Iterative Learning Control for uncertain systems: Robust monotonic convergence analysis
نویسندگان
چکیده
In this paper, we present a novel Robust Monotonic Convergence (RMC) analysis approach for finite time interval Iterative Learning Control (ILC) for uncertain systems. For that purpose, a finite time interval model for uncertain systems is introduced. This model is subsequently used in an RMC analysis based on μ analysis. As a result, we can handle additive andmultiplicative uncertainty models in the RMC problem formulation, analyze RMC of linear time invariant MIMO systems controlled by any linear trial invariant ILC controller, and formulate additional straightforward RMC conditions for ILC controlled systems. To illustrate the derived results, we analyze the RMC properties of linear quadratic (LQ) norm optimal ILC. © 2009 Elsevier Ltd. All rights reserved.
منابع مشابه
Perfect Tracking of Supercavitating Non-minimum Phase Vehicles Using a New Robust and Adaptive Parameter-optimal Iterative Learning Control
In this manuscript, a new method is proposed to provide a perfect tracking of the supercavitation system based on a new two-state model. The tracking of the pitch rate and angle of attack for fin and cavitator input is of the aim. The pitch rate of the supercavitation with respect to fin angle is found as a non-minimum phase behavior. This effect reduces the speed of command pitch rate. Control...
متن کاملRobust Iterative Learning Controller Design for 2d Uncertain Linear Systems Subject to External Disturbances
This paper presents a robust iterative learning control (ILC) for a class of two dimensional (2D) linear systems with parametric uncertainty and considerable disturbances. The proposed control law is iteratively updated to guarantees the robust stability. Based on H∞ setting, sufficient conditions for robust monotonic convergence of the proposed scheme are presented in terms of linear matrix in...
متن کاملRobust iterative learning control with current feedback for uncertain linear systems
Considering an uncertain plant in iterative learning control (ILC), robust convergence and robust stability are important issues. Since the feedback controller robustly stabilizes the uncertain plant and has an e ect on the convergence, it plays as signi® cant a role as the learning controller does in the ILC system. To deal with both convergence and stability in ILC, we take account of an ILC...
متن کاملMonotonic convergence of iterative learning control systems with variable pass length
Monotonic convergence of iterative learning control systems with variable pass length Thomas Seel, Thomas Schauer & Jörg Raisch To cite this article: Thomas Seel, Thomas Schauer & Jörg Raisch (2017) Monotonic convergence of iterative learning control systems with variable pass length, International Journal of Control, 90:3, 393-406, DOI: 10.1080/00207179.2016.1183172 To link to this article: ht...
متن کاملLMI-Based Synthesis of Robust Iterative Learning Controller with Current Feedback for Linear Uncertain Systems 171 LMI-Based Synthesis of Robust Iterative Learning Controller with Current Feedback for Linear Uncertain Systems
This paper addresses the synthesis of an iterative learning controller for a class of linear systems with norm-bounded parameter uncertainties. We take into account an iterative learning algorithm with current cycle feedback in order to achieve both robust convergence and robust stability. The synthesis problem of the developed iterative learning control (ILC) system is reformulated as the γ -s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Automatica
دوره 45 شماره
صفحات -
تاریخ انتشار 2009