Lemei M. Zhu

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Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on(More)
This paper uses the integral reinforcement learning (IRL) technique to develop an online learning algorithm for finding suboptimal static output-feedback controllers for partially-unknown continuous-time (CT) linear systems. To our knowledge, this is the first static output-feedback control design method based on reinforcement learning for CT systems. In(More)
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