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 new error conversion technology, any nonzero and bounded initial state is converted to zero, which not only solves the overshoot/oscillation problem of the output during the constraint switching phase but also unifies the control design of constrained and unconstrained systems. Second, a barrier function with asymmetric output constraints is designed, which converts the problem of satisfying the tracking control of nonlinear systems under output constraints into one of ensuring the boundedness. In addition, radial basis function neural networks (RBFNNs) are utilized to handle both unknown uncertain terms and deception attacks simultaneously. By utilizing the new asymmetric delayed barrier function error together with an RBFNN technique, the tracking error is ultimately uniformly bounded, regardless of the presence or absence of output constraints. Finally, the superiority of the proposed strategy is verified through its simulation on an unmanned aerial vehicle (UAV) system.