Neuro-adaptive fixed-time non-singular fast terminal sliding mode control design for a class of under-actuated nonlinear systems

This paper presents a fixed-time neuro-adaptive control design for class of uncertain under-actuated nonlinear systems (UNS) using non-singular fast terminal sliding mode (TSMC) with radial basis function (RBF)-based estimator to achieve the convergence and robustness against uncertainties. The mathematical model considered is reduced into an equivalent regular form. A TSMC designed transformed form improve performance annihilate associated singularity problem conventional TSMC. Lyapunov stability theory ensures steering manifold system states in fixed time. RBF neural networks are adaptively estimate drift functions. theoretical design, analysis, simulations cart-pendulum quadcopter demonstrate feasibility benefits transformation design. Comparing proposed synthesis standard literature attractive nature method such class.