This paper investigates the adaptive finite-time containment control problem for a class of nonlinear multiagent systems (MASs) under false data injection (FDI) attacks. On account of the fact that instability is inevitable when the false datas are injected into the researched system, the backstepping technique based on the modified coordinate transformation is applied to eliminate the impact of the FDI attacks. Furthermore, the “dynamic surface control”(DSC) approach is utilized to overcome the issue of “explosion of complexity” caused by repeatedly taking derivatives for the virtual control laws. Then, by designing an observer to refactor the immeasurable states of the MASs, a finite-time adaptive output-feedback tracking containment controller is constructed. It is shown that all the signals in the closed-loop systems are semi-globally practical finite-time stability(SGPFS), and the outputs of all the followers converge to the convex hull spanned by the multiple leaders’s outputs. Besides, the observer errors and the containment errors can converge to a small neighborhood of the origin in finite time. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed containment control protocol.

This article investigates the distributed adaptive fuzzy finite-time fault-tolerant consensus tracking control for a class of unknown nonlinear high-order multiagent systems (MASs) with actuator faults and high powers (ratio of positive odd rational numbers). The fault models include both loss of effectiveness and bias fault. Compared with existing similar results, the MASs considered here are more general and complex, which include the special case when the powers are equal to 1. Besides, the functions in this article are completely unknown and do not need to satisfy any growth conditions. In the backstepping framework, an adaptive fuzzy fault-tolerant consensus tracking controller is designed via adding one power integrator technique and directed graph theory so that the controlled systems are semiglobal practical finite-time stability (SGPFTS). Finally, numerical simulation results further verify the effectiveness of the developed control scheme.

For nearly a decade, quality-related fault detection algorithms have been widely used in industrial systems. However, the majority of these detection strategies rely on static assumptions of the operating environment. In this paper, taking the time series of variables into consideration, a dynamic kernel entropy component regression (DKECR) framework is proposed to address the instability of quality-related fault detection due to the existing dynamic characteristics. Compared with the typical kernel entropy component analysis method, the proposed method constructs the relationship between process states and quality states to further interpret the direct effect on the product taken by the fault. In the proposed approach, process measurements are converted to a lower-dimensional subspace with a specific angular structure that is more comprehensive than traditional subspace approaches. In addition, the angular statistics and their relevant thresholds are exploited to enhance the quality-related fault detection performance. Finally, the proposed method will be compared with three methods by means of a numerical example and two industrial scenarios to demonstrate its practicality and effectiveness. Note to Practitioners—This paper studies a quality-related fault detection problem for the dynamic nonlinear industrial system. Controlling and measuring the quality state is challenging for the high-level monitoring system of the manufacturing process due to the nonlinear dynamic feature in states. This paper proposes a new data-driven method based on the kernel entropy component analysis method to assess the correlation between the quality and fault in the industrial system, reducing unnecessary overhaul and maintenance. Based on the autoregressive moving average exogenous algorithm, the proposed method captures the dynamic interaction between the process states to decrease false alarms. In the experimental section, the DKECR method outperforms the compared approaches, which can provide stable fault detection results. Additionally, the unique angle structure of the proposed method can supply more information for engineers’ monitoring needs.

This paper investigates the connectivity preservation and collision avoidance problems of a multi-unmanned surface vehicle (USV) system. In order to achieve these two goals more effectively, an improved artificial potential function (APF) is designed. For the sake of solving the issue of nonlinear disturbance, a fuzzy sliding mode control method combined with fuzzy radial basis function neural network (Fuzzy-RBFNN) is introduced. Then by Lyapunov method, it can be proved that the system which employs the control scheme proposed in this paper is stable. In addition, the theoretical deduction proves that the USV can track a given ideal signal, and the connectivity preservation and collision avoidance of the multi-USV system can be achieved during formation. Finally, a multi-USV system model including four USVs is established, and the control strategy designed in this paper is adopted to conduct simulation experiments with MATLAB. The results testify that this control project is valid.