International Journal of Adaptive Control and Signal Processing最新文献

This paper proposes a novel high-precision distributed secure state estimation strategy for interconnected cyber-physical systems (CPSs) under false data injection (FDI) attacks. To address the challenge of poor error convergence by existing theoretical suppression of actuator attacks and sensor attacks, intermediate variables and observer matrices are constructed to decouple the impact of actuator attacks, thereby ensuring that the estimation errors of system states and sensor attacks converge asymptotically with an explicitly controlled decay rate. This approach overcomes the precision limitations inherent in conventional methods, significantly improving both estimation accuracy and practical applicability. For the estimation of actuator attacks, distributed intermediate observers are designed based on the $��{�H�}_{�\infty �}�$ method and the observer designed for system state estimation. All observer matrices for actuator and sensor attacks are derived by solving linear matrix inequalities (LMIs). The observer design and stability conditions are based on the original system framework, without employing the popular augmented methods, while reducing communication overhead. Numerical simulations validate the theoretical claims and demonstrate the superior performance of the proposed distributed secure state estimation strategy.

For an r-input m-output multivariable ARX system, it is commonly decomposed into m subsystems for identification. However, the corresponding identification algorithms incur a high computational burden because they fail to account for the coupling between variables within the subsystems. After parameterization, considering that all subsystem identification models share a common input information vector, we derive the coupled identification model for the entire system. Based on the obtained coupled identification model, this paper presents a hierarchical stochastic gradient identification algorithm and a hierarchical multi-innovation stochastic gradient identification algorithm, and their variants for multivariable ARX systems. Finally, the simulation example is provided to show the effectiveness of the proposed algorithms.

The resilience and robustness of model predictive control are typically contingent upon an adequately extended prediction horizon, yet it is constrained in real-time applications by limited computational resources. To tackle this issue, this paper introduces a finite-time Lyapunov-based model predictive control (FTLMPC) approach, independent of the prediction horizon, for unmanned surface vehicles (USVs) subject to external disturbances and denial-of-service (DoS) attacks. Firstly, a finite-time auxiliary control system is integrated within the FTLMPC framework. This system incorporates a finite-time extended state observer (FTESO) for precise disturbance estimation and a finite-time backstepping control law to guarantee zero tracking errors. Consequently, FTLMPC ensures finite-time stability during each DoS attack interval, effectively preventing error accumulation despite the presence of disturbances and DoS attacks. Secondly, a novel compensation scheme is introduced to mitigate the information loss induced by DoS attacks, wherein the compensation signal is solely derived from the control signal at the moment of the last successful transmission, thus minimizing reliance on the prediction horizon. It enables flexible adaptation of the prediction horizon and control performance according to the available computational resources. Finally, the simulation results validate the superior control performance of the proposed strategy.

This paper mainly studies the distributed fusion estimation problem for multi-sensor systems under mixed network attacks and event-triggered mechanisms. Here, the mixed network attacks consist of random deception attacks and denial-of-service (DoS) attacks. Specifically, when the DoS attacks occur, the predicted values of measurements are used for compensation, which avoids the severe impact of data loss caused by attacks on estimation performance and provides an effective data fault-tolerant mechanism for multi-sensor systems under attacks. In this case, both deception attacks and DoS attacks are incorporated into a unified research framework, and a mixed attack model is constructed by using Bernoulli random variables, which handles the problem of modeling complexity caused by the differences in characteristics between the two types of attacks. To better schedule information, an event-triggered mechanism is set up between the estimators and the fusion center. The timing of data transmission is determined by preset thresholds, which enable the on-demand allocation of communication resources in an attack environment and enhance the practicality of the system. On this basis, a recursive distributed fusion estimation method is proposed by using the information transmitted by local estimation and the prior fusion estimation. The cross-covariances about local estimations and those between local estimations and the fusion estimation can be obtained. Finally, the effectiveness of the distributed fusion estimation algorithm under mixed attacks and the event-triggered mechanism is shown by a practical simulation experiment.

It has been shown experimentally that incorporating dynamic adaptation gain/step-size (DAG) in variable step-size least mean squares (VS-LMS) parametric adaptation algorithms provides a significant acceleration of the adaptation/learning transients. The DAG is a filter acting on the correcting term before updating the estimated parameters. In this paper, the analysis of the VS-LMS algorithms incorporating a dynamic adaptation gain is done both in a deterministic and stochastic environment. Design of the DAG filter is discussed. Simulation results (adaptive line enhancer, filter identification) provide, on one hand, further evidence of the advantages of incorporating a DAG in VS-LMS algorithms and, on the other hand, validate the theoretical results.

This study focuses on the terminal angle constrained interception of maneuvering target by missile in three-dimensional (3D) coupled environment. Considering actuator failures and input saturation, an adaptive non-singular terminal sliding mode guidance law is proposed based on the predefined-time convergence theory. Its design features an adjustable convergence time parameter. First, an adaptive guidance law is developed, the states can converge to zero in the predefined time under ideal conditions. Subsequently, based on this foundation, another anti-saturation fault-tolerant guidance law is developed, which can realize the interception of targets in scenarios involving actuator failure and input saturation. Comprehensive simulation experiments validate the efficacy of the proposed algorithms.

A novel 6-DOF cooperative guidance and control law against a maneuvering target is proposed under full state constraints and non-ideal communication conditions. Taking the impact time and impact angle into account, a comprehensive integrated guidance and control (IGC) model with thrust is formulated. The user-defined flight limitations are well tackled by incorporating a nonlinear mapping function into the IGC dynamic, which brings about a fully state-constraint-free IGC model. Considering a multi-interceptor balanced system under switching topology and time-varying delay, a Lyapunov–Krasovskii function-based sufficient criterion is proposed, based on which an acceleration command in the direction of the line of sight (LOS) is generated to guarantee a simultaneous interception. The backstepping control technique combined with the fixed-time disturbance observer is implemented to formulate a robust fin deflection command in the normal direction of LOS. The overall desired transit and steady guidance performance is greatly enhanced by designing a novel prescribed performance control method with a self-adjusting mechanism. The simulation results show the effectiveness of the proposed cooperative guidance law.

In this article, a distributed secure filtering problem is studied for a class of discrete time-varying multi-rate systems with stochastic nonlinearities subject to false data injection (FDI) attacks over wireless sensor networks (WSNs). The state iteration method is employed to convert the multi-rate system to a single-rate one due to asynchronous state updates and sensor sampling cycles. In order to counter the malicious attacks, a detector featuring an adaptive decision rule is developed for each sensor and a compensation strategy is established based on a memory mechanism. The main objective of this article is to develop a distributed filter for addressing the multi-rate sampling scheme, hostile attacks and stochastic nonlinearities. An upper bound of the filtering error covariance is deduced via mathematical induction and the filter gains are formulated by minimizing this upper bound. Subsequently, a sufficient condition is provided to guarantee the uniform boundedness of the filtering error. Finally, illustrative simulations including comparative experiments are implemented to demonstrate the effectiveness of the proposed distributed filtering algorithm with a memory-based compensation strategy.

This article proposes a fixed-time prescribed performance controller with an adaptive disturbance observer to enhance disturbance rejection and dynamic performance in a permanent magnet synchronous motor (PMSM). Firstly, an adaptive control law designed using a fixed-time prescribed performance function ensures transient behavior and steady-state performance strictly adhere to preset boundary constraints. Secondly, a fixed-time command filter resolves the repeated differentiation issue of virtual control signals in backstepping, while an error compensation signal further improves tracking accuracy. Friction effects and unknown load torque are treated as lumped disturbances, with a novel adaptive disturbance observer developed to suppress these disturbances, thereby improving system robustness. Finally, simulations and experiments validate the algorithm's effectiveness.