Adaptive Neural Optimized Control for a Class of Switched Strict‐Feedback Systems With Unknown Control Gains

ABSTRACT This paper develops an adaptive neural optimized control strategy for a class of switched strict‐feedback systems with unknown control gains and prescribed performances utilizing an adaptive projection method. To design the desired adaptive neural controller, we firstly utilize the actor‐critic architecture to formulate both optimized virtual and actual controllers within the backstepping framework. Secondly, this paper introduces an adaptive projection to eliminate the assumption that the unknown control coefficient cannot pass through zero. Moreover, control issues related to predefined time constraints and precision requirements can be addressed through error transformation. The proposed control strategy ensures that the tracking error converges to the predefined range within the allocated time. Additionally, all the signals of the controlled system remain bound. Finally, a set of comparative simulation experiments is discussed for three different predefined times, and the experimental results confirm the feasibility of the proposed control strategy.