Abstract
In this paper we present a recurrent neural network for global asymptotic tracking control of discrete-time time-varying nonlinear affine systems with disturbances. The objective is to control the system so that its output can track, from any initial point an exogenous reference output generated by a known time-varying dynamics. First, we extend the dissipative inequality to a composite system combining the original system and the exogenous reference system. This composite system is not required to have an equilibrium point. Then, by choosing an appropriate time-varying quadratic storage function, the extended dissipative inequality leads to a group of linear matrix inequalities. This group of linear matrix inequalities is mapped to several convex optimization problems. To solve these convex optimization problems, a gradient flow system is developed. In addition, an augmented gradient flow system is carefully proposed to avoid the complicated computation of matrix inverses. A recurrent neural network is designed to realize this augmented gradient flow. At each time step, the recurrent neural network generates a desired control input based on the present state and the system model. The effectiveness and characteristics of the proposed neural controller are demonstrated by simulation results.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.