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- W3120743337 abstract "This paper aims at using an off-policy integral reinforcement learning (IRL) algorithm to solve the linear quadratic tracking (LQT) control problem of completely unknown continuous-time systems, such that an arbitrary higherorder polynomial signal can be followed via an optimal approach. Firstly, a linear continuous-time system with unknown model matrices is introduced with a target of tracking the reference signal with higher-order polynomials. Secondly, based on the knowledge of the on-policy IRL, an off-policy IRL algorithm is used to solve the derived iterative Bellman equation and update the control policy resulting in the optimal control policy. Finally, a simulation example is given to show the efficiency of the proposed approach." @default.
- W3120743337 created "2021-01-18" @default.
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- W3120743337 date "2020-12-13" @default.
- W3120743337 modified "2023-09-27" @default.
- W3120743337 title "Higher-order Polynomial Signal Tracking Control of Unknown Systems using Off-policy Integral Reinforcement Learning" @default.
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- W3120743337 doi "https://doi.org/10.1109/icarcv50220.2020.9305458" @default.
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