Adaptive Compensation Control of Dynamic Load Characteristics of Soft Soil Foundation Based on Deep Reinforcement Learning

Aiming at the complex nonlinear characteristics of soft soil foundation under dynamic load, this paper proposes an adaptive compensation control method based on deep reinforcement learning, aiming to improve the response accuracy and stability of the foundation in a dynamic environment. By introducing the deep Q network (DQN), this method realizes the modeling and control optimization of the dynamic behavior of the foundation, and designs a dynamic load characteristic compensation algorithm to cope with complex load disturbance scenarios. The experimental results show that the proposed control method has significant advantages over the traditional PID control in terms of response error, convergence speed and compensation control quantity, especially in the case of fixed load disturbance and random dynamic load disturbance. It shows strong robustness and adaptability. The effectiveness of this method in complex dynamic control scenarios is verified by simulation experiments and performance evaluation, which provides technical support and theoretical basis for the intelligent control of soft soil foundation dynamic problems, and has important engineering application value.