## 1 Introduction  
Wheel-legged mobile robots have the advantages of good mobility, strong obstacle-crossing ability, and high energy efficiency compared with traditional wheeled or footed mobile robots [1]. The robot uses wheeled motion on relatively flat terrain, which increases the speed of movement. The robot adopts foot motion when passing through complex terrain such as guillies and pits, which improves its ability to cross obstacles. The CPG is a bionic motion control model that generates rhythmic motion signals such as walking and swimming by virtue of a small amount of oscillator coupling [2]. The CPG algorithm exhibits several advantages over traditional dynamics model-based control methods, including self-excited oscillations, the absence of global trajectory planning, and natural immunity to perturbations [3]. The Matsuoka and Kimura models are representative of the neuron model CPG, while the Kuramoto phase oscillator and Hopf harmonic oscillator are dominant in the oscillator model CPG [4, 5]. However, most of the existing CPG studies focus on symmetric gait generation in structured environments, where the parameters are often fixed or dependent on manual tuning [6]. Therefore, it is difficult for robots to cope with sudden changes in slope and ground stiffness in unstructured terrain, such as slopesand gullies. During unilateral terrain disturbances, the conventional symmetric coupling matrix is unable to maintain body equilibrium and is prone to sideways tilting or capsizing. Currently, CPG controllers are being widely used in the motion control of various types of bionic robots, commonly including quadruped robots [7; 8], bionic snakes/worms [9; 10], and bionic robotic fishes [11]. This phenomenon has been the subject of recent scholarly inquiry, leading to a range of perspectives on the mechanisms underlying animal adaptations. For example, [PERSON], [PERSON] et al., proposed an innovative approach termed the Adaptive Analog Central Pattern Generator (AI-CPG) [12]. This approach entails the modification of feedforward patterns based on sensory feedback, thereby ensuring the stabilization and balance of the body. Additionally, it demonstrates the capacity to adapt to alterations in the environment or the target's velocity [12]. [PERSON] et al., put forward a framework that integrates the CPG into a deep reinforcement learning framework, with the objective of producing robust omnidirectional quadrupedal locomotion [13]. [PERSON], [PERSON] et al. proposed a learning torque control framework that enables quadrupeds to traverse a variety of terrains and resist external disturbances while following user-specified commands [14]. [PERSON] et al. proposed a multilevel CPG model (ML-CPG) for adaptive control of quadrupedal robots [5]. [PERSON], [PERSON] et al. proposed a method using deep reinforcement learning (DRL) to enhance the gait output of central pattern generators (CPG) for hexapod robots, aiming to improve their terrain adaptability. They validated the model through experiments on rough terrains, slopes, and stairs, showing better performance than using DRL or CPG alone [15]. [PERSON], [PERSON] et al. have presented a data-driven DRL method to optimize a hierarchically structured control policy including the central pattern generator (termed HRL-CPG) [16]. They evaluated the method's applicability in real robot control, noting that stable gaits emerged with few trials, and deduced that it enables robots to adapt to diverse environments within a moderate amount of physical time [16]. In recent years, researchers have endeavored to incorporate stability mechanisms into the CPG framework. These efforts have included methods such as adjusting gait amplitude through force feedback or introducing a virtual impedance model to suppress body sway [17].