On chaos control of nonlinear fractional chaotic systems via a neural collocation optimization scheme and some applications

In this paper, we study chaos control of a class of fractional-order chaotic systems where the dynamic control system depends on the Caputo fractional derivatives. We first propose an infinite horizon optimal control problem related to the given fractional chaotic system. With the help of an approximation, we replace the Caputo derivative to integer order derivative. We then convert the obtained infinite horizon optimal control problem into an equivalent finite horizon one. Based on the Pontryagin minimum principle (PMP) for optimal control problems and by constructing an error function, we define an unconstrained minimization problem. In the optimization problem, we use trial solutions for state, costate and control functions where these trial solutions are constructed by using a two-layered perceptron neural network. A learning procedure of the proposed neural network with convergence properties are also given. Some numerical results are introduced to explain our main results. Three applicable examples on chaos control of Malkus waterwheel, finance fractional chaotic models and fractional-order Geomagnetic Field models are finally considered.