Adaptive Optimal Control of Continuous-Time Linear Systems via Hybrid Iteration
Omar Qasem, Weinan Gao, Tao Bian · 2021 IEEE Symposium Series on Computational Intelligence (SSCI) · 2021
In this paper, we propose a novel dynamic programming (DP) algorithm, under the name of hybrid iteration (HI), for continuous-time linear systems. The proposed HI approach combines the advantages of two well-known DP algorithms, i.e., policy iteration (PI) and value iteration (VI). In particular, HI drops the need of an initial stabilizing control policy required in PI, and at the same time it maintains a faster convergence rate compared with VI. Based on the proposed HI algorithm, a data-driven adaptive optimal controller design is also proposed. Simulation results for randomly generated continuous-time linear systems with different system orders demonstrate that the proposed HI approach can save CPU time up to 73% and reduce the number of iterations to converge up to 98% comparing with the VI approach.