arXiv subjects

Haoyan Lin

Publications and source records attributed to Haoyan Lin.

3 recordsLinked to original sources

arXiv2026

Data-Driven Output Feedback based Analysis and Control for Unknown Discrete-Time Linear System

Using the notion of data informativity, the existing results have given conditions under which the data are informative for controller designs for various control problems of unknown linear discrete-time systems, and have developed methods to compute such controllers from data. Nevertheless, the existing conditions for computing a dynamic output feedback control law based on the input and output data are somehow stringent. In this paper, we first focus on developing data informativity analysis and control for an unknown system with a known input matrix. It turns out that the informativity conditions for such a system are much milder and the methods for computing state feedback control laws for stabilization, deadbeat control, and the linear-quadratic regulator (LQR) for such a system are much more straightforward. Further, based on the parameterized observer, we show that the problem of computing a dynamic output feedback control law for an unknown system can be converted to the problem of designing a state feedback control law for an ancillary system whose input matrix is known. Therefore, the results of the first part of this paper can be directly used to compute a dynamic output feedback control law for stabilization and deadbeat control for unknown linear discrete-time systems based on the input and output data. Moreover, we present a dynamic output feedback control law which will asymptotically approach a state feedback LQR solution to the original unknown system.

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arXiv2026

Data-Driven Output-Based Approach to the Output Regulation Problem of Unknown Linear Systems via Value Iteration

The output regulation problem for unknown linear systems has been studied using state-based and output-based internal model approaches in the special case with no disturbances. This paper further investigates the output regulation problem for unknown linear systems using a data-driven output-based approach via value iteration. For this purpose, we first develop a novel output-feedback control law that does not explicitly rely on the observer gain to solve the output regulation problem. We then show that the data-driven approach for designing an output-feedback control law for the given plant can be reduced to the data-driven design of a state-feedback control law for a well-defined augmented auxiliary system. As a result, we develop a systematic data-driven approach to solve the output regulation problem for unknown linear systems via value iteration. Finally, we establish a relation between the data-driven state-feedback control law and the data-driven output-feedback control law in the LQR sense.

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arXiv2025

A New Approach to the Data-Driven Output-Based LQR Problem of Continuous-Time Linear Systems

A promising method for constructing a data-driven output-feedback control law involves the construction of a model-free observer. The Linear Quadratic Regulator (LQR) optimal control policy can then be obtained by both policy-iteration (PI) and value-iteration (VI) algorithms. However, this method requires some unknown parameterization matrix to be of full row rank and needs to solve a sequence of high dimensional linear equations for either PI or VI algorithm. In this paper, we first show that this matrix is of full row rank under the standard controllability assumption of the plant, thus removing the main hurdle for applying this method. Then we further modify the existing method by defining an ancillary system whose LQR solution will lead to a data-driven output-feedback control law. By this new method, the rank condition of the unknown parameterization matrix is not needed any more. Moreover, we derive a new sequence of linear equations for either PI or VI algorithm whose number of unknown variables is significantly less than that of the existing PI or VI algorithm, thus not only improving the computational efficiency but also relaxing the solvability conditions of the existing PI or VI algorithm. Further, since the existing PI or VI algorithm only applies to the case where some Riccati equation admits a unique positive definite solution, we prove that both the PI algorithm and the VI algorithm in the literature can be generalized to the case where the Riccati equation only admits a unique positive semi-definite solution.

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