Journal of The Franklin Institute-engineering and Applied Mathematics最新文献

This paper investigates the coverage control problem for Multi-Stratospheric Airship (MSA) system within unknown non-uniform wind field. To address the challenge of deploying airships strategically in regions with weak wind, an adaptive collaborative coverage control algorithm is proposed. The algorithm includes a coverage planning approach based on wind field estimation and an airship motion control approach. Firstly, as accurate wind field data in the mission area is unavailable, an adaptive estimation algorithm based on the Gaussian approximation method is proposed to estimate the wind data. This involves processing discrete wind field sampling information with a density function. Secondly, an event-triggered dynamic tracking controller with a neural network to handle model uncertainties is proposed to steer the MSA system approaching a near-optimal configuration. Additionally, convergence of the coverage system is proven through a Lyapunov-like proof. Simulation results illustrate the superior performance of the adaptive collaborative coverage control algorithm.

This paper is concerned with the adaptive neural networks (NNs) prescribed-time control problem for a class of strict-feedback nonlinear systems subject to unmeasured states via the self-triggered control (STC). By developing a new state observer with prescribed-time function, an adaptive NNs self-triggered controller is designed to solve the problem of prescribed-time performance (PTP). Due to the initiative of the STC, it has excellent practical significance in terms of contracting computing resources and network communication resources. With the proposed new strategy, the PTP of the closed-loop system can be guaranteed, and all the signals within the closed-loop system are bounded. Finally, the practicability and effectiveness of the above prescribed-time STC algorithm are verified via some physical simulations.

In this study, the sliding mode control (SMC) is investigated for discrete singular stochastic semi-Markov jump systems under a semi-Markov kernel. Based on the remarkable application background and advantages in modeling complex dynamic systems, singular semi-Markov jump systems are utilized to address discrete SMC issues. A mode-dependent sliding function with a singular matrix is considered to reduce the effects of the numerical problems. The main aim is to use an improved approach to eliminate the matrix power terms so that the underlying augmented system can realize mean-square admissibility. An appropriate SMC law is constructed to achieve the reachability of the quasi-sliding mode, overcoming the difficulty caused by parameter uncertainty. Finally, the effectiveness of the SMC approach is verified using a DC motor model.

This paper investigates the synchronization problem for Markovian jump complex dynamical networks (MJCDNs) subject to link attacks. An extended dynamic event-triggered (DET) control is proposed that takes into account limited network resources and time-varying delay constraints. The proposed method enhances the robustness and resource-saving ability of the DET mechanism, and avoids the Zeno phenomenon. It provides MJCDNs to have a buffering ability for external interference. Additionally, this paper introduces a robust model-based strategy to address scenarios where network attackers inject false data into the link channels between nodes. This approach reduces the complexity of processing MJCDNs subject to link attacks. By leveraging network topology and Lyapunov functions, the criteria are derived to achieve robust synchronization of MJCDNs under link attacks. Finally, simulation examples including Chua’s circuit are presented to verify the validity and superiority of the results.

This paper focuses on a trajectory tracking problem for unmanned underwater vehicles (UUVs) subject to current disturbances and static obstacles. A double-loop framework is established. The kinematic governor employs model predictive control(MPC), which takes into account the UUV’s kinematic characteristics as well as the conditions for obstacle avoidance when calculating the control command and provides an optimal velocity command input for the dynamic controller. Then, the dynamic controller is developed based on a fast finite-time extended state observer (FESO) and a fast adaptive integral terminal sliding mode controller (FAITSMC). The construction of the fast FESO integrates UUV’s dynamics model, a proportional integral velocity variable, and a fractional order term of the output observation error, which can identify model uncertainties and external disturbances in finite time. By means of adaptive uncertainty compensation, the FAITSMC enables the error between actual speed and speed reference to converge to a minimum quickly. By applying Lyapunov stability theory and the finite-time analysis technique, sufficiency criteria are established to guide and keep the UUV on a reference trajectory via an inner-outer loop control structure. Finally, simulation examples in different scenarios are presented to verify the feasibility and effectiveness of the derived theoretical results.

This paper investigates the problem of finite-time adaptive fuzzy event-triggered control for a class of uncertain nonlinear systems with input quantization. The nonlinear systems under consideration contain unmodeled dynamics and time-varying delays. Fuzzy logic systems are used to handle unknown nonlinear terms. Additionally, finite-time filters are introduced to solve the problem of “explosion of complexity” as well as to ensure that the derivative of the virtual controller can be approximated in finite time. The Lyapunov–Krasovskii function is used to handle time-varying delays. The hysteresis quantizer is used to ensure adequate precision when there is a low transmission rate requirement. The control strategy combines event-triggered mechanisms and quantization technology to ensure that all signals of the closed-loop system remain bounded in finite time. Finally, two examples are included to demonstrate the effectiveness of the proposed controller.

In this paper, we consider Krylov subspace methods based quaternion tensor form for a class of generalized Sylvester quaternion tensor equation. A novel quaternion tensor product and a global quaternion Arnoldi process based on the tensor form are proposed. Then, the quaternion tensor Krylov subspaces and their orthogonal bases are constructed via the proposed global quaternion Arnoldi process. The quaternion full orthogonalization method based on tensor format (denoted by QFOM${}_{-}$BTF) and the quaternion generalized minimal residual method based on tensor format (denoted by QGMRES${}_{-}$BTF) are established to solve the quaternion tensor equation. The theoretically analyze results of the proposals are discussed on the quaternion tensor form. Finally, we demonstrate the feasibility of QFOM${}_{-}$BTF and QGMRES${}_{-}$BTF methods on some numerical examples including the color video restoration.

Software-defined network (SDN) platforms play a key role in providing security against today's Internet attacks. SDNs decouple the control plane from the data plane to maximize network performance. A DDOS attack is one of several in cloud-based networks. SDNs play a crucial role in controlling DDoS attacks and protecting end nodes like IoT nodes, as well as other computing devices, in large-scale cloud networks. This paper provides an efficient approach to DDoS attack detection and prevention using machine learning algorithms. The paper analyses the performance of SDNs in IoT systems, incorporating a huge set of computing devices that use multi-controllers. It also proposes an effective method to handle DDoS attacks. DDoS attacks are generated from IoT end devices in the infrastructure layer, which targets resources via an SDN-controlled testbed. The proposed ML method outperforms existing methods in terms of accurately and effectively detecting and mitigating flooding DDoS attacks with 99.99% accuracy. The proposed work's results are also compared to the results of other articles to prove the effectiveness of the results.

This paper investigates the multistability of recurrent neural networks (RNNs) with unbounded time-varying delays whose activation functions are general periodic functions. The activation function can be linear, nonlinear, or have multiple corner points, as long as it satisfies Lipschitz continuous condition. According to the characteristics of the parameters of the RNNs and the state space division method, the number of equilibrium points (EPs) of the RNNs is split into three categories, which can be unique, finite, or countable infinite. Some sufficient conditions for determining the number of EPs are presented, the criterion of asymptotically stable EPs is deduced, and the attraction basins of stable EPs are estimated. Furthermore, the multistability results in this paper are an extension of some previous results. The theoretical results are validated using three numerical simulation examples.