Abstract
In practical applications, there are some requirements for time-varying constraints on full system states. Currently, Barrier Lyapunov Function is a popular tool for constraint control. However, how to handle various uncertainties while satisfying constraint requirements is worth further research. Accordingly, a high-performance multilayer neuroadaptive constraint-handling controller for a class of nonlinear systems with uncertainty compensation will be developed. Significantly, asymmetric time-varying constraints for full system states can be implemented. Furthermore, a set of novel multilayer neuroadaptive disturbance observers will be proposed to address modeling uncertainties. Moreover, by introducing a set of nonlinear command filters, the intelligent control algorithm will be designed via the command filtered backstepping method. The stability of the whole closed-loop system is strictly proved. Additionally, the proposed algorithm is applied to different nonlinear systems including a hydraulic robotic manipulator system to verify its feasibility.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call