Indirect Adaptive Neural Network Control For A Class Of Non - Strictly Feedback Nonlinear Time - Delay Systems

It is well known that the nonlinear phenomenon and time delays are widely spread in real engineering systems. Therefore, stability analysis and control design for nonlinear time-delay systems are important both in theory and in practice. The time delays often lead to degradation of system stability.Based on the above discussion, this paper proposes a control scheme of indirect adaptive control for a class of nonlinear non-strict feedback systems. A variable separation method is developed in the system. Then the new unknown nonlinear functions are approximated by the neural network, the backstepping techniques and adaptive method are utilized to analysis of system. The controller which has only one parameter is presented. The Lyapunov stability theorem is used to prove that the effectiveness of the proposed scheme. Based on the section, we discuss the problem of adaptive neural network control for a class of nonlinear non-strict feedback systems with time delays. Using Lyapunov function eliminates the time delays. Neural network, adaptive control technique and backstepping method are utilized to construct state feedback controller which has only one parameter. Simulation results illustrate the effectiveness of the control scheme.