NEURAL NETWORK-BASED ADAPTIVE PI BACKSTEPPING CONTROL FOR FRACTIONAL-ORDER CHAOTIC SYSTEMS WITH STATES CONSTRAINTS

In this paper, for a class of uncertain fractional-order chaotic systems with state constraints, an adaptive PI synchronization control algorithm based on neural networks is proposed, in which the neural network is used to approximate the unknown function. A PI-based regulator with proportional-integral is introduced to participate in the controller adjustment process and a special generalized error variable is proposed in this process. In order to avoid the state crossing the boundary, a logarithmic barrier function is proposed in time to manage the state of the system. Moreover, the proposed PI-based backstepping synchronous controller can ensure the stability of the closed-loop system and the error converges to a particularly small domain from the origin. Simulation experiments verify the practicability and effectiveness of this method.