The focus of this article is on the consensus problem of the multiagent system (MAS) with binary-valued quantized data under data tampering attacks. First, the properties of data tampering attacks are analyzed, and sufficient conditions are provided under which the attacks can effectively disrupt the consensus algorithm. Subsequently, inspired by the scheme of hierarchical estimation, a multitime scale consensus algorithm is proposed, enabling the MAS to achieve mean square consensus in the presence of tampering attacks. Next, the convergence speed of the consensus algorithm is analyzed, and it is demonstrated that the algorithm designed in this article retains the same upper bound for convergence speed under attacks as the two-time scale consensus algorithm without attacks. Finally, the effectiveness of the proposed method are validated through a signal frequency consensus experiment of the uncrewed aerial vehicles swarm system.

In this article, adaptive tracking control of finite impulse response (FIR) systems is studied with binary-valued measurements. An adaptive control strategy is proposed based on an online identification algorithm. First, the designed control inputs are proved to be bounded and satisfy a persistent excitation (PE) condition under the assumption of periodic and PE target signals, which ensures the convergence of the identification algorithm. Second, the convergence rate of the identification algorithm is proved to be O(1/t) and it depends on the true parameter instead of a priori information of the parameter, which is more intuitive. Due to the convergence and the convergence rate of the identification algorithm, we finally prove that the adaptive tracking control is asymptotically optimal and the tracking speed is faster than the previous control algorithm. The simulations are given to validate the developed results in this article.

This paper considers the identification problem of finite impulse response (FIR) systems with binary-valued observations under the assumption of fixed threshold and bounded persistently excitations. A recursive projection algorithm is constructed to estimate the unknown parameter. For first-order FIR systems, the convergence properties of the algorithm are analyzed theoretically. With mild conditions on the weight coefficients in the parameter update, the algorithm is proved to be convergent in mean square and the convergence rate can be the reciprocal of the number of observations, which has the same order as the optimal estimation when the system output is exactly known. Furthermore, it is also shown that the Cramér-Rao (CR) lower bound is achieved asymptotically with proper weight coefficients, which indicates that the algorithm is optimal in the sense of asymptotic efficiency. Some numerical examples are simulated to demonstrate the effectiveness of the proposed algorithm in both first-order and high-order FIR systems.