Fault tolerant synchronization of chaotic heavy symmetric gyroscope systems via Gaussian RBF Neural Network Based on Sliding Mode Control

In this paper, fault tolerant synchronization of chaotic gyroscope systems via Gaussian RBF neural network based on sliding mode control is investigated. Taking a general nature of fault in the slave system into account, a new synchronization scheme, namely, fault-tolerant synchronization, is proposed, by which the synchronization can be achieved no matter if the fault and disturbance occur or not. By making use of a slave-observer and Gaussian RBF Neural Network Based on Sliding Mode Control, the fault tolerant synchronization can be achieved. The adaptation law of designed controller is obtained based on sliding mode control methodology without calculating the Jacobian of the system. The proposed method can compensate the actuator faults and disturbances occurred in the slave system. Numerical simulation results demonstrate the validity and feasibility of the proposed method to fault tolerant synchronization.