2023
DOI: 10.1109/tac.2023.3246761
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Deep Neural Network-Based Approximate Optimal Tracking for Unknown Nonlinear Systems

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Cited by 20 publications
(5 citation statements)
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“…Concurrent learning has been incorporated into other control frameworks beyond the conventional adaptive control to enhance learning and control performance [48], [50], [63]- [76]. In [63], concurrent learning was employed for plant model learning to enhance the performance of model predictive control.…”
Section: Discrete Data-driven Mrementioning
confidence: 99%
See 2 more Smart Citations
“…Concurrent learning has been incorporated into other control frameworks beyond the conventional adaptive control to enhance learning and control performance [48], [50], [63]- [76]. In [63], concurrent learning was employed for plant model learning to enhance the performance of model predictive control.…”
Section: Discrete Data-driven Mrementioning
confidence: 99%
“…The extensions of concurrent learning to the neural network (NN) control of nonlinear systems with unstructured uncertainties were done in [48], [50], [66]- [71]. Concurrent learning was incorporated into adaptive dynamic programming and reinforcement learning for developing adaptive optimal control methods in [72]- [76].…”
Section: Discrete Data-driven Mrementioning
confidence: 99%
See 1 more Smart Citation
“…15 For a class of nonlinear control-affine systems, a deep NN was updated in real time to approximate the unknown dynamics, which used a multiscale concurrent learning-based weight update policy. 16 For an unmanned surface vehicle with complex unknowns, NNs approach was further used to identify complex unknowns. On this basis, the optimal tracking control was realized.…”
Section: Introductionmentioning
confidence: 99%
“…RL was employed to resolve optimal controller design issue of nonlinear unknown strict‐feedback systems, and a NNs‐based observer was developed to identify unknown dynamics 15 . For a class of nonlinear control‐affine systems, a deep NN was updated in real time to approximate the unknown dynamics, which used a multiscale concurrent learning‐based weight update policy 16 . For an unmanned surface vehicle with complex unknowns, NNs approach was further used to identify complex unknowns.…”
Section: Introductionmentioning
confidence: 99%