Model-based control is crucial for efficient robotic operations, yet accurately identifying robot dynamics remains challenging, particularly for parallel kinematic manipulators (PKMs) . This work leverages physics-informed neural networks (PINNs), specifically the Deep Lagrangian Network in combination with non-symmetric Coulomb friction, to achieve physically consistent dynamics models by incorporating principles such as energy conservation and friction modeling. Validated on the ABB IRB 360-6/1600 Delta robot, the approach demonstrates high fidelity in torque prediction and effective real-time control implementation on industrial hardware under stringent computational constraints. Experimental results highlight improved torque prediction accuracy, reduced trajectory tracking lag, and robust handling of complex dynamic interactions, paving the way for adaptive and efficient industrial automation.