A Novel Model-Free Output-Feedback H_∞ Parameterization Control Method With Unknown States Under Ill-Condition

Developing model-free $H_{\infty }$ optimal control schemes in systems with unknown model parameters and unmeasurable states is challenging. In this article, an output-feedback (OPFB) suboptimal control scheme based on adaptive dynamic programming (ADP) is proposed to realize model-free $H_{\infty }$ control under uncertain disturbances. First, a free matrix is introduced to compute the suboptimal gain in the absence of an optimal OPFB gain, and a policy iterative algorithm is developed to solve for the suboptimal gain and shown to converge to a solution of the algebraic Riccati equation. In addition, a model-free ADP algorithm is proposed to realize online learning of control parameters without relying on system dynamics parameters. The Lanczos method is introduced to solve the ill-condition problem in the model-free algorithm solution. After that, the algorithm is further extended to the case where the system state is not measurable and parameterized reconstruction is performed using online input-output data. The results show that the proposed algorithm can realize model-free control with unknown parameters and unmeasurable states. The effectiveness of the proposed control scheme is simulated by an F-16 aircraft.