Adaptive Neural Control For The Perturbed Time Delay Systems

As we known,perturbed and time-delay widely exist in the real systems, andthey have a signifcant infuence on the system stability and system performance.When we design a controller to the system.Firstly, we always guarantee the systemhave stability and then consider some other problems of the system.In this paper,based on RBF neural controller for a class of perturbed nonlinear time-varyingdelay systems.The major contents are as follow:1.In this thesis,we discussed the basic knowledge of feedback control andneural network.Focus on RBF neural network.An example is given to inllustratethe theorems.2.In this thesis We present adaptive neural control design for a class of per-turbed nonlinear MIMO time-varying delay systems in block-triangular form.Basedon a neural controller is obtained by constructing a quadratic-type Lyapunov-Krasovskii functional,which efciently avoids the controller singularity.The pro-posed control guarantees that all closed-loop signals remain bounded,while theoutput tracking error dynamics converges to a neighborhood of the desired trajec-tories.The simulation results demonstrate the efectiveness of the proposed controlscheme.3.In this paper,an adaptive neural control for a class of time-delay nonlinearfrst-order systems with perturbed is proposed,based on backstepping、adaptivecontrol and neural networks.The radius basis function (RBF) neural networks isemployed to estimate the unknown continuous functions.Finally, the correspond-ing system of a theorem,and further proof is given.4.In this paper,an adaptive neural control for a class of time-delay nonlinearnth-order systems with perturbed is proposed,based on backstepping、adaptivecontrol and neural networks.The radius basis function (RBF) neural networks isemployed to estimate the unknown continuous functions. Simulation results areprovided to illustrate the performance of the proposed approach.