This paper studies the robustness of message masking communication over noisy channels using modified chaotic systems. First, the modified chaotic systems are introduced with a higher capability of transmitting messages than typical chaotic systems. Then, assuming an ideal channel, the chaotic message masking scheme is derived which achieves asymptotic convergence or dead-beat performance for recovering messages. Next, considering the case of noisy channels, an H∞ performance and an L2-gain optimal noise rejection are achieved by solving linear matrix inequality (LMI) problems. Furthermore, the ultimate bound of synchronization error and recovered message error can be adjusted by both design gains and the system parameter of the modified chaos. Using the proposed method, the bit-error-ratio and noise tolerance are improved. Finally, numerical simulations and DSP experiments are carried out to verify the theoretical derivations.

This paper proposes a robust fuzzy integral controller for output regulating a class of affine nonlinear systems subject to a bias reference to the origin. First, a common biased fuzzy model is introduced for a class of continuous/discrete-time affine nonlinear systems, such as dc-dc converters, robotic systems. Then, combining an integrator and parallel distributed compensators, the fuzzy integral regulator achieves an asymptotic regulation. Moreover, when considering disturbances or unstructured certainties, a virtual reference model is presented and provides a robust gain design via LMI techniques. In this case, H∞ performances is guaranteed. Note that the information regarding the operational point and bias terms are not required during the controller implementation. Thus, the controller can be applied to a multi-task regulation. Finally, three numerical simulations show the expected results.