System identification of a closed loop mechanical system using physics inspired neural network

This study focuses on how PINNs join forces with MPC to improve system identification mechanisms for closed-loop mechanical systems that include robotic arms drones and automobile control. The identification process using traditional approaches faces difficulties because of nonlinear system components together with complex parameters. Due to their inability to embed physical constraints PINNs represent a solution which enhances both prediction accuracy and robustness as well as adaptive functionality. The sensitivity of PINNs to noisy data along with their need for substantial training data can be counterbalanced by using prior system information to perform effectively in real-time control applications. Researchers have integrated PINNs with MPC toolkit to find solutions for dynamic nonlinear partial differential equations. Engineers use this integration to handle computational expenses and scalability issues while ensuring real-time control of robots and aerospace devices and energy system applications which enhance control precision and computational timing capabilities through minimal data reliance. The application of PINNs faces difficulties because of elevated processing requirements together with network factor sensitivities that obstruct immediate usage. Using PINNs in system identification results in better robustness together with decreased data needs while providing enhanced model generalization capabilities. PINNs exist as an evolving technology despite their computational challenges and parameter refinement complexity because they demonstrate promising development opportunities for modeling and control of large-scale high-accuracy systems.