Learning‐based robust neuro‐control: A method to compute control Lyapunov functions

Abstract

AbstractNonlinear dynamical systems play a crucial role in control systems because, in practice, all the plants are nonlinear, and they are also a hopeful description of complex robot movements. To perform a control and stability analysis of a nonlinear system, usually, a Lyapunov function is used. In this article, we proposed a method to compute a control Lyapunov function (CLF) for nonlinear dynamics based on a learning robust neuro‐control strategy. The procedure uses a deep neural network architecture to generate control functions supported by the Lyapunov stability theory. An estimation of the region of attraction is produced for advanced stability analysis. We implemented two numerical examples to compare the performance of the proposed technique with some existing methods. The proposed method computes a CLF that provides the stabilizability of the systems and produced better solutions to nonlinear systems in the design of stable controls without linear approximations and in the presence of disturbances.