RBFNN-Based Secure Tracking Control for a Class of Strict-Feedback Nonlinear Systems with Asymmetric Output Constraints and Its Application to UAVs

This paper investigates a tracking control problem for a class of strict-feedback nonlinear systems with time delays, asymmetric output constraints, and deception attacks on the controller. First, by introducing a novel error transformation techniques, any non-zero and bounded initial state is converted into zero. Second, a barrier function with the asymmetric output constraints is designed, which convert the problem of satisfying the tracking control problem of nonlinear systems under output constraints boils down to ensuring the boundedness. In additional, the radial basis function neural networks (RBFNNs) are utilized to handle both unknown uncertain term and deception attacks simultaneously. By utilizing the new asymmetric delayed barrier function error together with a RBFNNs technique, the tracking controller is designed to achieve asymptotic tracking, regardless of presence or absence of output constraints. Finally, the effectiveness of the proposed strategy is verified through its simulation on the unmanned aerial vehicle’s (UAVs) systems.