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2004
Automatica
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Equivalent feedback control-is the idea meaningful? ...
The area of iterative learning control (ILC) now has a large and increasing body of research with an increasing number of applications (supported by a sizable number of actual experimental veriÿcation ...
doi:10.1016/j.automatica.2003.05.001
fatcat:m6bjdira2ve33cwdidaoomgqyu
RLO-MPC: Robust Learning-Based Output Feedback MPC for Improving the Performance of Uncertain Systems in Iterative Tasks
[article]
2021
arXiv
pre-print
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Previously, this problem was solved for linear time-invariant (LTI) system for the case when noisy full-state measurements are available using a robust iterative learning control framework, which we refer ...
To overcome these limitations, we propose a combination of RL-MPC with robust output feedback model predictive control, named robust learning-based output feedback model predictive control (RLO-MPC). ...
Our proposed robust learning-based output feedback MPC (RLO-
MPC, red) combines robust output feedback MPC with iterative learning
control. ...
arXiv:2110.00542v1
fatcat:cpr3tyq33jb4faeufznxfjauuq
Page 6298 of Mathematical Reviews Vol. , Issue 2002H
[page]
2002
Mathematical Reviews
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iterative learning control. ...
The objective is to track a
specific realizable output profile. A “learning” control is proposed
and is given by a linear combination of past inputs and tracking
errors from previous cycles. ...
High bandwidth control of precision motion instrumentation
2008
Review of Scientific Instruments
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Iterative learning control ͑ILC͒, a feedforward technique that uses previous iterations of the desired trajectory, is used to leverage the repetition that occurs in many tasks, such as raster scanning ...
The ILC designs demonstrate significant bandwidth and precision improvements over the feedback controller, and the ability to achieve precision motion control at frequencies higher than multiple system ...
ITERATIVE LEARNING CONTROL ILC uses several iterations, or trials, of a process to automatically generate the feedforward control. ...
doi:10.1063/1.2980377
pmid:19044716
fatcat:duuxey3fnngntfc76r52f4knzm
Data-Driven Control of Linear Time-Varying Systems
2020
2020 59th IEEE Conference on Decision and Control (CDC)
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Further contributions to data-driven control include model-free adaptive control [6], iterative feedback tuning [7], virtual reference feedback tuning [8] and unfalsified control [9]. ...
In particular, with the availability of increasing computational power and novel machine learning techniques, model-free controllers using neural networks [2] and reinforcement learning [3], [4] have gained ...
ACKNOWLEDGEMENTS The authors thank Prof. Claudio De Persis (University of Groningen) for his valuable comments. ...
doi:10.1109/cdc42340.2020.9303845
fatcat:anwkecyhcvbz5odhnadtxgzvfe
Data-Driven Control of Unknown Systems: A Linear Programming Approach
2020
IFAC-PapersOnLine
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We develop both policy iteration (PI) and value iteration (VI) methods to compute an approximate optimal feedback controller with high precision and without the knowledge of a system model and stage cost ...
We develop both policy iteration (PI) and value iteration (VI) methods to compute an approximate optimal feedback controller with high precision and without the knowledge of a system model and stage cost ...
We develop both policy iteration (PI) and value iteration (VI) methods to compute an approximate optimal feedback controller with high precision and without the knowledge of a system model and stage cost ...
doi:10.1016/j.ifacol.2020.12.027
fatcat:3psowni2erfflif7h5rq3e64iy
Comparison of Standard and Lifted ILC on a Motion System
2004
IFAC Proceedings Volumes
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Iterative Learning Control (ILC) is a technique for improving the performance of systems or processes that operate repetitively over a fixed time interval. ...
The basic idea of ILC is that it exploits every possibility to incorporate past repetitive control information, such as tracking errors and control input signals into the construction of the present control ...
Modern theory on systems and control offers a large number of (different) techniques for designing a high-performance motion control system, like H ∞ feedback control, Iterative Learning Control, and many ...
doi:10.1016/s1474-6670(17)31105-9
fatcat:lejpjgrfhfhhpcfsmbf26472dy
A frequency domain iterative feed-forward learning scheme for high performance periodic quadrocopter maneuvers
2013
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
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The learning is carried out in the frequency domain and uses a simplified model of the closed-loop dynamics of quadrocopter and feedback controller. ...
Due to the use of simplified models in the design of feedback control algorithms, the execution of highperformance flight maneuvers under pure feedback control typically leads to large tracking errors. ...
A list of past and present participants of the project is available at http://bit.ly/RdO6g1 This research was funded in part by the Swiss National Science Foundation (SNSF). ...
doi:10.1109/iros.2013.6696700
dblp:conf/iros/HehnD13
fatcat:lrdj7x7t65f3totokzworkwkoq
Robust Iterative Learning Control Design: Application to a Robot Manipulator
2008
IEEE/ASME transactions on mechatronics
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The design procedure is based upon solving the robust performance condition using the Youla parameterization and the µ-synthesis approachto obtain a feedback controller. ...
Index Terms-Iterative learning control (ILC), robot manipulators, robust performance. I. INTRODUCTION For many mechanical components in mechatronic systems and robotics, the motions are repeatable. ...
through the feedback controller, and the performance of the iterative process through the learning filters. ...
doi:10.1109/tmech.2008.2004627
fatcat:alng37kmv5fw3hidhov6qviyxy
Page 1233 of Mathematical Reviews Vol. , Issue 91B
[page]
1991
Mathematical Reviews
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The authors examine first the case of linear time-invariant (LTI) causal learning control for known plants and find conditions for convergence with zero and nonzero errors. ...
The control law is an output feedback with time-varying gain and reference input. ...
Extrapolation of optimal lifted system ILC solution, with application to a waferstage
2002
Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301)
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The optimal solution from the lifted system design methods presented by Tousain [ 13 has many advantages over classic Zterative Learning Control (ILC) design methods, with one drawback: the ILC solution ...
This low order extrapolated solution has been compared to a high order lifted system optimal control (Q-)lLC solution in the application to an industrial grade wafer stage, showing the value of this extrapolated ...
It does however facilitate understanding and analysis of Iterative Learning Control. ...
doi:10.1109/acc.2002.1025176
fatcat:5kpcuayuifaefiauvmwhkaabpy
A computational method for simultaneous LQ optimal control design via piecewise constant output feedback
2001
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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We show that the problem can be reduced to the design of an constant output feedback gain minimizing a set of equivalent discrete-time performance indexes for a set of LTI discrete-time systems. ...
For example, the solution of (14) P j i and F j also satisfies (15). In the subsequent iterations, the solution of OP3 can be guaranteed by the solution of the previous iteration. ...
Concept maps possess a number of appealing features which make them a promising tool for teaching, learning, evaluation, and curriculum planning. ...
doi:10.1109/3477.956046
pmid:18244849
fatcat:aym7trl2b5bj3fsgs3qbhspsay
Joint optimization of communication rates and linear systems
2003
IEEE Transactions on Automatic Control
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We optimize the stationary performance of the linear system by jointly allocating resources in the communication system and tuning parameters of the controller. ...
With the coding and medium access schemes of the communication system fixed, the achievable bit rates are determined by the allocation of communications resources such as transmit powers and bandwidths ...
ACKNOWLEDGMENT The authors would like to thank W. Yu and X. Liu for helpful discussions. ...
doi:10.1109/tac.2002.806669
fatcat:hrq675e6zvf37pqliire6raeum
Imitation Learning of Stabilizing Policies for Nonlinear Systems
[article]
2021
arXiv
pre-print
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Work in this area has generally considered linear systems and controllers, for which stabilizing imitation learning takes the form of a biconvex optimization problem. ...
In this paper it is demonstrated that the same methods developed for linear systems and controllers can be readily extended to polynomial systems and controllers using sum of squares techniques. ...
A major limitation of the previous work in this area is that stability has only been considered with respect to LTI systems, and often for linear state-feedback controllers. ...
arXiv:2109.10854v1
fatcat:eeccatras5cltogfn4paqtjvtm
An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems
[article]
2023
arXiv
pre-print
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In this paper, we present a Q-learning algorithm to solve the optimal output regulation problem for discrete-time LTI systems. ...
Moreover, our formulation of the proposed algorithm renders it computationally efficient. We provide conditions that guarantee the convergence of the algorithm to the optimal solution. ...
Acknowledgments This work has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 948679). ...
arXiv:2312.03451v1
fatcat:y435vxaq6zcktfvu2kjamotsuy
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