Current quadrupedal robot control methods face critical challenges: model-based approaches require extensive manual parameter tuning, while reinforcement learning (RL) methods demand prohibitive training time. To promote quadrupedal robot development, simplify motion controller design, and facilitate robot deployment, this study proposes a novel bio-inspired control scheme. Specifically, inspired by the differentiated modalities of the animal's proximal and distal joints, a multi-model fusion scheme is constructed. First, the hip movement in joint space is obtained by a central pattern generator(CPG), whereby motion gaits, including trotting and galloping, are generated by a coupling network. Then, to generate the knee motion, a CPG-driven finite state machine is first proposed to determine the gait state. On top of this, the spring-loaded inverted pendulum model is utilized to regulate the knee joint's torque command. To enhance forward stability and speed tracking accuracy, this study incorporates online feedback regulation that adjusts both the CPG frequency and joint oscillation amplitude based on attitude angle and forward velocity information. And, a virtual model control strategy is designed to modify the torque profile of the knee torque. To verify the proposed methodology, hardware experiments are conducted on a newly developed quadrupedal robot. Results demonstrate that (i) the small-sized robot can reach 0.8 m/s (2.0 BL/s), with minimal tracking errors and relatively stable robot postures; (ii) compared with the traditional case where CPG generates both hip and knee trajectory directly, the energy consumption is reduced by 11.2% with our method; (iii) the robot can realize smooth trot-gallop-trot gait transition on flat ground and robust walking across uneven terrains.