Asian Journal of Control最新文献

For continuous-time complex-valued neural networks, this paper addresses the state-feedback stabilization issue via dynamic event-triggered protocol. Aiming at random parameters' switching, semi-Markov jump model surpasses the Markov jump model in terms of its generality, enabling us to effectively capture the occurrence of random abrupt alterations in both the structure and parameters of complex-valued neural networks. To optimize packet transmission, a new dynamic event-based protocol is introduced to judge whether the previous signal transmission continues. The design of this protocol takes into full consideration the imaginary part characteristics of the system, while also integrating the system modes and dynamic variables. Utilizing an appropriate Lyapunov functional that contains auxiliary internal dynamical variables, the desired stability is proposed. Eventually, the effectiveness of theoretical findings is ultimately validated through two numerical simulations.

In this article, the issue of adaptive predefined-time control for high-order switched systems is researched. Neural networks (NNs) are introduced to approximate the uncertain nonlinear functions. In particular, a novel predefined-time convergence filter is proposed to refrain from the problem of repeated differentiation of virtual controllers. On the basis of the backstepping recursion technique and the common Lyapunov function (CLF) approach, a neural adaptive predefined-time dynamic surface control (DSC) scheme is proposed that can demonstrate all the signals in closed-loop systems are bounded and the tracking error can converge to a small area near zero within predefined time. The simulation results illustrate the effectiveness of the proposed control scheme.

In this article, the problem of bipartite time-varying formation (BTVF) tracking for multi-agent systems (MASs) under signed digraphs is investigated, in which the inputs of leaders are unknown and bounded. A novel kind of adaptive nonsmooth BTVF tracking protocol for MASs is proposed that did not utilize eigenvalue of Laplacian matrix. Based on the state observer, the problem that the system states are not measurable is solved. In addition, in order to avoid undesirable chattering caused by leaders' nonzero inputs, continuous protocols are proposed. And in this case, the error is uniformly ultimately bounded. Lyapunov method reflects the convergence analysis of the submitted protocols. At last, the desired theoretical results are achieved on a multirobot system.

This paper studies the cooperative output regulation problem for heterogeneous multi-agent systems (MASs) subject to false data injection (FDI) attacks, which means the malicious cyber-attackers may inject false data into the communication topology randomly. To ensure that each follower agent can track the leader agent accurately, we construct both an observer and a control law for each follower agent. Since the FDI attacks may exist in the communication network, a filter with an adaptive compensator, which can estimate attack signals dynamically, is equipped for each observer, such that the filter can neutralize attacks when malicious attackers launch attacks. Therefore, the cooperative output regulation can be achieved under the control law based on the observer no matter whether there exist attacks or not. Finally, a numerical example is provided for validating the theoretical results.

This paper mainly addresses the bipartite time-varying formation tracking (BTVFT) problem for networked Euler–Lagrange systems (NELSs) with external disturbances and parametric uncertainties, considering a directed signed communication network that encompasses both cooperative and competitive interactions. A novel nonsingular sliding mode control (SMC) method serves as the foundation for a predefined-time hierarchical control algorithm (PTHCA), introduced to tackle the BTVFT challenge for NELSs in a predefined time. This approach enables the settling time to be predetermined as a control parameter. Moreover, sufficient conditions for establishing the predefined-time stability of closed-loop systems are provided based on the Lyapunov argument. Simulations are conducted to demonstrate the effectiveness of the main findings.

In this paper, the tracking control problem is investigated for a class of discrete-time multidelayed systems under the accumulation-based event-triggered protocol. The considered phenomena of multidelays, which are extremely common in the real world, include the time-varying delays and the constant delays. The accumulation-based event-triggered protocol is deployed in the communication from sensors to the remote tracking controller in order to alleviate the communication burden. By utilizing the Lyapunov–Krasovskii functional and matrix inequality technology, sufficient conditions are presented to guarantee the exponential stability of the tracking error and the $��{�\ell �}_{�2�}�-�{�\ell �}_{�\infty �}�$ performance, and the controller gain is subsequently designed. Finally, a simulation example is conducted to verify the validity of the developed tracking control scheme.

In this article, we will examine the finite time stability (FTS) of $��\rho �$-Caputo neural network fractional systems (NNFS) with an order of $��0�<�\phi �<�1�$. Utilizing technical inequalities such as Gronwall and Hölder inequalities, we present some FTS results. Two numerical examples are provided to illustrate the theoretical findings.

The active shimmy control methods for electric vehicle driven by in-wheel motors (EV-DIM) have been proposed in the recent years. However, these methods assume that data obtained from sensors are accurate, despite the fact that sensor measurements are prone to error. This unknown measurement error can make shimmy control difficult. Additionally, current shimmy models are low degree-of-freedom, which simplifies control but decreases accuracy. In this paper, we address these issues using a sampled-data output control method based on a higher seven-degree-of-freedom (7DOF) shimmy model which includes the steering system, suspension, and electric wheel. We first construct a 7DOF electric wheel shimmy model and use Lagrange's theorem to derive the electric wheel shimmy dynamic equations. We then obtain system state equations that account for unknown sensor measurement error based on the 7DOF shimmy model. A sampled-data observer and controller are designed to attenuate or eliminate the shimmy phenomenon via a domination gain. Finally, we conduct numerical simulations and experiments to verify the effectiveness of our proposed method.

This paper studies the security control of direct current (DC) microgrids under deception attacks, and a dynamic event-triggered mechanism (DETM) is proposed to save constrained network bandwidth. The DETM can reduce the frequency of data communications and exclude Zeno behavior inherently. Then, a time-delay closed-loop system model is built, which integrates parameters of the DC microgrid, the DETM, deception attacks, injection current controller, and network-induced delays in one unified framework. Sufficient conditions are derived for globally exponentially ultimately bounded stability in mean square, which establish the relationship between system performance and the contributing factors such as the DETM, deception attacks, and network delays. Further, a co-design method is presented to derive the parameters of the controller and the DETM in just one step. Simulation results confirm the effectiveness of the proposed method for security control of DC microgrids, achieving 21.5% savings of communication resources while effectively stabilizing the system even 10.7% of the transmitted data that are manipulated by the deception attacks.

In this paper, we focus on the filtering issue for nonlinear non-Gaussian systems. Considering the limitations of the Gaussian function with a single kernel bandwidth, we design a novel extended version called weighted Gaussian function, which consists of a weighting coefficient and two distinct kernel bandwidths. The additional coefficient can directly balance two kernel bandwidths, which improves the flexibility and performance of the correntropy. We develop a cost function utilizing the suggested weighted Gaussian function and statistical linearization method, followed by deriving a maximum weighted correntropy filter based on the maximum correntropy criterion. The proposed algorithm is demonstrated to converge to a Gaussian filter as the kernel bandwidths approach infinity. In addition, we derive the corresponding information filter, which takes the form of information matrix and information vector. It is computationally efficient and easier to generalize to multisensor systems. The performance of the proposed algorithms is compared with other filters based on the third-order spherical cubature rule and the fixed point iteration technique in a target tracking system. Simulation results confirm the effectiveness of the new approaches.