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Optimal Observer Design Using Reinforcement Learning and Quadratic Neural Networks
It is proven that the obtained linear correction term is optimal for linear systems, as both the value function and the QNN's input-output mapping are quadratic.
Data-Driven LQR using Reinforcement Learning and Quadratic Neural Networks
This paper introduces a novel data-driven approach to design a linear quadratic regulator (LQR) using a reinforcement learning (RL) algorithm that does not require a system model.
Optimal Cruise Airspeed for Hybrid-Electric and Electric Aircraft: Applications to Air Mobility
Electric and hybrid-electric aircraft can help our society transition towards more sustainable aviation and lower greenhouse gas (GHG) emissions.
From LQR to Static Output Feedback: a New LMI Approach
Finally, for the cases where the output is equal to the state it is shown that the LQR solution verifies the proposed LMI.
Analysis and Design of Quadratic Neural Networks for Regression, Classification, and Lyapunov Control of Dynamical Systems
The results in the paper cast regression, classification, system identification, stability and control design as convex optimization problems, which can be solved efficiently with polynomial-time algorithms to a global optimum.
