing the response speed. Then, by a composite weighted human learning optimization network based nonlinear generalized predictive control (CWHLO-GPC), high-accuracy tracking performance is achieved. Finally, an example on a 1000MW USC power plant demonstrates the proposed method can achieve fast and stable dynamic response under large load variation.

Dual Channels Event-Triggered Asymptotic Consensus Control for Fractional-Order Nonlinear Multiagent Systems.

This article investigates the event-triggered leaderless consensus control problem for fractional-order multiagent systems (FOMASs), where both the agent-to-agent communication channel and the controller-to-actuator communication channel are based on the events. A filter is introduced to transform the original high-order system into a first-order one, greatly simplifying the complexity of controller design compared to the traditional backstepping. Further, the convergence of filtered output signals is proved to be consistent with that of the outputs of agents themselves. Superior to the traditional event-triggered scheme, two dynamic variables are designed for the triggering conditions of the communication among agents and the controller update, respectively. Via elaborately constructing the dynamic variables, zero-error leaderless consensus can be achieved instead of only ultimately uniformly bounded result. It is proved that the proposed control strategy can guarantee better control performance of leaderless consensus under limited communication resources, and Zeno behavior is excluded. Finally, two examples are provided to verify the effectiveness of our proposed control approach.

New Stability Criterion for Positive Impulsive Fuzzy Systems by Applying Polynomial Impulse-Time-Dependent Method.

The problems of exponential stability and L1 -gain for positive impulsive Takagi-Sugeno (T-S) fuzzy systems are further studied in this article. Different from the Lyapunov function in the literature, where the Lyapunov matrices are time-invariant or only linearly dependent on the impulse interval, in this article, a novel polynomial impulse-dependent (ID) copositive Lyapunov function (CLF) is constructed by using the polynomial impulse time function. In addition, the binomial coefficients are applied to derive new finite linear programming conditions. Less conservative results are obtained since the polynomial ID CLF contains more impulse interval information. Three examples demonstrate the influence of the polynomial degree on the results and the effectiveness of the developed new results.

Practical Fixed-Time Bipartite Synchronization of Uncertain Coupled Neural Networks Subject to Deception Attacks via Dual-Channel Event-Triggered Control.

This article investigates the practical fixed-time synchronization of uncertain coupled neural networks via dual-channel event-triggered control. Contrary to some previous studies, the bipartite synchronization of signed graphs representing cooperative and antagonistic interactions is studied. The communication channel is introduced into deception attacks, which are described by Bernoulli's stochastic variables. Based on the concept of two channels, event-triggered mechanisms are designed for sensor-to-controller and controller-to-actuator channels to reduce communication consumption and controller update consumption as much as possible. Lyapunov and comparison theories are used to derive synchronization criteria and explicit expression of settling time. An example of Chua's circuit system is presented to demonstrate the feasibility of the obtained theoretical results.

Event-Based Two-Step Transmission Mechanism for the Stabilization of Networked T-S Fuzzy Systems With Random Uncertainties.

This article studies an event-based two-step transmission mechanism (TSTM) in the control design for networked T-S fuzzy systems. The transmission task is achieved in two steps. Consecutive triggering packets are relabeled in the first step by applying a traditional event-triggered mechanism (ETM). Then a probabilistic approach is employed to determine which packet is a real release packet (RRP) in the second step. This event-based TSTM is particularly suitable for scenarios in which traditional ETMs are unable to determine which packets are redundant. By discarding most of the unnecessary data packets, especially when the system is tending toward stability, the burden on the network bandwidth is reduced. To establish a control strategy for T-S fuzzy-based nonlinear systems with random uncertainties, a new timing analysis technique is proposed. Additionally, the necessary conditions for a nonlinear system's mean-square asymptotic stability (MSAS) are derived. Finally, two practical applications demonstrate the effectiveness of the suggested transmission mechanism in networked T-S fuzzy systems.

Prescribed Fixed-Time Control for Constrained Uncertain Nonlinear Cyber-Physical Systems Against Deception Attacks.

In this note, a novel prescribed fixed-time adaptive tracking control scheme is developed to cope with the fixed-time tracking control issue for a category of constrained MIMO nonlinear cyber-physical systems (CPSs) with exogenous perturbations, which suffer from deception attacks started in controller-actuator (C-A) channel. Distinguished from the conservative dynamic surface control (DSC) schemes with a linear filter, a novel nonlinear filter is designed in our strategy, which can tackle the intrinsic issue of explosion of computational complexity and promote the system performance. Besides, a new barrier Lyapunov function (BLF) is designed to ulteriorly enhance the tracking performance on the basis of the prescribed performance function (PPF) approach. Prominently, the proposed control strategy could accommodate the exogenous interferences and deception attacks simultaneously. Furthermore, we have substantiated that the developed approach can not only make certain that all the tracking errors of the resulting closed-loop system, including output tracking errors and virtual tracking errors, enter a prespecified small region near equilibrium point with fixed-time convergence rate, but also guarantee them obey the corresponding constraints throughout the entire control operation, where the regulation time and the tracking accuracy level keep prior known and could be prespecified arbitrarily. Finally, the validity and effectiveness of the proposed control scheme are illustrated through a representative application instance.

Nussbaum-Based Adaptive Neural Networks Tracking Control for Nonlinear PDE-ODE Systems Subject to Deception Attacks.

In this article, the novel adaptive neural networks (NNs) tracking control scheme is presented for nonlinear partial differential equation (PDE)-ordinary differential equation (ODE) coupled systems subject to deception attacks. Because of the special infinite-dimensional characteristics of PDE subsystem and the strong coupling of PDE-ODE systems, it is more difficult to achieve the tracking control for coupled systems than single ODE system under the circumstance of deception attacks, which result in the states and outputs of both PDE and ODE subsystems unavailable by injecting false information into sensors and actuators. For efficient design of the controllers to realize the tracking performance, a new coordinate transformation is developed under the backstepping method, and the PDE subsystem is transformed into a new form. In addition, the effect of the unknown control gains and the uncertain nonlinearities caused by attacks are alleviated by introducing the Nussbaum technology and NNs. The proposed tracking control scheme can guarantee that all signals in the coupled systems are bounded and the good tracking performance can be achieved, despite both sensors and actuators of the studied systems suffering from attacks. Finally, a simulation example is given to verify the effectiveness of the proposed control method.

Quantization-Based Adaptive Fuzzy Consensus for Multiagent Systems Under Sensor Deception Attacks: A Novel Compensation Mechanism.

This study mainly investigates the adaptive leader-following consensus tracking control problem for a class of nonlinear multiagent systems (MASs) subjected to unknown control directions, external disturbances, and sensor deception attacks. To start with, an equivalent MAS with known control directions is obtained by introducing a linear state transformation. For the purpose of estimating the unavailable system states caused by malicious attacks, a quantization-based fuzzy state observer is designed, and the fuzzy-logic system (FLS) is utilized to approximate nonlinear functions. Moreover, a dynamic uniform quantizer with scaling function is established to reduce information transmission. With the help of coordinate transformation and available compromised states, a novel compensation mechanism is designed to offset the influence of filter errors while avoiding the problem of "explosion of complexity" in the backstepping design process. In addition, the Nussbaum-type function is considered to eliminate the design obstacle of unknown control gains resulting from the attacks. Under the constructed consensus protocol, it is proved theoretically that the consensus tracking error converges to an adjustable small neighborhood of the origin, and all signals in the closed-loop system are bounded. Finally, the feasibility of the provided secure control scheme is verified through two simulation examples.

Novel Dynamic Event-Triggered Consensus Control of Multiagent Systems With Markovian Switching Topologies Under DoS Attacks.

This article focuses on the issue of novel dynamic event-triggered consensus control of multiagent systems (MASs) with denial-of-service (DoS) attacks. Different from the conventional Markovian switching topologies, the generally uncertain semi-Markovian (GUSM) switching topologies with partially unknown elements and time-dependent uncertainties are constructed for the leader-following MASs by considering the equipment performance and external uncertain environment influence. To save communication resources, the novel dynamic memory event-triggered strategy (DMETS) is presented to decrease the frequency of communication between agents. Some secure consensus control criteria are established for the MASs with GUSM switching topologies and DoS attacks due to the potential system communication disruption caused by attackers. Finally, two physical system examples are designed to prove the effectiveness of the presented method.

Integral BLF-Based Adaptive Dynamic Event-Triggered Boundary Control for a Flexible Riser System.

For the flexible riser systems modeled with partial differential equations (PDEs), this article explores the boundary control problem in depth for the first time using a dynamic event-triggered mechanism (DETM). Given the intrinsic time-space coupling characteristic inherent in PDE computations, implementing a state-dependent DETM for PDE-based flexible risers presents a significant challenge. To overcome this difficulty, a novel dynamic event-triggered control method is introduced for flexible riser systems, focusing on optimizing available control inputs. In order to save computational costs from the controller to the actuator, a dynamic event-triggered adaptive boundary controller is designed to effectively reduce boundary position vibrations. Additionally, considering external disturbances, an adaptive bounded compensation term is incorporated to counteract the influence of external disturbances on the system. Addressing boundary position constraints, a new integral barrier Lyapunov function (iBLF) tailored specifically for flexible riser systems is introduced, thereby alleviating conservatism in the controller design of flexible risers modeled by PDEs. At last, the validity of the proposed method is demonstrated through a simulation example.

Periodic Event-Triggered Dynamic Feedback Synchronization Control of Discrete-Time Neural Networks.

This article investigates the event-triggered synchronization control problem of discrete-time neural networks (DNNs) in the case of periodic sampled-data. A discrete-time periodic event-triggered mechanism is adopted to evaluate the measurements, which avoids formulating the triggering function in a continuous manner and saves energy consumption. Under this framework, an event-triggered dynamic output-feedback controller is designed to achieve the goal of synchronization. A piecewise Lyapunov functional is constructed to analyze the sawtooth-like pattern of sampled-error signals. Thereafter, the synchronization criteria are formulated for the considered DNNs. The co-designed issue is further discussed for the control gains and triggering parameter. Finally, a simulation example is presented to show the effectiveness of the proposed method.

Dynamic Periodic Event-Triggered Synchronization of Complex Networks: The Discrete-Time Scenario.

This article reports the synchronization control of discrete-time complex networks using an event-triggered method. The main contributions are twofold: 1) a discrete-time scenario of the dynamic periodic event-triggered mechanism is developed to schedule the transmissions of measurements. The proposed mechanism monitors the synchronization error in a periodic manner, which is beneficial to reduce the calculation resources of sensors. Simultaneously, the proposed mechanism increases the triggering threshold so that it contributes to enlarging the average interevent interval and 2) a new Lyapunov functional is developed to deal with the periodic samplings. On the one hand, the proposed functional involves a delay-dependent term, which is convenient to formulate the synchronization criterion by the delay analysis technique. On the other hand, the functional takes the sawtooth constraint of periodic samplings into consideration by introducing a piecewise functional. Finally, a succinct criterion is derived such that the considered networks are synchronized with a predetermined error level. A simulation example is provided to show our advantages in comparison with the existing approaches.

Quasi-Synchronization of Discrete-Time-Delayed Heterogeneous-Coupled Neural Networks via Hybrid Impulsive Control.

This article explores the quasi-synchronization of discrete-time-delayed heterogeneous-coupled neural networks (CNNs) via hybrid impulsive control. By introducing an exponential decay function, two non-negative regions are introduced that are named time-triggering and event-triggering regions, respectively. The hybrid impulsive control is modeled by the dynamical location of Lyapunov functional in two regions. When the Lyapunov functional locates in the time-triggering region, the isolated neuron node releases impulses to corresponding nodes in a periodical manner. Whereas, when the trajectory locates in the event-triggering region, the event-triggered mechanism (ETM) is activated, and there are no impulses. Under the proposed hybrid impulsive control algorithm, sufficient conditions are derived for quasi-synchronization with a definite error convergence level. Compared with pure time-triggered impulsive control (TTIC), the proposed hybrid impulsive control method can effectively reduce the times of impulses and save communication resources on the premise of ensuring performance. Finally, an illustrative example is given to verify the validity of the proposed method.

A New Neural Dynamic Learning Framework for Discrete-Time Strict-Feedback Systems: Internal Interaction-Based Weight Adaptive Laws.

This article investigates internal interaction-based dynamic learning control (LC) for uncertain discrete-time strict-feedback systems. On the basis of predict technology, the original system is converted into a common n -step-ahead input-output predict model. The predict model causes every estimated neural weight to converge to n different constants using the existing control framework. To solve such a problem, the predict model is further decomposed into n one-step-ahead subsystems, which can be viewed as n independent agents. Subsequently, the distributed cooperative weight adaptive laws are designed by introducing an undirected and connected interconnection topology among subsystems. By constructing the variable relationship between the subsystems and the n -step-ahead predict model, a new internal weight interaction-based neural dynamic LC framework is proposed for the whole closed-loop system, in which estimated weights at different times share their weight knowledge. The proposed framework ensures the ultimately uniform boundedness of the closed-loop system and achieves the excellent control performance. By combining the consensus theory and a cooperative persistent excitation condition, every estimated weight along the neural input orbit is verified to exponentially converge to a close vicinity of a unique ideal constant, rather than n different constants. Consequently, the developed LC framework facilitates constant weights storage, saves the knowledge storage space, and improves the robustness of knowledge utilization. These characteristics are verified by simulation results.

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.