## 1 Introduction  
Due to the open nature of the channel, wireless communication is susceptible to both inadvertent and intentional jamming, significantly reducing the reliability and effectiveness of information transmission [1]. To ensure reliable transmission in harsh electromagnetic environments, it is necessary to research anti-jamming technologies. Currently, common anti-jamming techniques in communication include spread spectrum [2], power control [3], and rate adaptation. However, traditional anti-jamming methods often rely on fixed strategies or rules. While these methods can counteract certain types and levels of malicious jamming, predefined patterns and parameters struggle to handle carefully designed malicious attacks, how to achieve efficient communication under unknown and dynamic malicious jamming has become a current research focus [4; 5]. Reinforcement learning is a machine learning technique inspired by the living organisms' natural tendency to avoid harm. In reinforcement learning, the agent interacts with the external environment by performing various actions and continuously learns from the feedback provided by the environment, thereby discovering strategies that are more advantageous to itself [6]. Recently, reinforcement learning-based anti-jamming technologies for communication systems have garnered widespread attention. For example, [PERSON] [7] models the anti-jamming problem under temporal random pulse jamming as a Markov Decision Process (MDP) and proposes a Q-learning-based temporal anti-jamming algorithm, which enables the transmitter to flexibly switch between active and silent states to evade random pulse jamming. [PERSON] [8] proposes an alternating reinforcement learning anti-jamming algorithm for channel selection and power control under tracking jammingthreats, which achieves optimal channel selection and suboptimal power control. However, real-world communication anti-jamming problems often involve large state-action spaces, leading to the \"curse of dimensionality\". Classic reinforcement learning methods, which explore state-action spaces through single-step iteration, struggle to converge [9] and face challenges in solving real-time online anti-jamming decision problems in complex jamming environments. Deep reinforcement learning (DRL) [10], with its powerful fitting capabilities through deep neural networks, alleviates the curse of dimensionality problem faced by traditional reinforcement learning, enabling effective anti-jamming in complex jamming environments. For instance, Reference [11] proposes a cross-domain anti-jamming algorithm based on Deep Q-Network (DQN), which enables mobile nodes to learn optimal strategies for position adjustment and power control in unknown dynamic jamming environments, thereby achieving reliable transmission. Reference [12] presents a deep reinforcement learning anti-jamming scheme based on the Actor-Critic (AC) framework for mobile edge networks, capable of simultaneously selecting offloading nodes, transmission power, and data rates to achieve anti-jamming data offloading. However, DRL algorithms represented by DQN have a slower adaptation speed to jamming environments, requiring longer training times, and struggle to maintain communication reliability under unknown and rapidly changing jamming. To overcome the aforementioned drawbacks, some researchers have explored communication anti-jamming methods based on control theory [13]. From the perspective of control theory, the normal transmission of wireless communication systems under unknown jamming is viewed as a control process affected by disturbance, with the jamming modeled as time-varying uncertainty disturbances in the control system. This approach helps to address or circumvent the limitations of existing machine learning-based anti-jamming algorithms. Reference [14] proposes a robust power control scheme for cognitive radio networks based on Lyapunov stability theory and switched affine systems.