Journal ArticleDOI
An Overview of Dynamic-Linearization-Based Data-Driven Control and Applications
TLDR
This work highlights the characteristics and comments of the different model-free adaptive control schemes in detail to facilitate the understanding of the readers.Abstract:
A brief overview on the model-based control and data-driven control methods is presented. The data-driven equivalent dynamic linearization, as a foundational analysis tool of data-driven control methods for discrete-time nonlinear systems, is introduced in detail with motivations and distinct features. The prototype model-free adaptive control schemes by using the dynamic linearization to an unknown nonlinear plant model, as well as the alternative model-free adaptive control methods by using the dynamic linearization to an unknown ideal nonlinear controller, are discussed. Furthermore, the extensions of the dynamic linearization to unknown nonlinear repetitive systems and the corresponding model-free adaptive iterative learning control methods are also overviewed and summarized. This work highlights the characteristics and comments of the different model-free adaptive control schemes in detail to facilitate the understanding of the readers. Finally, some perspectives on data-driven control methods in information-rich age are given.read more
Citations
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Journal ArticleDOI
On Model-Free Adaptive Control and Its Stability Analysis
TL;DR: The theoretical analysis of the bounded-input bounded-output stability, the monotonic convergence of the tracking error dynamics, and the internal stability of the full form dynamic linearization based MFAC scheme are rigorously presented by the contraction mapping principle.
Proceedings ArticleDOI
Adaptive Iterative Learning Control for Robot Manipulators
Genzhong Wu,Haiyan Zhu,Zeqi Wu +2 more
TL;DR: In this paper, an adaptive iterative learning algorithm is applied on manipulators with repetitive time-varying disturbance to reduce the torque chattering problem and to force the manipulators to track time-changing reference signal fast, correctly and with assigned speed.
Journal ArticleDOI
Data-Driven Control and Learning Systems
TL;DR: Any industry processes, aerospace systems, transportation systems, transport systems, power grid systems, etc. are becoming more and more complex, it would be very significant if the establishment and development of data-driven control theory and methodology are the urgent issues.
Journal ArticleDOI
High-Order Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle With Enhanced Convergence
TL;DR: This article proposes a high-order pseudopartial derivative-based model-free adaptive iterative learning controller (HOPPD-MFAILC) that can track the desired trajectory with improved convergence and tracking performance.
Journal ArticleDOI
Feedback Linearization Based on Gaussian Processes With Event-Triggered Online Learning
Jonas Umlauft,Sandra Hirche +1 more
TL;DR: A learning feedback linearizing control law using online closed-loop identification that ensures high data efficiency and thereby reduces computational complexity, which is a major barrier for using Gaussian processes under real-time constraints.
References
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Model Predictive Control: A Review of Its Applications in Power Electronics
Sergio Vazquez,Jose I. Leon,Leopoldo G. Franquelo,Jose Rodriguez,Hector Young,Abraham Marquez,Pericle Zanchetta +6 more
TL;DR: Model-based predictive control (MPC) for power converters and drives is a control technique that has gained attention in the research community as mentioned in this paper, and it can easily handle multivariable case and system constraints and nonlinearities in a very intuitive way.
Journal ArticleDOI
Iterative feedback tuning: theory and applications
TL;DR: An optimization approach to iterative control design and a direct optimal tuning algorithm that is particularly well suited for the tuning of the basic control loops in the process industry, which are typically PID loops.
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