In this paper, the problem of aero-engines ensemble modeling under sparse data is addressed. Firstly, the Makima method is used to interpolate and complement the sparse data by analyzing the experimental data of a specific real aero-engine. In this way, the data sparsity problem due to sampling or transmission is solved equally well. Secondly, the Nonlinear Auto-Regressive with Exogenous Inputs (NARX) neural network is brought in as the computational structure of the model. Based on the Automatic Neural Network Architecture Search (ANAS) method, the hyperparameters of the model can be searched efficiently, and the performance is improved. Third, a novel ensemble modeling method based on the Makima method, the NARX model, and the ANAS method is proposed to realize high-precision modeling throughout the entire operation process of the aero-engine from the idle state to the full throttle state. Finally, the proposed method is validated by simulations and experiments, and the results illustrate the innovation and correctness.

In this paper, the problem of fixed-time consensus tracking for a class of nonlinear multi-agent systems (MASs) with uncertainties and external disturbances is addressed. To solve this problem, a new fixed-time consensus tracking protocol with disturbance rejection abilities is proposed that consists of a fixed-time distributed observer (FTDO), a fixed-time disturbance observer (FDO), and a nonsmooth backstepping controller with lumped disturbance compensation (NBCDC). The fixed-time stabilities are analyzed correspondingly. Firstly, an FTDO is designed to estimate a leader’s output by using the topology of communication networks for each follower. Second, an FDO is developed to observe the lumped disturbances composed of model uncertainties and external disturbances. Finally, an NBCDC is proposed for improving the closed-loop performance. The simulation results show that the proposed method is effective.

This paper addresses the problem of fixed-time consensus tracking for a class of nonlinear multiagent systems (MASs) with disturbances. We establish a novel fixed-time consensus tracking protocol with adaptive disturbance rejection capabilities, leveraging adaptive selective disturbance elimination (ASDE) technology. This protocol consists of a distributed fixed-time observer (DFTO), a fixed-time disturbance observer (FTDO), and an adaptive selective disturbance elimination backstepping controller (ASDE) with adaptive lumped disturbance compensation abilities. The DFTO estimates the leader’s output using the communication network topology of each follower, while the FTDO rapidly observes the lumped disturbances and their derivatives. By adding disturbance indicator terms and disturbance observation attenuation terms to the control law, the beneficial and harmful effects of disturbance are distinguished. Under favorable disturbance conditions, lumped disturbances can be used to accelerate tracking speed. If disturbances are harmful, they are adaptively compensated to improve tracking accuracy. Furthermore, the fixed-time stability of each part of the protocol is analyzed using Lyapunov theory. Simulation results show that, under different initial states and command inputs, the proposed method achieves faster convergence and smaller tracking errors compared to the adaptive conditional disturbance negaton backstepping controller (ACDN), conditional disturbance negation backstepping controller (CDN), and non-smooth backstepping controller (NBCDC), verifying the effectiveness of the proposed method. The research outcomes serve as a reference for future multiagent adaptive anti-disturbance cooperative control technology.