In this study, an adaptive critics design based on a support vector machine (SVM) is adopted to design a finite-horizon optimal feedback controller. The adaptive critics design consists of actor and critic networks. The actor (control input) and critic (cost-to-go) network are trained off-line with respect to various initial states and final times within a finite step. Using the well-trained actor-critic, the near-optimal feedback control solution can be obtained online. In the process of applying SVM to the adaptive critics, an adequate kernel function and parameters depending on the kernel function must be selected. In this study, a polynomial function and radial basis function are used for the SVM kernel function to implement the algorithm. A minimum control effort problem with final constraints for spacecraft rendezvous is considered to demonstrate the performance of the proposed the developed algorithm with respect to each kernel function and to show its potential for designing an optimal controller.
|Journal||Journal of Aerospace Engineering|
|Publication status||Published - 2019 Jan 1|
Bibliographical noteFunding Information:
This work has been supported by the National GNSS Research Center program of Defense Acquisition Program Administration and Agency for Defense Development.
© 2018 American Society of Civil Engineers.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Materials Science(all)
- Aerospace Engineering
- Mechanical Engineering