In this paper, fixed-time consensus tracking for multiagent systems (MASs) with dynamics in the form of strict feedback affine nonlinearity is addressed. A fixed-time antidisturbance consensus tracking protocol is proposed, which consists of a distributed fixed-time observer, a fixed-time disturbance observer, a nonsmooth antidisturbance backstepping controller, and the fixed-time stability analysis is conducted by using the Lyapunov theory correspondingly. This paper includes three main improvements. First, a distributed fixed-time observer is developed for each follower to obtain an estimate of the leader's output by utilizing the topology of the communication network. Second, a fixed-time disturbance observer is given to estimate the lumped disturbances for feedforward compensation. Finally, a nonsmooth antidisturbance backstepping tracking controller with feedforward compensation for lumped disturbances is designed. In order to mitigate the “explosion of complexity” in the traditional backstepping approach, we have implemented a modified nonsmooth command filter to enhance the performance of the closed-loop system. The simulation results show that the proposed method is effective.

Gas fuel control valves play important roles in the control of gas flow in high-precision gas turbines. To clarify the influence of coupling between the structure and the fluid system, a thermal fluid–solid coupling mechanism is presented based on numerical investigations carried out using a dynamic mesh technique. Valve core deformation can affect the outlet gas flow accuracy. At 2% valve opening, the gas temperature contributes 93% to the deformation. The effect of deformation on the flow accuracy at 6% valve opening and 4% valve opening is increased by 4.8% and 7.3%, respectively. The fluctuation range of the gas temperature and pressure in front of the valve should be strictly controlled to ensure the high precision and high stability of the outlet flow. These results help to clarify the processes that occur in the valve flow path, leading to the flow control instability observed in the control valve.

During high-altitude simulation tests, the accurate reproduction of environmental conditions directly affects the ability to reliably evaluate the engine’s performance under simulated high-altitude conditions. Traditional physical models, though interpretable, often fall short in handling nonlinear dynamics and time-delayed effects caused by disturbances such as abrupt flow rate fluctuations. For this problem, this work proposes a novel hybrid modeling framework that integrates physical principles with deep learning architectures. The proposed approach incorporates three key innovations. First, a physics-guided residual learning scheme is introduced, where the theoretical outlet temperature derived from energy conservation laws and assumptions of symmetric inlet mixing serves as a prior, and a data-driven model corrects residual deviations. Second, a multiscale feature extraction module is constructed by combining a Transformer with DWT, capturing both long-term regularities and short-term fluctuations that often exhibit quasi-symmetric patterns in structured systems. Third, a perturbation-aware memory structure is designed, fusing a Transformer branch for long-term dependency modeling with an extended LSTM (xLSTM) branch for short-term dynamic sensitivity. The experimental results on real-world test datasets demonstrate that the proposed hybrid model significantly outperforms traditional physical models. Specifically, it achieves an MAPE of 0.0156 and a R² of 0.9867, indicating high predictive accuracy. The model not only achieves superior prediction accuracy but also preserves physical interpretability, making it a promising solution for intelligent control in industrial mixing systems.