Adaptive Neural Control Design for a Class of Perturbed Nonlinear Time-varying Delay Systems

In this thesis, Some adaptive neural control design ways are presented for a class of multi-input multi-output (MIMO) nonlinear systems in block-triangular form with disturbance input and state time-varying delay. Neural networks are employed to approximate the unknown continuous functions. By combining the use of a novel quadratictype Lyapunov-Krasovskii functionals and adaptive NN backstepping, an adaptive neural controller is obtained, which efficiently avoids the controller singularity. The proposed control guarantees that all closed-loop signals remain bounded, while the output tracking error dynamics converges to a neighborhood of the desired trajectories. The feasibility is investigated by a simulation example.