The results demonstrate that residual encryption based on the Youla parameterization framework achieves robust kernel-attack detection while preserving operational integrity under adversarial conditions.

Second part of this work studies a projection-based fault detection methodology for LTI systems. This approach projects the input-output pairs to the image space of system, where the resulting projection residuals can be interpreted as errors caused by faults and uncertainties. By computing the upper bound of system uncertainties with the corresponding method, an adaptive threshold for fault detection can be established. Both theoretical derivations and experimental validations demonstrate that this approach exhibits superior fault detection capability compared to the observer-based fault detection methods.

Finally, this work investigates a performance-based fault diagnosis method for a nonlinear (affine) system. By solving the Hamilton-Jacobi Equation (HJE) and designing the feedback and feedforward controllers, the system is transformed into an inner system with lossless properties. Based on Control-Theory-Informed-Neural-Network (CTIML), the primary contribution of this part is the proposal of an online method for solving the HJE, while the lossless property will be continuously monitored online for faults and potential performance degradation detection. Compared to traditional Physics-Informed Neural Networks (PINNs), the proposed approach enables real-time acquisition of system states and online application.