In this paper, an attack-tolerant fuzzy path following control method is proposed for distributed drive electric vehicles subject to aperiodic denial-of-service (DoS) attacks based on an event-triggered output feedback framework. Firstly, to construct a framework for feasible controller design under DoS attacks, a switched interval type-2 fuzzy output feedback control framework is established with consideration of vehicle dynamics nonlinearity coupled with DoS attacks. Secondly, to guarantee the stability and desired path following performance of the vehicle closed-loop control system under DoS attacks, an attack-tolerant sufficient condition is derived by constructing piecewise Lyapunov functional. Thirdly, to balance control performance and network bandwidth utilization under DoS attacks, a resilient event-triggered fuzzy output feedback control method is proposed in terms of a set of linear matrix inequalities. Finally, experimental results validate the effectiveness and superiority of the proposed method in the aspect of path following accuracy and network resource conservation following accuracy compared with existing methods.

Large language models (LLMs) have shown great potential in decision-making due to the vast amount of knowledge stored within the models.However, these pre-trained models are prone to lack reasoning abilities and are difficult to adapt to new environments, further hindering their application to complex real-world tasks. To address these challenges, inspired by the human cognitive process, we propose Causal-Aware LLMs, which integrate the structural causal model (SCM) into the decision-making process to model, update, and utilize structured knowledge of the environment in a "learning-adapting-acting" paradigm.Specifically, in the learning stage, we first utilize an LLM to extract the environment-specific causal entities and their causal relations to initialize a structured causal model of the environment. Subsequently, in the adapting stage, we update the structured causal model through external feedback about the environment, via an idea of causal intervention. Finally, in the acting stage, Causal-Aware LLMs exploit structured causal knowledge for more efficient policy-making through the reinforcement learning agent. The above processes are performed iteratively to learn causal knowledge, ultimately enabling the causal-aware LLM to achieve a more accurate understanding of the environment and make more efficient decisions. Experimental results across 22 diverse tasks within the open-world game "Crafter" validate the effectiveness of our proposed method.

With the development of deep neural networks, deep reinforcement learning (DRL) has shown great potential in solving complex decision-making problems in high-dimensional environments. Among various DRL methods, Q-value function approaches are well-grounded in theory and exhibit strong practical performance, but often suffer from overestimation of the value function. We find that this overestimation primarily stems from unobserved random factors (latent variables) within the environmental dynamics, while existing solutions fail to address this issue, relying mainly on low-noise and unbiased sampling environments. To address this problem, we introduce latent variables to model unobserved randomness behind the environmental dynamics and employ causal models to capture the underlying data generation mechanisms. Subsequently, we propose a causal Q-value function method based on latent causal models. Specifically, we first design a causal encoding-decoding framework for learning the latent causal models, then utilize the inferred latent variables to construct a causal Q-value function, effectively mitigating the Q-value function overestimation problem. Experimental results show that our method significantly improves the stability of policy learning and the speed of convergence, enhancing the robustness of reinforcement learning in complex dynamic environments.

Conventional unsupervised multi source domain adaptation (UMDA) methods are based on the assumption that all source domain data are accessible directly. Aiming at the problem that current UMDA methods cannot directly obtain source domain data in federated learning (FL), a knowledge distillation-based multisource domain adaptation method adapted to FL is proposed. First of all, considering that knowledge distillation allows learning solely through model access, this article adopts an improved voting mechanism by applying a smoothing technique to the confidence distribution in the source domain models. This reduces the influence of models with extreme high confidence, thereby extracting high-quality consensus knowledge. Second, this article designs a teacher model adaptive weighting strategy. It identifies irrelevant domains and malicious domains according to the similarity between consensus knowledge and the output of the teacher model. Then, it improves the robustness of the model for negative transfer. Finally, this article introduces the idea of contrastive learning. It can control the drift of a single source domain and bridge the deviation between the representation learned by the local model and the global model. Experiments show that the method proposed in this article is superior to the mainstream UMDA methods. Moreover, it is robust to negative transfer, which is suitable for many practical FL applications.