Soft robotics has emerged as a promising field, offering unique advantages such as flexibility, adaptability, and safety compared to traditional rigid robots. The robot’s flexibility and its constructional-specific features cause difficulties in programming and tracking its movements. High precision is what engineering expects from a robot, especially when it comes to fine manipulation operations. Existing models, such as the Piecewise Constant Curvature (PCC) model, are widely used but often fail to capture the complex behaviour of soft robots without modifications, leading to discrepancies between theoretical predictions and experimental observations. The proposed study addresses these limitations by introducing a modified PCC model that incorporates correlation coefficients to improve its accuracy. These coefficients adjust the model’s scale and linear biases, effectively compensating for nonlinearities and segment interactions. The proposed modifications were validated through experiments on a two-segment soft robotic structure with six pneumatic muscles. The results demonstrate that the modified model closely matches experimental data, significantly reducing the errors observed in the unmodified PCC model.