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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… Click to show full 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.
Journal Title: IEEE Transactions on Control Systems Technology
Year Published: 2020
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