arXiv subjects

Hamin Chang

Publications and source records attributed to Hamin Chang.

3 recordsLinked to original sources

arXiv2026

Inverse Learning-Based Output Feedback Control of Nonlinear Systems with Verifiable Guarantees

In this paper, we present a data-driven output feedback controller for nonlinear systems that achieves practical output regulation, using noise-free input/output measurement data. The proposed controller is based on (i) an inverse model of the system identified via kernel interpolation, which maps a desired output and the current state to the corresponding desired control input; and (ii) a data-driven reference selection framework that actively chooses a suitable desired output from the dataset which has been used for the identification. We establish a verifiable sufficient condition on the dataset under which the proposed controller guarantees practical output regulation. Numerical simulations demonstrate the effectiveness of the proposed controller, with additional evaluations in the presence of output measurement noise to assess its robustness empirically.

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arXiv2023

Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller

This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the system's state/input measurements. When its results are provided, we present conditions that guarantee a certain level of reference tracking performance, regardless of the identification method employed for the inverse model. Specifically, when Gaussian process regression (GPR) is used as the identification method, we propose sufficient conditions for the required data by applying some lemmas related to identification errors to the aforementioned conditions, ensuring that the Model reference-GPR (MR-GPR) controller can guarantee a certain level of reference tracking performance. Finally, an example is provided to demonstrate the effectiveness of the MR-GPR controller.

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arXiv2022

Model Reference Gaussian Process Regression: Data-Driven Output Feedback Controller

Data-driven controls using Gaussian process regression have recently gained much attention. In such approaches, system identification by Gaussian process regression is mostly followed by model-based controller designs. However, the outcomes of Gaussian process regression are often too complicated to apply conventional control designs, which makes the numerical design such as model predictive control employed in many cases. To overcome the restriction, our idea is to perform Gaussian process regression to the inverse of the plant with the same input/output data for the conventional regression. With the inverse, one can design a model reference controller without resorting to numerical control methods. This paper considers single-input single-output (SISO) discrete-time nonlinear systems of minimum phase with relative degree one. It is highlighted that the model reference Gaussian process regression controller is designed directly from pre-collected input/output data without system identification.

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