Privacy Preserving for Switched Systems Under Robust Data-Driven Predictive Control

Differential privacy preserving ensures the privacy of the system data by adding certain regular noises to the data to cover up the real information. The main challenges of strong nonlinearities, uncertainty, and data privacy are considered together for switched systems, and a novel privacy-preserving robust model-free adaptive predictive control (PPR-MFAPC) method is proposed that guarantees both H-infinity performance and system privacy. At first, a performance-dependent differential privacy noise conforming the Laplace distribution is designed, which can adaptively adjust the noise size to balance the system performance and privacy. Then, a novel privacy level analysis with evaluation method is presented. Subsequently, the strong uncertainties of switched systems is solved through a dynamic linearization method. On this basis, a novel cost function is designed by considering both the H-infinity performance and system privacy to balance the system performance and privacy from the perspective of control design. Further, by incorporating a parameter estimator and a prediction algorithm, the private MFAPC anti-noise controller is obtained. Finally, the feasibility of the PPR-MFAPC is explained with illustrative example.