International Journal of Adaptive Control and Signal Processing最新文献

This article studies the dynamic event-triggered $��{�H�}_{�\infty �}�$ control problem for switched Takagi-Sugeno (T-S) fuzzy systems against multiple cyber attacks. A new multiple cyber attack model is established by considering random false data injection attacks and aperiodic denial-of-service attacks. Then, to further efficiently utilize network communication resources, a dynamic event-triggered mechanism (ETM), which includes a nonnegative dynamic variable, is constructed. Furthermore, the time-delay switched T-S fuzzy system considering dynamic ETM and multiple cyber attacks is derived by utilizing model transformation methods. Moreover, sufficient conditions for globally asymptotically stability and $��{�H�}_{�\infty �}�$ performance are derived by utilizing multiple Lyapunov functions and average dwell time method. Finally, an example is provided to validate the effectiveness of dynamic ETM.

This article considers the problem of edge-based event-triggered output feedback control for linear multi-agent systems (MASs) with state-independent process and measurement noise under undirected communication topologies. The main breakthroughs are the design of distributed event-triggered output feedback control strategy and the stochastic stability analysis. Toward this, first, according to Kalman filtering theory, An observer is contrasted to optimally estimate the state of each agent. Next, a novel edge-based event-triggered mechanism (EBETM) is designed to reduce the communication frequency among agents effectively, and a positive interval between events is enforced in EBETM, which can eliminate the Zeno behavior. Then, stability of the estimation error and the consensus error systems is analyzed, the execution error is estimated and the almost sure consensus is achieved. Finally, a numerical example is given to show that MASs achieves almost sure consensus and there is a positive interval between the events of all edges.

This article considers the output regulation problem of continuous-time linear systems subject to input saturation in the scenario that the system dynamics are unknown. Based on the low-gain technique and the output feedback technique, we construct an output feedback control law to solve the output regulation problem with input saturation. Since the system dynamics are unknown, we estimate the feedback and feedforward terms in the control law with a two-step data-driven method. An output feedback adaptive dynamic programming algorithm is proposed to estimate the feedback term. An online updating algorithm based on output tracking error is proposed to estimate the feedforward term directly without solving the output regulation equations. Both algorithms don't rely on the measurement of the internal states of the exosystem and the original system. We show that the input saturation caused by the iterative feedforward gain matrix in the online updating algorithm doesn't affect the convergence of the algorithm. With these two data-driven algorithms, the control law doesn't rely on the dynamics anymore. The low-gain control law obtained by the model-free method prevents input saturation from occurring and hence solves the output regulation problem. Finally, a numerical example is provided to validate the theoretical results.

In this article, for switched nonlinear time-delay multi-agent systems (MASs) under directed graphs, an adaptive event-triggered (ET) consensus problem is investigated. Each agent's switching signal is allowed to be arbitrary and asynchronous. To save communication resources, an adaptive ET consensus strategy is designed. In addition, the Pade approximation approach is introduced to transform the system into an input delay-free system. A Lyapunov–Krasovskii functional is employed to analyze the effect of time-varying delays on system stability. It is proved that the consensus errors of switched NMASs are bounded, and the Zeno behavior does not exist. Finally, the presented strategy's effectiveness is verified by simulation results.

This work presents an approach to control a type of stochastic cascade nonlinear systems, which involve external disturbances and nonlinear terms. The external disturbances under the consideration are not measurable and regarded as extended states. The proposed method addresses the problem of unmeasured states and external disturbances by introducing an extended state observer (ESO). Based on the above work, a new stochastic nonlinear system including ESO is established. To ensure the stability and boundedness of the system, an adaptive output feedback controller is designed by using stochastic integrated input-to-state stability and backstepping design technique. This controller guarantees that all the states remain almost surely bounded and globally asymptotically stable in probability. Simulation experiments are conducted to validate the effectiveness of the proposed control scheme.

Considering the existence of time delay and interval uncertainty, a set-membership state estimation problem based on zonotopes is studied for discrete-time switched systems subject to unknown but bounded noises. To conform the actual situation, a state observer asynchronous with the subsystem is designed. The $��{�L�}_{�\infty �}�$ performance is introduced to attenuate the effect of noises in the observer design. By dealing with the interval uncertainty, the design approach of $��{�L�}_{�\infty �}�$ observer is given and transformed into a convex optimization problem which can be solved off-line. The zonotope and the estimation interval containing state are provided based on the $��{�L�}_{�\infty �}�$ observer. Finally, a numerical example is given to verify the effectiveness of the proposed algorithm.

Kalman smoother is an effective algorithm to estimate the state of the dynamic systems with Gaussian noise. However, when the system is affected by non-Gaussian noise, the traditional Kalman smoother may suffer severe performance degradation, since it is derived from the minimum mean square error criterion. By introducing the maximum correntropy criterion, which accounts for all higher order moments and has the ability to resist non-Gaussian noise, this article studies the state estimation problem of the bilinear state-space system with non-Gaussian noises and parametric uncertainties. The bilinear system with parametric uncertainties is transformed into a linear time-varying system, and a robust fixed-point Kalman filter algorithm is derived based on the Cauchy kernel-based correntropy criterion. To improve the state estimation accuracy, a Cauchy kernel-based fixed-point Kalman smoother (CK-FPKS) algorithm is presented by introducing the backward smoothing. Simulation results show the effectiveness of the proposed algorithm.

In this article, an adaptive deep neural network (DNN) optimized control strategy is developed for a class of nonlinear strict-feedback systems with prescribed performance. First, the DNN is applied to approximate the unknown function, and the weight update law is designed to reduce the mathematical challenge based on the first-order Taylor's series. Second, the optimized backstepping technique is utilized to construct virtual and actual controllers in the backstepping process to achieve the overall control optimization of the system. Next, a control strategy based on the time-varying switching function and the quartic barrier Lyapunov function is employed to achieve the prescribed performance. Then, the tracking error can converge to the prescribed accuracy within the prescribed time, and every signal within the system has a bound. Finally, the particle swarm optimization algorithm is utilized to search for the designed parameters and simulation examples to verify the effectiveness of the control strategy.

Quadrotor transportation systems have been widely utilized in both commercial and civilian fields. However, challenges arise due to the variable payload mass and unpredictable wind disturbances during cargo transport, potentially leading to excessive payload swinging and system instability. To tackle this issue, this article proposes an adaptive sliding mode control approach that concurrently achieves trajectory tracking for the quadrotor and mitigates payload swinging. In the outer loop subsystem, the quadrotor dynamics model is partitioned into two components that is, actuated and underactuated components. Sliding surfaces are designed based on this divided system model, and two adaptive laws are designed to compensate for payload mass uncertainty and unknown wind disturbances. The system's asymptotic stability is assured through the application of the Lyapunov theorem. In the inner loop subsystem, a disturbance rejection controller is formulated. Comparative simulation results conclusively demonstrate the outstanding performance of the proposed method in terms of trajectory tracking and robustness.