Abstract

In this paper, an optimal self-learning control scheme for discrete-time nonlinear systems is developed using a new local value iteration based adaptive dynamic programming (ADP) algorithm. The developed local value iteration algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. In the developed local value iteration algorithm, the iterative value function and iterative control law are updated by a subset of the state space. A new analysis method of the convergence property is presented to show that the iterative value functions will converge to the optimum. The convergence criterion for the local value iteration algorithm is presented. A simulation example is given to demonstrate the validity of the present optimal control scheme.

Full Text
Paper version not known

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

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.