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The adaptive tracking control strategy is investigated for a class of multi-input and multi-output pure-feedback nonlinear delayed systems with unknown dead-zone inputs. This problem is challenging due to the existence of unknown dead zones, time-varying delays and unavoidable state variables. By constructing fuzzy approximators and state observers, the difficulties from unknown nonlinearities and unavailable state variables are surmounted, respectively. Lyapunov-Krasovskii functions are introduced to deal with the time-varying delays. The adaptive controllers are designed by a backstepping method and adaptive technique so that the closed-loop systems remain stable and the target signals can be tracked within a small error as well. At last, two examples are provided to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper exploits the outer synchronization between two dynamic networks with diversely varying node behaviours at the presence of noise. Aiming to obtain a generic technical solution for network outer synchronization, the problem is addressed for the dynamic network scenarios with non-identical topological structures, nonlinear inner coupling connections and time-delayed node characteristics. The impact of noise is fully considered in the theoretical and performance analysis which makes the derived synchronization criteria be able to be adopted in practical engineering deployment with minimized hurdles. By using a Lyapunov-Krasovskii energy function, the synchronization criteria and adaptive control solution are presented and the convergence of the system error states is also proved. Finally, the numerical analysis is carried out based on the simulation study and the result verifies the correctness and effectiveness of the suggested control solution. [ABSTRACT FROM AUTHOR]

This paper investigates observer-based adaptive fuzzy control for a class of delayed nonlinear systems in pure-feedback form. We first proposed a linear observer to get the estimation of the system's state. And then utilise fuzzy logic systems to model those nonlinearities. Further, adaptive fuzzy approach and backstepping technique are combined to develop the desired controller via Lyapunov analysis. The suggested adaptive fuzzy control scheme ensures that all the signals of the resulting nonlinear system converge to a small neighbourhood of the origin. Then a real simulation is used to illustrated the effectiveness of our results. [ABSTRACT FROM AUTHOR]