Event-triggered model-free adaptive predictive control for multiple-input and multiple-output networked control systems under deception attacks

The event-triggered model-free adaptive predictive control (ET-MFAPC) problem for a class of multiple-input and multiple-output nonlinear networked control systems (NCSs) with network delay under deception attacks is addressed in this article. First, a data driven MFAPC strategy with adaptive predictive gain is proposed by transforming the NCSs into an equivalent data model via dynamic linearization technology and designing controller only with I/O information rather than the system model. Meanwhile, an ET mechanism is applied to reduce the amount of data transmission under the limited network bandwidth. Further, the boundedness and convergence of tracking error is rigorous proved through the contraction mapping principle. Finally, the theoretical results are demonstrated through detailed simulations.