Fitted Q-Function Control Methodology Based on Takagi-Sugeno Systems

Abstract:

This paper presents a combined identification/Q-function fitting methodology that involves identification of a Takagi-Sugeno model, computation of (sub)optimal controllers from linear matrix inequalities (LMIs), and subsequent data-based fitting of the Q-function via monotonic optimization. The LMI-based initialization provides a conservative solution, but it is a sensible starting point to avoid convergence/local-minima issues in raw data-based fitted Q-iteration or Bellman residual minimization. An inverted-pendulum experimental case study illustrates the approach.

Año de publicación:

2020

Keywords:

• fitted Q-function
• Adaptive dynamic programing (DP)
• Linear Matrix Inequality (LMI)
• Takagi-Sugeno (TS)
• reinforcement learning (RL)

Fuente:

scopus