Asian Journal of Control最新文献

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.

This work focuses on the finite-time synchronization (F-TS) for nonidentical delayed neural networks (DNNs) including Caputo fractional partial differential operator and reaction–diffusion terms. A novel F-TS lemma is proposed by constructing a new Caputo differential inequality. Applying the new lemma and designing two hybrid controllers with time delay and the sign function, the F-TS criteria on the introduced Caputo reaction–diffusion nonidentical DNNs under the Neumann boundary condition are derived by making use of the Filippov differential inclusion, Green's theorem, and fractional Razumikhin theorem. The conditions are expressed through the algebraic inequality which can greatly decrease the computation in checking the F-TS performance. Moreover, the validity and correctness of the F-TS results are illustrated by selecting various orders, spatial positions, and diffusion parameters.

This paper is centered on the adaptive asymptotic tracking control of multi-input multi-output uncertain strict-feedback nonlinear systems in the presence of both multiplicative and additive sensor faults. Through adaptive accommodation techniques, the proposed control scheme can dynamically compensate for sensor faults, thereby ensuring precise and stable control. The control scheme is devised using the backstepping design that incorporates positive integrable time-varying functions, thereby ensuring the boundedness of all signals in the closed-loop system and guaranteeing that the output tracking error asymptotically converges to zero. Additionally, extensive simulations and experimental evaluations conducted on the Quanser platform with 2-link flexible-joint robots demonstrate the efficacy of the proposed control scheme in mitigating the impact of sensor faults and achieving accurate tracking performance.

Due to the limited payload capacity of single-quadrotor unmanned aerial vehicles (UAVs) in cargo transportation, there is a pressing need for the collaborative transportation of heavy goods using multiple quadrotor UAVs. In this context, the dual-quadrotor aerial transportation system stands out for its heightened research significance, driven by its stringent requirements for controller swing reduction performance. In practical transportation scenarios, uncertainties such as disturbances and emergency avoidance constraints elevate the risk associated with excessive variations in the relative distances between quadrotor aircraft during collaborative transportation. Building upon the established nonlinear model by D'Alembert, this paper proposes a nonlinear coordinated control strategy based on the barrier Lyapunov function. The strategy employs an innovative energy storage approach, incorporating cooperative error constraints to effectively restrict relative errors between quadrotor aircraft, thereby mitigating the risk of collisions. Furthermore, this control strategy facilitates rapid convergence of the positions of quadrotor aircraft and the swing angles of suspended loads. Theoretical analyses, grounded in LaSalle's invariance theorem, provide evidence of the stability of the closed-loop system. Finally, comparative assessments conducted using the Matlab/Simulink visualization platform validate the superior control performance of the designed controller in collaborative transportation scenarios.

For a first-order nonlinear multi-agent system who is subject to false data injection (FDI) attacks on agents' actuators and sensors, agents execute a distributed resource allocation algorithm according to the compromised control inputs and interactive information such that the multi-agent system is unstable and agents' decisions deviate from the optimal resource allocation. At first, we analyze the robustness of the distributed resource allocation algorithm under the FDI attacks. Then, a resilient distributed algorithm is proposed to solve the distributed resource allocation problem by resisting the adverse effect of the attacks. In detail, the unknown nonlinear term and the false data injected in agents are considered as extended states that can be estimated by extended state observers. The estimation is used in the feedback control to suppress the effect of the FDI attacks. As a result, the designed resilient algorithm ensures that agents' decisions converge to the optimal allocation without requiring any information about the nature of the attacks. An example is given to illustrate the results.

This paper studied the problem of dissipative control for networked T-S fuzzy switched systems under denial of service (DoS) attacks via a memory-based adaptive event-triggered mechanism. Since the system mode and controller mode do not match exactly, the asynchronous control is designed. The memory-based adaptive event-triggered mechanism can achieve better performance by using the historical data released. Though constructing a controller mode dependent Lyapunov functional, the dissipative performance of the closed-loop system can be guaranteed. Finally, a numerical example is proposed to illustrate the superiority of the approach.

Based on the so-called lazy gradient information, this note proposes two communication-reduced distributed optimization algorithms over undirected multi-agent networks. The lazy gradients refer to some gradients that do not change much in the past iterations and thus may not be distributed among agents which correspondingly reduces the communication load in the networks. For both the deterministic and the stochastic frameworks, the asymptotic properties of the distributed optimization algorithms are established. Compared with the existing literature using the lazy gradient information, the proposed algorithms in the paper are fully distributed and more suitable for the situation of decentralized multi-agent networks. The effectiveness of the proposed algorithms is also testified through numerical simulations.