Computational modeling demonstrates enhanced tracking accuracy in robotic systems with input deadzone, highlighting the efficacy of physics-informed neural network control.
This paper investigates a novel controller for stochastic nonlinear system with input deadzone by leveraging Physics Informed Neural Network (PINN). The unknown system nonlinearities are approximated by using PINN through backstepping. A quartic Lyapnov function is utilised to construct virtual and actual controller, ensuring boundedness in probability of all closed loop signals. Deadzone nonlinearity is reformed into a linear input form to complete the backstepping process. Simulation result on a robotic manipulator system demonstrate excellent tracking performance and improved accuracy compared to existing method, confirming the effectiveness of proposed PINN based control strategy.
No takes yet. Share an insight, caveat, or question.
Singh et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: