Neural network-based predefined-time bipartite formation tracking control of uncertain heterogeneous Euler-Lagrange systems in task space.

The main problem addressed in this paper is the task-space bipartite formation tracking problem of uncertain heterogeneous Euler-Lagrange systems in predefined time. To solve this problem, an effective hierarchical predefined-time control algorithm is designed. This algorithm utilizes a non-singular sliding surface, allowing for the adjustment of the upper bound of the settling time as a flexible parameter. Key components of the proposed approach include an estimator for the leader's states and a controller tailored to the formation problem. To mitigate the effects of dynamic uncertainties in the system, the radial basis function neural network is integrated into the methodology. Finally, the effectiveness and validity of the proposed algorithm are demonstrated through numerical simulations, showcasing their practical applicability and efficacy.

A refined adaptive event-based resilient filtering for complex networks over switching topology.

The problem of the resilient filtering for a class of discrete-time complex networks over switching topology is investigated. Taking into account the limitation of channel bandwidth, a refined adaptive event-triggered scheme is derived, whose threshold is determined by the change rate of measurement. The large change rate of measurement results in a smaller threshold, which means that more data packets will be transmitted to guarantee the performance of filtering, and the smaller one leads to a bigger threshold to save the network energy. Under the adaptive event-triggered scheme, considering the switching topology and uncertain inner coupling, a resilient filtering with a variable filtering gain is proposed. Additionally, the minimal upper bound of the covariance of estimation error is developed and the sufficient conditions are also given to obtain the exponentially bounded in mean square of the estimation error system. Finally, a simulation is presented to certify the effectiveness of the derived resilient filtering.

An Efficient Matching Game Approach to Association Formation in UAV-Enabled Hierarchical Distributed Learning.

Distributed machine learning has emerged as a promising data processing technology for next-generation communication systems. It leverages the computational capabilities of local nodes to efficiently handle large datasets, creating highly accurate data-driven models for analysis and prediction purposes. However, the performance of distributed machine learning can be significantly hampered by communication bottlenecks and node dropouts. In this article, a novel unmanned aerial vehicle (UAV)-enabled hierarchical distributed learning architecture is proposed to support machine learning applications, e.g., regional monitoring. Multiple UAV receivers (URs) are introduced as wireless relays to improve the communication between the UAV transmitters (UTs) and the cloud server. Our objective is to identify the optimal UT-UR association to maximize the social welfare of the network, which is distinctly different from the existing works that focus on the unilateral profit-maximizing problem. We formulate a two-side many-to-one matching game to model the UT-UR association problem, and a two-phase many-to-one matching algorithm is designed to identify the stable matching. The validity of our proposed scheme is verified through in-depth numerical simulations.

Adaptive Intermittent Pinning Control for Synchronization of Delayed Nonlinear Memristive Neural Networks With Reaction-Diffusion Items.

In this article, the global exponential synchronization problem is investigated for a class of delayed nonlinear memristive neural networks (MNNs) with reaction-diffusion items. First, using the Green formula, Lyapunov theory, and proposing a new fuzzy adaptive pinning control scheme, some novel algebraic criteria are obtained to ensure the exponential synchronization of the concerned networks. Furthermore, the corresponding control gains can be promptly adjusted based on the current states of partial nodes of the networks. Besides, a fuzzy adaptive aperiodically intermittent pinning control law is also designed to synchronize the fuzzy MNNs (FMNNs). The controller with intermittent mechanism can obtain appropriate rest time and save energy consumption. Finally, some numerical examples are provided to confirm the effectiveness of the results in this article.

Distributed Control for Nonlinear Time-Delay Multiagent Systems: Hybrid Saturation-Constraint Impulsive Approach.

This article mainly studies the problem of impulse consensus of multiagent systems under communication constraints and time delay. Considering the limited communication bandwidth of the agent, global and partial saturation constraints are considered. In addition, so as to further improve communication efficiency by reducing communication frequency, the novel control protocol combining event-triggered strategy and general impulse control protocol is proposed. Under this kind of novel control protocol, the communication frequency of multiagent systems can be reduced while avoiding "Zeno behavior." Through theoretical analysis, sufficient conditions for the systems to achieve consensus are obtained for the above two saturation constraint cases. In the end, the effectiveness of the novel protocols is proved by providing two different simulation instances.

Optimal tracking performance of networked control systems under communication constraints.

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output discrete-time communication-constrained systems by thinking about Denial of Service (DoS) attacks, codecs and additive Gaussian white noise under energy constraints. The non-cooperative relationship between DoS attacks and intrusion detection systems (IDS) is analyzed using repeated game theory. A penalty mechanism is constructed to force the attackers to adopt a cooperative strategy, thus improving the system performance. Partial factorization and spectrum decomposition are used to provide the OTP for systems. The results demonstrate that the systems' OTP are linked to intrinsic characteristics like non-minimum phase zeros and unstable poles. Finally, concrete examples are shown that the results are accurate.

Hierarchical prescribed-time bipartite consensus for multiple Euler-Lagrange systems with input-to-output redundancy and directed matrix-weighted signed graph.

This paper studies the prescribed-time bipartite consensus problem for multiple Euler-Lagrange systems (MELSs) under directed matrix-weighted signed graph, in which input-to-output redundancy, external disturbances and uncertain dynamic terms have been taken into consideration. Firstly, this paper proposes the prescribed-time hierarchical control (PTHC) algorithm to tackle the aforesaid issue. It is worth pointing out that the convergence time can be arbitrarily prescribed based on actual engineering needs. Then, the corresponding sufficient conditions for achieving the prescribed-time bipartite consensus are obtained by invoking Lyapunov stability analysis. Eventually, the numerical simulation results are performed, which vividly reflect the effectiveness and feasibility of the developed control algorithm.

Adaptive neural network control for Markov jumping systems against deception attacks.

This paper proposes an innovative approach for mitigating the effects of deception attacks in Markov jumping systems by developing an adaptive neural network control strategy. To address the challenge of dual-mode monitoring mechanisms, two independent Markov chains are used to describe the state changes of the system and the intermittent actuator. By employing a mapping technique, these individual chains are amalgamated into a unified joint Markov chain. Additionally, to effectively approximate the unbounded false signals injected by deception attacks, an adaptive neural network technique is skillfully built. A mode monitoring scheme is implemented to design an asynchronous control law that links the mode information between the joint Markov chain and controller with fewer modes. The paper derives sufficient criteria for the mean-square bounded stability of the resulting system based on Lyapunov theories. Finally, a numerical experiment is conducted to demonstrate the effectiveness of the proposed method.

Optimized Distributed Filtering for Saturated Systems With Amplify-and-Forward Relays Over Sensor Networks: A Dynamic Event-Triggered Approach.

In this article, the optimized distributed filtering problem is studied for a class of saturated systems with amplify-and-forward (AF) relays via a dynamic event-triggered mechanism (DETM). The AF relays are located in the channels between sensors and filters to prolong the transmission distance of signals, where the transmission powers of sensors and relays can be described by a sequence of random variables with a known probability distribution. With the purpose of alleviating the communication burden and preventing data collision, the DETM is used to schedule the transmission cases of nodes by dynamically adjusting the triggered threshold according to the practical requirements. An upper bound matrix (UBM) of the filtering error (FE) covariance is first provided under the sense of variance constraint and the proper filter gain is further constructed via minimizing the proposed UBM. In addition, the boundedness evaluation regarding the trace of the UBM is provided. Finally, simulation experiments are used to illustrate the usefulness of the developed distributed recursive filtering scheme.

Protocol-Based Synchronization of Stochastic Jumping Inertial Neural Networks Under Image Encryption Application.

This work investigates the protocol-based synchronization of inertial neural networks (INNs) with stochastic semi-Markovian jumping parameters and image encryption application. The semi-Markovian jumping process is adopted to characterize INNs under sudden complex changes. To conserve the limited available network bandwidth, an adaptive event-driven protocol (AEDP) is developed in the corresponding semi-Markovian jumping INNs (S-MJINNs), which not only reduces the amount of data transmission but also avoids the Zeno phenomenon. The objective is to construct an adaptive event-driven controller so that the drive and response systems maintain synchronous relationships. Based on the appropriate Lyapunov functional, integral inequality, and free weighting matrix, novel criteria are derived to realize the synchronization. Moreover, the desired adaptive event-driven controller is designed under a semi-Markovian jumping process. The proposed method is demonstrated through a numerical example and an image encryption process.

Optimal Consensus Control for Continuous-Time Linear Multiagent Systems: A Dynamic Event-Triggered Approach.

This article investigates the optimal consensus problem for general linear multiagent systems (MASs) via a dynamic event-triggered approach. First, a modified interaction-related cost function is proposed. Second, a dynamic event-triggered approach is developed by constructing a new distributed dynamic triggering function and a new distributed event-triggered consensus protocol. Consequently, the modified interaction-related cost function can be minimized by applying the distributed control laws, which overcomes the difficulty in the optimal consensus problem that seeking the interaction-related cost function needs all agents' information. Then, some sufficient conditions are obtained to guarantee optimality. It is shown that the developed optimal consensus gain matrices are only related to the designed triggering parameters and the desirable modified interaction-related cost function, relaxing the constraint that the controller design requires the knowledge of system dynamics, initial states, and network scale. Meanwhile, the tradeoff between optimal consensus performance and event-triggered behavior is also considered. Finally, a simulation example is provided to verify the validity of the designed distributed event-triggered optimal controller.

Data-Driven Optimal Bipartite Consensus Control for Second-Order Multiagent Systems via Policy Gradient Reinforcement Learning.

This article investigates the optimal bipartite consensus control (OBCC) problem for unknown second-order discrete-time multiagent systems (MASs). First, the coopetition network is constructed to describe the cooperative and competitive relationships between agents, and the OBCC problem is proposed by the tracking error and related performance index function. Based on the distributed policy gradient reinforcement learning (RL) theory, a data-driven distributed optimal control strategy is obtained to guarantee the bipartite consensus of all agents' position and velocity states. In addition, the offline data sets ensure the learning efficiency of the system. These data sets are generated by running the system in real time. Besides, the designed algorithm is an asynchronous version, which is essential to solve the challenge caused by the computational ability difference between nodes in MASs. Then, by means of the functional analysis and Lyapunov theory, the stability of the proposed MASs and the convergence of the learning process are analyzed. Furthermore, an actor-critic structure containing two neural networks is used to implement the proposed methods. Finally, a numerical simulation shows the effectiveness and validity of the results.

Decentralized Control for Large-Scale Systems With Actuator Faults and External Disturbances: A Data-Driven Method.

This article investigates optimal control for a class of large-scale systems using a data-driven method. The existing control methods for large-scale systems in this context separately consider disturbances, actuator faults, and uncertainties. In this article, we build on such methods by proposing an architecture that accommodates simultaneous consideration of all of these effects, and an optimization index is designed for the control problem. This diversifies the class of large-scale systems amenable to optimal control. We first establish a min-max optimization index based on the zero-sum differential game theory. Then, by integrating all the Nash equilibrium solutions of the isolated subsystems, the decentralized zero-sum differential game strategy is obtained to stabilize the large-scale system. Meanwhile, by designing adaptive parameters, the impact of actuator failure on the system performance is eliminated. Afterward, an adaptive dynamic programming (ADP) method is utilized to learn the solution of the Hamilton-Jacobi-Isaac (HJI) equation, which does not need the prior knowledge of system dynamics. A rigorous stability analysis shows that the proposed controller asymptotically stabilizes the large-scale system. Finally, a multipower system example is adopted to illustrate the effectiveness of the proposed protocols.

Event-Triggered SMC for Networked Markov Jumping Systems With Channel Fading and Applications: Genetic Algorithm.

The event-triggered sliding-mode control (SMC) for discrete-time networked Markov jumping systems (MJSs) with channel fading is investigated by means of a genetic algorithm. In order to reduce resource consumption in the transmission process, an event-triggered protocol is adopted for networked MJSs. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to delay, random noise, and amplitude attenuation in a networked environment. With the aid of a common sliding surface, an event-triggered SMC law is designed by adjusting the system network mode. Under the framework of stochastic Lyapunov stability, sufficient conditions are constructed to ensure the mean-square stability of the closed-loop networked MJSs, and the sliding region is reached around the specified sliding surface. Moreover, based on the iteration optimizing accessibility of objective function, an effective SMC approach under genetic algorithm is proposed to minimize the convergence region around the sliding surface. Finally, the effectiveness of the proposed method is proved by the F-404 aircraft model.

Dual periodic event-triggered control for multi-agent systems with input saturation.

This paper is concerned with the periodic event-triggered consensus of multi-agent systems subject to input saturation. Due to the nonlinearity caused by the input saturation constraint, the accuracy of the event-triggered mechanism to screen data will be reduced. To deal with this problem, a novel dual periodic event-triggered mechanism is first proposed, in which a saturation-assisted periodic event-trigger and a complemental periodic event-trigger work synergistically to screen data more efficiently under the input saturation constraint. In addition, considering the various disturbances in the environment, a more general mixed H∞ and passive performance is introduced to describe the disturbance attenuation level. Based on the Lyapunov-Krasovskii functional, some less conservative consensus criteria are obtained for the multi-agent systems. In addition, under different input saturation constraints, the relationship between the disturbance attenuation level and the data transmission rate is explored. After that, a particle swarm optimization algorithm is a first attempt to estimate and enlarge the region of asymptotic consensus. Finally, an example is given to verify the effectiveness and superiority of our proposed method.

A Dynamic-Memory Event-Triggered Protocol to Multiarea Power Systems With Semi-Markov Jumping Parameter.

This work deals with the dynamic-memory event-triggered-based load frequency control issue for interconnected multiarea power systems (IMAPSs) associated with random abrupt variations and deception attacks. To facilitate the transient faults, a semi-Markov process is addressed to model the dynamic behavior of IMAPSs. In order to modulate transmission frequency, a novel area-dependent dynamic-memory event-triggered protocol (DMETP) is scheduled by resorting to a set of the historically released packets (HRPs), which ensures better dynamic performance. From the viewpoint of the defender, the randomly occurring deception attack is taken into account, which is regulated by a Bernoulli-distributed scalar. Benefitting from the DMETP scheduling, a novel framework of the memory-based asynchronous control strategy is formulated, in which the hidden semi-Markov model is adopted to reveal the mode mismatches. Based on the Lyapunov theory, sufficient conditions are established to ensure the stochastic stability of the resulting systems. In the end, the simulation result is presented to reveal the efficiency of the proposed dynamic-memory event-triggered-based approach.

Event-Triggered Approximate Optimal Path-Following Control for Unmanned Surface Vehicles With State Constraints.

This article investigates the problem of path following for the underactuated unmanned surface vehicles (USVs) subject to state constraints. A useful control algorithm is proposed by combining the backstepping technique, adaptive dynamic programming (ADP), and the event-triggered mechanism. The presented approach consists of three modules: guidance law, dynamic controller, and event triggering. First, to deal with the "singularity" problem, the guidance-based path-following (GBPF) principle is introduced in the guidance law loop. In contrast to the traditional barrier Lyapunov function (BLF) method, this article converts the USV's constraint model to a class of nonlinear systems without state constraints by introducing a nonlinear mapping. The control signal generated by the dynamic controller module consists of a backstepping-based feedforward control signal and an ADP-based approximate optimal feedback control signal. Therefore, the presented scheme can guarantee the approximate optimal performance. To approximate the cost function and its partial derivative, a critic neural network (NN) is constructed. By considering the event-triggered condition, the dynamic controller is further improved. Compared with traditional time-triggered control methods, the proposed approach can greatly reduce communication and computational burdens. This article proves that the closed-loop system is stable, and the simulation results and experimental validation are given to illustrate the effectiveness of the proposed approach.

Output-Feedback Control for Fuzzy Singularly Perturbed Systems: A Nonhomogeneous Stochastic Communication Protocol Approach.

In this study, the output-feedback control (OFC) strategy design problem is explored for a type of Takagi-Sugeno fuzzy singular perturbed system. To alleviate the communication load and improve the reliability of signal transmission, a novel stochastic communication protocol (SCP) is proposed. In particular, the SCP is scheduled based on a nonhomogeneous Markov chain, where the time-varying transition probability matrix is characterized by a polytope-structure-based set. Different from the existing homogeneous Markov SCP, a nonhomogeneous Markov SCP depicts the data transmission in a more reasonable manner. To detect the actual network mode, a hidden Markov process observer is addressed. By virtue of the hidden Markov model with partly unidentified detection probabilities, an asynchronous OFC law is formulated. By establishing a novel Lyapunov-Krasovskii functional with a singular perturbation parameter and a nonhomogeneous Markov process, a sufficient condition is exploited to guarantee the stochastic stability of the resulting system, and the solution for the asynchronous controller is portrayed. Eventually, the validity of the attained methodology is expressed through a practical example.

Tracking performance for NCSs with multi-communication constraints.

In this paper, the tracking performance of multiple input multiple output (MIMO) networked control systems (NCSs) with multiple communication constraints is studied. The effects of network induced factors (packet loss and network delay) and basic communication constraints (bandwidth, encoding-decoding and different noise) are considered. By inner-outer factorization and Youla parameterization, the mathematical expressions of the performance of the one-degree of freedom (1DOF) controller and two-degree of freedom (2DOF) controller are obtained, respectively. It shows that internal characteristics and network constraints have different effects on the tracking performance of the plant, and the performance of the 2DOF controller is better than that of the 1DOF controller. Finally, several simulation examples are provided to verify the influence of each constraint.

Improved Stability Analysis Results of Generalized Neural Networks With Time-Varying Delays.

This article studies the stability problem of generalized neural networks (GNNs) with time-varying delay. The delay has two cases: the first case is that the delay's derivative has only upper bound, the other case has no information of its derivative or itself is not differentiable. For both two cases, we provide novel stability criteria based on novel Lyapunov-Krasovskii functionals (LKFs) and new negative definite conditions (NDCs) of matrix-valued cubic polynomials. In contrast with the existing methods, in this article, the proposed criteria do not need to introduce extra state variables, and the positive-definite constraint on the novel LKF is relaxed. Moreover, based on free-matrix-based inequality (FMBI) and new NDCs, the stability conditions are expressed as linear matrix inequalities (LMIs). Eventually, the merits and efficiency of the proposed criteria are checked through some classical numerical examples.