Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 206-210· 0 citations· 26 references
Abstract
In this paper, data generated by a plant that can be described as a linear time-invariant system subject to both process and measurement noise are considered. An original and structured organization of the collected input–output data is introduced, leading to an alternative representation of the system dynamics within an extended state-space framework. Based on this formulation, the Dynamic Mode Decomposition with Control algorithm is employed to identify a Data-Driven model in a lifted space. The resulting representation enhances robustness with respect to noise, allowing for a more accurate and reliable characterization of the underlying system dynamics in the presence of disturbances and measurement uncertainty. The proposed approach is assessed through numerical simulations, which illustrate its effectiveness in capturing the system behavior and mitigating the impact of noisy data, thereby highlighting its potential for data-driven modeling and control applications.
This paper presents a synergistic control strategy for discrete-time singularly perturbed systems, where data-driven learning is seamlessly combined with LMI-based synthesis, thereby offering an effective new approach for controlling discrete-time singularly perturbed systems in complex engineering environments.
Peng Wang, Wenkai Zhou, Yangyang Wang et al.· Advances in Complex Systems· 0 citations
This paper presents a subspace data-driven predictive control method for linear parameter-varying (LPV) systems. Starting from an affine LPV state-space model in innovation form, we derive a multi-step predictor that separates the effects of past data, future inputs, scheduling trajectories, and innovations. By projecting this representation onto the row span of lifted input-output-scheduling data, we obtain an asymptotically unbiased data-driven predictor that can be embedded directly in a receding-horizon control problem, without explicitly identifying an LPV model. To make the resulting LPV data-driven predictive control (DDPC) formulation tractable, we introduce an LPV extension of $\gamma$-DDPC based on an LQ factorization. This formulation fixes the number of online decision variables independently of the length of the dataset. A reduced-order predictor is then proposed to curb the exponential growth of scheduling-dependent regressors, which also relaxes the persistence-of-excitation condition. Simulation studies, including an unbalanced-disk example, show that the proposed controller achieves good tracking performance and, compared to existing LPV DDPC schemes, achieves better robustness to measurement noise and reduced computational cost, making multi-step LPV DDPC practically deployable, even with longer past horizons.
Federico Porcari, C. Verhoek, V. Breschi et al.· arXiv.org· 0 citations
This letter addresses the distributed observer design problem for linear interconnected systems with unknown dynamics using offline data. In contrast to classical model-based methods, the proposed approach constructs a distributed observer through a direct data-driven formulation without explicitly identifying a parametric state-space model. A key idea is to explicitly incorporate neighboring outputs so that the coupling terms can be canceled in the local error dynamics, rather than being treated solely as unknown inputs. Necessary and sufficient data-based conditions are derived for observer feasibility and asymptotic convergence. Furthermore, these direct data-driven conditions are shown to be equivalent to their model-based counterparts, thereby clarifying the relation between data-driven synthesis and model-based observer design. A multi-area power benchmark is employed to show the effectiveness of the proposed method.
Yalin Gui, Bo Chen, Zheming Wang et al.· IEEE Signal Processing Lette...· 0 citations
This work proposes a data-driven predictive control framework for nonlinear systems that incorporates data column preferences according to their proximity to the current operating point through a weighted norm regularization, thereby localizing the predictor without discarding any data.
F. Engeln, S. Zieglmeier, Marta A. Zagorowska et al.· 0 citations
We propose a data-driven method for designing state-feedback gains that achieve stabilization, $H_2$-control, and $H_\infty$-control for continuous-time systems. The state-input data are assumed to be corrupted by process noise, measurement noise, and input disturbances. We first characterize the set of all systems consistent with the noisy data using operator-based data embedding. This characterization yields necessary and sufficient conditions for data informativity under a certain class of noise. These conditions are formulated as linear matrix inequalities, and the feedback gains are constructed from their solutions. To enable direct controller design from noisy sampled data for continuous-time systems, we also obtain an upper bound on the reconstruction error of continuous-time signals.
Nonlinear electrical and electromechanical systems pose significant challenges for observer-based control design. Conventional observer approaches require accurate mathematical models, which often fail under physical irregularities such as sensor noise, measurement delays, and parameter variations. These changes decrease estimation accuracy and degrade control action. This paper proposes a Hybrid ARX– Observer framework to overcome these limitations. It combines the stability of a traditional model-based observer with the flexibility of a data-driven ARX estimator. To guarantee input-to-state stability (ISS), observer gains are computed using a matrix-multiplier technique based on linear matrix inequalities (LMI). The ARX component employs Recursive Least Squares (RLS) adaptation to attenuate measurement noise and capture residual nonlinearities. Both estimates are then fused together using a fusion gain α. This framework is tested on a robotic arm and the results show that the hybrid framework improved the robustness and reduced estimation error up to 4% compared to the conventional observer based estimation, while remaining computationally light compared to fully data-driven alternatives.
Tanmay Wankhade, Saish Pakhare, Aakanksha Mane et al.· International Conference on...· 0 citations
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