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This paper proposes a novel learning-based adaptive optimal controller design method for a class of continuous-time linear time-delay systems. A key strategy is to exploit the state-of-the-art reinforcement learning (RL) techniques and adaptive dynamic programming (ADP), and propose a data-driven method to learn the near-optimal controller without the precise knowledge of system dynamics. Specifically, a value iteration (VI) algorithm is proposed to solve the infinite-dimensional Riccati equation for the linear quadratic optimal control problem of time-delay systems using finite samples of input-state trajectory data. It is rigorously proved that the proposed VI algorithm converges to the near-optimal solution. Compared with the previous literature, the nice features of the proposed VI algorithm are that it is directly developed for continuous-time systems without discretization and an initial admissible controller is not required for implementing the algorithm. The efficacy of the proposed methodology is demonstrated by two practical examples of metal cutting and autonomous driving.
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Hybrid robust minimum-time control for a class of non-exponentially unstable planar systems
This paper deals with robust minimum-time control of a class of asymptotically null-controllable with bounded input planar systems. A hybrid controller is proposed to robustly achieve global finite time stability of a set of points wherein the plant state is zero. The resulting controller provides time optimal response from initial conditions in a certain subset of the state space, and finite time convergence elsewhere. Finally, the effectiveness of the proposed methods is demonstrated in a numerical example.
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- Award ID(s):
- 1710621
- PAR ID:
- 10066576
- Date Published:
- Journal Name:
- Proceedings of the 2017 IEEE 56th Annual Conference on Decision and Control (CDC)
- Page Range / eLocation ID:
- 139 to 144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CDC.2017.8263656
