In this paper, a new nonlinear dynamic system, new three-dimensional fractional order complex chaotic system, is presented. This new system can display hidden chaotic attractors or self-excited chaotic attractors. The Dynamic behaviors of this system have been considered analytically and numerically. Different means including the equilibria, chaotic attractor phase portraits, the Lyapunov exponent, and the bifurcation diagrams are investigated to show the chaos behavior in this new system. Also, a synchronization technique between two identical new systems has been developed in master- slave configuration. The two identical systems are synchronized quickly. Furthermore, the master-slave synchronization is applied in secure communication scheme based on chaotic masking technique. In the application, it is noted that the message is encrypted and transmitted with high security in the transmitter side, in the other hand the original message has been discovered with high accuracy in the receiver side. The corresponding numerical simulation results proved the efficacy and practicability of the developed synchronization technique and its application
In this article, a PD-type iterative learning control algorithm (ILC) is proposed to a nonlinear time-varying system for cases of measurement disturbances and the initial state errors. The proposed control approach uses a simple structure and has an easy implementation. The iterative learning controller was utilized to control a constant current source inverter (CSI) with pulse width modulation (PWM); subsequently the output current trajectory converged the sinusoidal reference signal and provided constant switching frequency. The learning controller's parameters were tuned using particle swarm optimization approach to get best optimal control for the system output. The tracking error limit is achieved using the convergence exploration. The proposed learning control scheme was robust against the error in initial conditions and disturbances which outcome from the system modeling inaccuracies and uncertainties. It could correct the distortion of the inverter output current waveform with less computation and less complexity. The proposed algorithm was proved mathematically and through computer simulation. The proposed optimal learning method demonstrated good performances.
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