This study aims to enhance the control performance of second-order uncertain systems， especially those characterized by unknown models and accompanied by dynamic disturbances. A novel control strategy was proposed using the ball-and-plate system as the experimental object. An RBF neural network （RBF1） was used to predict the key parameters of the system， and its internal parameters were dynamically adjusted through adaptive algorithms to ensure prediction accuracy. Based on this predictive model， a sliding mode controller with an integral sliding surface was designed， enabling the system states to reach the sliding manifold directly and thereby improving the robustness and response speed. To further optimize control performance， this paper innovatively introduced a second RBF neural network （RBF2） to dynamically adjust the sliding mode controller parameters， which were tuned via the gradient descent method， enhancing the flexibility and adaptability of the control strategy. Simulation experiments show that this control strategy performs well in trajectory tracking of ball-and-plate systems， effectively dealing with system uncertainties and disturbances， which confirms its favorable control performance and practical application prospects.