scispace - formally typeset
Search or ask a question
Author

Youqing Wang

Bio: Youqing Wang is an academic researcher from Beijing University of Chemical Technology. The author has contributed to research in topics: Iterative learning control & Control theory. The author has an hindex of 25, co-authored 48 publications receiving 2505 citations. Previous affiliations of Youqing Wang include University of California, Santa Barbara & Tsinghua University.


Papers
More filters
Journal ArticleDOI
TL;DR: Three control methods—iterative learning control, repetitive control (RC), and run-to-run control (R2R)—are studied and compared and some promising fields for learning-type control are revealed.

679 citations

Journal ArticleDOI
TL;DR: This manuscript surveys the current state of the art in SILC from the perspective of key techniques, which are divided into three parts: SILC for linear stochastic systems, SilC for nonlinear stochastics systems, and systems with other stochastically signals.

190 citations

Journal ArticleDOI
TL;DR: The proposed methodology is robust to random variations in meal timings within ±60 min or meal amounts within ±75% of the nominal value, which validates MPILC's superior robustness compared to run-to-run control.
Abstract: A novel combination of iterative learning control (ILC) and model predictive control (MPC), referred to here as model predictive iterative learning control (MPILC), is proposed for glycemic control in type 1 diabetes mellitus. MPILC exploits two key factors: frequent glucose readings made possible by continuous glucose monitoring technology; and the repetitive nature of glucose-meal-insulin dynamics with a 24-h cycle. The proposed algorithm can learn from an individual's lifestyle, allowing the control performance to be improved from day to day. After less than 10 days, the blood glucose concentrations can be kept within a range of 90-170 mg/dL. Generally, control performance under MPILC is better than that under MPC. The proposed methodology is robust to random variations in meal timings within ±60 min or meal amounts within ±75% of the nominal value, which validates MPILC's superior robustness compared to run-to-run control. Moreover, to further improve the algorithm's robustness, an automatic scheme for setpoint update that ensures safe convergence is proposed. Furthermore, the proposed method does not require user intervention; hence, the algorithm should be of particular interest for glycemic control in children and adolescents.

147 citations

Journal ArticleDOI
TL;DR: In this paper, an online iterative learning model predictive control (ILMPC) law is proposed with a quadratic programming problem to be solved online, and an offline ILMPC is also proposed and compared.

139 citations

Journal ArticleDOI
TL;DR: The experimental results show that the proposed KDICA compares favorably with existing methods, and a new method of fault diagnosis, a non-linear contribution plot is developed for KDICA.

130 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view.
Abstract: This paper provides an overview of the recent developments in data-based techniques focused on modern industrial applications. As one of the hottest research topics for complicated processes, the data-based techniques have been rapidly developed over the past two decades and widely used in numerous industrial sectors nowadays. The core of data-based techniques is to take full advantage of the huge amounts of available process data, aiming to acquire the useful information within. Compared with the well-developed model-based approaches, data-based techniques provide efficient alternative solutions for different industrial issues under various operating conditions. The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view. This paper begins with a brief evolutionary overview of data-based techniques in the last two decades. Then, the methodologies only based on process measurements and the model-data integrated techniques will be further introduced. The recent developments for modern industrial applications are, respectively, presented mainly from perspectives of monitoring and control. The new trends of data-based technique as well as potential application fields are finally discussed.

856 citations

Journal ArticleDOI
TL;DR: Three control methods—iterative learning control, repetitive control (RC), and run-to-run control (R2R)—are studied and compared and some promising fields for learning-type control are revealed.

679 citations

Journal ArticleDOI
TL;DR: The Takagi-Sugeno (T-S) fuzzy model approach is adapted with the consideration of the sprung and the unsprung mass variation, the actuator delay and fault, and other suspension performances to design a reliable fuzzy H∞ controller for active suspension systems with actuatordelay and fault.
Abstract: This paper is focused on reliable fuzzy H∞ controller design for active suspension systems with actuator delay and fault. The Takagi-Sugeno (T-S) fuzzy model approach is adapted in this study with the consideration of the sprung and the unsprung mass variation, the actuator delay and fault, and other suspension performances. By the utilization of the parallel-distributed compensation scheme, a reliable fuzzy H∞ performance analysis criterion is derived for the proposed T-S fuzzy model. Then, a reliable fuzzy H∞ controller is designed such that the resulting T-S fuzzy system is reliable in the sense that it is asymptotically stable and has the prescribed H∞ performance under given constraints. The existence condition of the reliable fuzzy H∞ controller is obtained in terms of linear matrix inequalities (LMIs) Finally, a quarter- vehicle suspension model is used to demonstrate the effectiveness and potential of the proposed design techniques.

516 citations

Journal ArticleDOI
TL;DR: The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas).
Abstract: The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.

461 citations

Journal ArticleDOI
TL;DR: The objectives of this article are to introduce recent development and advances in nonlinear ILC schemes, highlight their effectiveness and limitations, as well as discuss the directions for further exploration of non linear ILC.
Abstract: In this article we review the recent advances in iterative learning control (ILC) for nonlinear dynamic systems. In the research field of ILC, two categories of system nonlinearities are considered, namely, the global Lipschitz continuous (GLC) functions and local Lipschitz continuous (LLC) functions. ILC for GLC systems is widely studied and analysed using contraction mapping approach, and the focus of recent exploration moves to application problems, though a number of theoretical issues remain open. ILC for LLC systems is currently a hot area and the recent research focuses on ILC design and analysis by means of Lyapunov approach. The objectives of this article are to introduce recent development and advances in nonlinear ILC schemes, highlight their effectiveness and limitations, as well as discuss the directions for further exploration of nonlinear ILC.

349 citations