All real-world applications of feedback control involve control actuators with amplitude limitation. In practice any electromechanical device can produce a limited capacity of force, torque, hit, flow or linear angular velocity. Saturation in control systems, is the conditions that at least one of the physical elements reaches the maximum of its performance in the control loop at certain points of time. Since the formation ofmodern control engineering, the control problem of systems with saturation is a significant and challenging research topic as actuators are always subject to limits. Also in control engineering, many practical systems have dynamic non-linear behavior because they have complex coupling state variables. Investigation about Controller design for nonlinear systems has been developed from both in theoretical and practical points of view. A method of designing desired controlofthese systems is that the controller can be able to adapt itself with uncertain systems. Therefore, adaptive control is one of the very important methods which is used to control uncertain nonlinear systems. In adaptive neural control, neural networks have been used as on lineapproximators to approximate the unknown nonlinear functions using the idea ofacksteppingtechnique, without the needformatching conditions adaptive neural controllers is provided for a stroked="f" filled="f" path="m@4@5l@4@11@9@11@9@5xe" o:preferrelative="t" o:spt="75" coordsize="21600,21600" of the system increases. This problem can be solved by using dynamic surface control (DSC). The main objective of this research is to developed robust adaptive neural control nonlinear systems in the presence of actuator saturation by usingthe DSC method. The studied systems, which are strict feedback form in the presence of actuator saturation and external disturbances and without considering these factors. RBF neural networks are used to approximate the uncertain functions. Control law and adaptation laws are defined, and by introducing a suitable Lyapunov function, system stability is investigated. Keywords: Nonlinear systems in strict feedback form, Adaptive neural control, RBF neural network, Dynamic surface control, Actuator saturation.