This paper develops an innovative event-triggered adaptive control scheme with guaranteed prescribed performance for nonlinear systems under simultaneous multiple faults, including actuator and external faults. The primary objective is to guarantee system stability and tracking performance despite the coexistence of these faults. In the backstepping design, multi-dimensional Taylor networks (MTNs) are employed to approximate the external fault function and other unknown nonlinear functions. To guarantee transient and steady-state performance, an asymmetric state transformation function is introduced, effectively constraining the tracking error within prescribed bounds. Moreover, to optimize network resource utilization, an event-triggered mechanism is incorporated, significantly reducing communication burden while maintaining system stability. Theoretical analysis proves that all signals in the system remain bounded, and the output tracks the reference signal under the proposed scheme. Finally, the efficacy of the proposed control scheme is rigorously validated through simulation examples.

Predicting the 3-D ocean temperature field is a significant task that helps to understand global climate change and the state of ocean motion. Lots of numerical and data-driven models are used to predict ocean temperatures. However, these methods are restricted to the time-sequence prediction of discrete points or rely on convolutional layers to inefficiently capture local spatial dependencies for spatio-temporal prediction. In this letter, we propose a deep learning model named SA-PredRNN that combines attention mechanisms and predictive recurrent neural networks to capture global positional correlations and spatio-temporal features. Global gridded Argo temperature data with Barnes objective analysis (BOA-ARGO) are used to predict the future 3-D ocean temperature. The average RMSEs of the proposed model are promoted by at most 11% and 10%, which indicates that the SA-PredRNN model has better performance than the other baseline models.

This paper is concerned with the tracking control for a class of switched nonlinear systems subject to prescribed performance. Firstly, a barrier function and a normalized function are introduced to achieve prescribed performance control, which ensures that the tracking error evolves within prescribed boundary. Then, multi-dimensional Taylor network provides a new approach for how to estimate the nonlinearity arising from the backstepping control process. Significantly, the proposed multi-switching-based adaptive controller realizes that all signals in the closed-loop system are globally uniformly ultimately bounded while ensuring asymptotic tracking. Different from most existing network-approximation-based control strategies, the developed method in this paper is not only independent of the initial state, but also can achieve global stability. Finally, it is easy to verify the effectiveness of the proposed control method through two simulations.Note to Practitioners—This research is motivated by the fact that the control ideas of many practical engineering systems can be provided by making a profound study on switched nonlinear systems. However, most of the existing results focused on semi-global stability of systems. Therefore, this study devotes to develop a novel adaptive prescribed performance control strategy, which can not only ensure global stability of closed-loop system but also realize the asymptotic tracking of the switched nonlinear systems. It is worth noting that the proposed control approach has great significance for many practical systems, such as circuit systems and single-link inverted pendulum systems.

In this paper, the adaptive fault-tolerant control (FTC) problem is addressed for the stochastic nonlinear systems with multiple faults. The multiple faults, including the actuator and abrupt system faults, are first discussed in the same theoretical framework. The unknown nonlinearities are approximated by multi-dimensional Taylor networks (MTNs). By taking advantage of backstepping technique and finite-time control, the actual control law and virtual control signals are constructed, and then a novel adaptive FTC scheme based on the finite-time control method is proposed. The proposed controller guarantees that the closed-loop system is semi-global finite-time stable in probability (SGFSP) and the tracking error converges to a small neighborhood around the origin in the finite-time. Lastly, three examples are given to illustrate the effectiveness of the proposed scheme. Note to Practitioners—This research is motivated by the fact that actuators faults exist widely in real applications, which often degrade the control accuracy of the system and even result in the system instability. So far, the actuator and abrupt system faults of the stochastic nonlinear system have not yet been considered under the same theoretical framework. Therefore, this study designs a new MTN-based adaptive finite-time FTC scheme, which can ensure that the closed-loop system is SGFSP. The proposed control scheme has excellent practical value.

Accurate prediction of the traffic state has received sustained attention for its ability to provide the anticipatory traffic condition required for people's travel and traffic management. In this paper, we propose a novel short-term traffic flow prediction method based on wavelet transform (WT) and multi-dimensional Taylor network (MTN), which is named as W-MTN. Influenced by the short-term noise disturbance in traffic flow information, the WT is employed to improve prediction accuracy by decomposing the time series of traffic flow. The MTN model, which exploits polynomials to approximate the unknown nonlinear function, makes full use of periodicity and temporal feature without transcendental knowledge and mechanism of the system to be predicted. Our proposed W-MTN model is evaluated on the traffic flow information in a certain area of Shenzhen, China. The experimental results indicate that the proposed W-MTN model offers better prediction performance and temporal correlation, as compared with the corresponding models in the known literature. In addition, the proposed model shows good robustness and generalization ability, when considering data from the different days and locations.