Mixed Driven Iterative Adaptive Critic Control Design Towards Nonaffine Discrete-Time Plants⁎This work was supported in part by Beijing Natural Science Foundation under Grant JQ19013, in part by the National Natural Science Foundation of China under Grant 61773373 and Grant 61890930-5, and in part by the National Key Research and Development Project under Grant 2018YFC1900800-5. Issue 2 (2020) - Wang, Ding
Abstract: In this paper, an effective mixed driven framework is constructed involving both data and event considerations. The primary purpose lies in that the mixed driven iterative adaptive critic method is established to address approximate optimal control towards discrete-time nonlinear dynamics. The neural dynamic programming technique is inventively integrated with the mixed driven architecture, such that the knowledge of the controlled plant is needless and the number for updating control inputs is prominently reduced. A triggering threshold is also designed with theoretical guarantee, which renders that the control signals can be updated conditionally. Through carrying out simulation studies with comparisons, the superiority of the present near-optimal regulation approach is confirmed at last.