On Self-Learning Mechanism for the Output Regulation of Second-Order Affine Nonlinear Systems
This article studies global robust output regulation of second-order nonlinear systems with input disturbances that encompass the fully-actuated Euler-Lagrange systems. We assume the availability of relative output (w.r.t. a family of reference signals) and output derivative measurements. Based on a...
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Published in | IEEE transactions on automatic control Vol. 67; no. 11; pp. 5964 - 5979 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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Abstract | This article studies global robust output regulation of second-order nonlinear systems with input disturbances that encompass the fully-actuated Euler-Lagrange systems. We assume the availability of relative output (w.r.t. a family of reference signals) and output derivative measurements. Based on a specific separation principle and self-learning mechanism, we develop an internal model-based controller that does not require a priori knowledge of reference and disturbance signals and it only assumes that the kernels of these signals are a family of exosystems with unknown parameters (e.g., amplitudes, frequencies, or time periods). The proposed control framework has a self-learning mechanism that extricates itself from requiring absolute position measurement nor precise knowledge of the feedforward kernel signals. By requiring the high-level task/trajectory planner to use the same class of kernels in constraining the trajectories, the proposed low-level controller is able to learn the desired trajectories, to suppress the disturbance signals, and to adapt itself to the uncertain plant parameters. The framework enables a plug-and-play control mechanism in both levels of control. |
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AbstractList | This article studies global robust output regulation of second-order nonlinear systems with input disturbances that encompass the fully-actuated Euler–Lagrange systems. We assume the availability of relative output (w.r.t. a family of reference signals) and output derivative measurements. Based on a specific separation principle and self-learning mechanism, we develop an internal model-based controller that does not require a priori knowledge of reference and disturbance signals and it only assumes that the kernels of these signals are a family of exosystems with unknown parameters (e.g., amplitudes, frequencies, or time periods). The proposed control framework has a self-learning mechanism that extricates itself from requiring absolute position measurement nor precise knowledge of the feedforward kernel signals. By requiring the high-level task/trajectory planner to use the same class of kernels in constraining the trajectories, the proposed low-level controller is able to learn the desired trajectories, to suppress the disturbance signals, and to adapt itself to the uncertain plant parameters. The framework enables a plug-and-play control mechanism in both levels of control. |
Author | Jayawardhana, Bayu Xu, Dabo Wu, Haiwen |
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Snippet | This article studies global robust output regulation of second-order nonlinear systems with input disturbances that encompass the fully-actuated Euler-Lagrange... This article studies global robust output regulation of second-order nonlinear systems with input disturbances that encompass the fully-actuated Euler–Lagrange... |
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SubjectTerms | Adaptation models Adaptive control certainty equivalence principle Controllers Feedforward systems internal model principle Kernel Kernels Learning Nonlinear systems output regulation Parameter uncertainty Position measurement Reference signals Regulation servo systems Task analysis Trajectory Trajectory control |
Title | On Self-Learning Mechanism for the Output Regulation of Second-Order Affine Nonlinear Systems |
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