Lyapunov-based continuous-time nonlinear control using deep neural network applied to underactuated systems

Several learning-based control with computational intelligence strategies handle challenges related to the difficulty of modeling complex systems or the need for control strategies with provably safe. In recent years, learning-based control using machine learning has been successfully demonstrated in robotics applications and applied to deal with nonlinearities. These control methods may lead to better solutions to nonlinear problems, such as the safety-critical industry, which requires strong guarantees about the controller behavior. Learningbased neural network control can comprehend and learn about plants, disturbances, the environment, and operating conditions. In this paper, we presented a Lyapunov-based nonlinear control determined from a deep neural network, which uses the Lyapunov theory to compute a control law for a nonlinear system. For advance stability analysis, an estimation of the region of attraction is presented. A numerical example and experimental simulations using the rotational inverted pendulum system are performed and compared with a conventional control technique. The proposed method calculated a control law that provided the stabilizability of the system and produced better solutions considering different tracking and process disturbance.

Automated summary

Learning-based neural network control methods can be applied to handle complex nonlinear systems, providing better solutions and stability guarantees.