The work studies the tracking control issue for a class of nonlinear strict-feedback cyber-physical systems (CPSs) subject to unknown deception attacks. We propose a coordinate transformation technique with a reference signal, which incorporates the attack gains, and exploit the compromised states to construct robust controllers. The Nussbaum functions are also introduced to resist the inherent uncertainty of the time-varying attack injection signal. Meanwhile, the neural networks are used to handle the nonlinearities of CPSs. In addition, to reduce the network bandwidth of the CPSs, we adopt an event-triggered mechanism that updates the control input only when necessary. Finally, the presented control scheme guarantees that all signals are bounded in the control system and the tracking control of the CPSs can be achieved. Simulation results demonstrate that the developed algorithm is effective.
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