Receding Horizon Neural H_inf Control for a Class of Nonlinear Unknown Systems
| Authors: | Ahn Choon Ki, Seoul National University, Korea, Republic of
Han Soo Hee, Seoul National University, Korea, Republic of
Kwon Wook Hyun, Seoul National University, Korea, Republic of |
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| Topic: | 2.3 Non-Linear Control Systems |
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| Session: | Nonlinear Controller Design |
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| Keywords: | Receding horizon control, Neural networks, H_inf control, Nonlinear systems, Unknown systems |
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Abstract
In this paper, we present new RHNHC (Receding Horizon Neural H_inf Control) for nonlinear unknown systems. First, we propose LMI (Linear Matrix Inequality) condition on the terminal weighting matrix for stabilizing RHNHC. Under this condition, noninceasing monotonicity of the saddle point value of the finite horizon dynamic game is shown to be guaranteed. Then, we propose RHNHC for nonlinear unknown systems which guarantees the infinite horizon H_inf norm bound and the internal stability of the closed-loop systems. Since RHNHC can deal with input and state constraints in optimization problem effectively, it does not cause an instability problem or give a poor performance in contrast to the existing neural H_inf control schemes.
