International Journal of Adaptive Control and Signal Processing最新文献

Anti-disturbance performance is crucial for unmanned nonlinear systems in ensuring system stability and safe flight, which has drawn tremendous research attention over past decades. This paper investigates a neural network-based adaptive backstepping controller for attitude of an underactuated tandem rotor helicopter, in which both mass imbalance of two rotors and crosswind load disturbance is considered. First, underactuated dynamical model and detailed wind field as well as load model are established for tandem rotor helicopter system. Then, an adaptive backstepping attitude controller is developed to regulate the motion of three angles simultaneously. The mass difference of two rotors is estimated by adaptive algorithm, while radial basis function (RBF) neural network based on gradient descent algorithm is introduced to identify crosswind load in real time. In addition, the lynchpin of gradient descent algorithm is to construct the neural network approximation error, which is indirectly obtained by establishing interaction between the fitting error and the control error. Finally, comparative simulation tests with Matlab/Simulink are conducted to confirm the efficiency and superiority of the proposed control scheme.

This paper investigates stochastic discrete-time systems with multiplicative state-dependent and input-dependent noise via a novel adaptive dynamic programming(ADP) based control method combined with optimal stationary control techniques. Imposing a notably greater difficulty, the tracking control problem without the knowledge of system dynamics and the reference system has been generalized that the system dynamics do not have to be Hurwitz which is more practically relevant. An augmented system has been constructed while a discount factor has been introduced into the cost function. After the discount factor has been brought in to solve the stochastic algebraic Riccati equation(SARE), the linear quadratic tracking(LQT) problem has been proved to be well-posed. Hence, we develop a second-order moment formulation to solve the SARE. Based on stochastic adaptive control, a novel on-policy ADP algorithm has been proposed to solve the LQT problem by only the state and input data. The convergence and stability of the novel ADP algorithm has been rigorously investigated and discussed. Finally, numerical simulations and practical experiments of two distinguished systems are performed to validate the effectiveness and practicability of the proposed ADP methodology.

This paper addresses the problem of event-triggered predefined-time adaptive bipartite consensus control for nonlinear multi-agent systems (MASs) with actuator faults. By incorporating the command filter and error compensation mechanism into the backstepping design framework, the problem of “explosion of complexity” is effectively handled, while simultaneously reducing the effect of filtered errors. Moreover, the utilization of the adaptive compensation technique and event-triggered control strategy effectively handles unknown actuator faults and conserves communication resources. The singularity issue is resolved by utilizing the feature of hyperbolic tangent functions. It is proven that the closed-loop system is practically predefined-time stable, and the bipartite consensus errors converge to a small neighborhood of the origin within a predefined time. Finally, simulation results confirm the effectiveness of the proposed predefined-time control method.

This paper presents a dissipative filtering approach for a specific class of discrete-time switched interconnected systems (DTSIS). The study considers discrete-time subsystems with coupled states, while accounting for disturbances, sensor failures, and time-varying delays. Also, the DTSIS is subject to uncertainties in the system parameters, which may affect its robustness, stability, and performance. Sensor signals are modeled as sequences of Bernoulli-distributed white noise, reflecting potential errors and inconsistencies. The primary objective is to design a reliable dissipative filter capable of reducing the impact of external noise sources. The challenges posed by switching within the system are addressed using the average dwell time (ADT) method, which helps control the switching behavior. First, a filter is developed for the system under consideration, and the error dynamics are then analyzed through a linear matrix inequality (LMI) approach and Lyapunov-Krasovskii theory. Stability conditions for the augmented system, encompassing both the system and error states, are derived. Finally, a numerical example is provided to demonstrate the effectiveness and practicality of the proposed approach.

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.