Command Filtered Adaptive Fixed‐Time Tracking Control for State‐Constrained Switched Systems: An Feasibility Conditions‐Removing Method

The article focuses on the adaptive neural fixed‐time tracking control for nonlinear switched systems with asymmetric, time‐varying full‐state constraints. To address the uncertain nonlinear dynamics, radial basis function neural networks (RBF NNs) are adopted for the uncertain function approximation. A fixed‐time command filter is utilized, thereby avoiding the computational complexity associated with the iterative differentiation of the virtual controller. Specially, the state constraints problem is coped by utilizing the nonlinear transformation function directly, which removes the need for feasibility conditions on virtual controllers. An adaptive fixed‐time tracking control approach is proposed by incorporating the command filtering control approach within the backstepping control framework. The proposed control algorithm assures the fixed‐time bounded stability of the considered system, while maintaining full‐states constraint satisfaction. Lastly, the rationality of the proposed control method is illustrated via the simulation section.