Iterative Learning Control Systems
2 papers on this topic
Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks
The expansion in automation of increasingly fast applications and low-power edge devices poses a particular challenge for optimization based control algorithms, like model predictive control. Our...
Sample- and computationally efficient data-driven predictive control
Recently proposed data-driven predictive control schemes for LTI systems use non-parametric representations based on the image of a Hankel matrix of previously collected, persistently exciting,...
Predictive Control with Learning-Based Terminal Costs Using Approximate Value Iteration
Stability under model predictive control (MPC) schemes is frequently ensured by terminal ingredients. Employing a (control) Lyapunov function as the terminal cost constitutes a common choice....
Adaptive tracking MPC for nonlinear systems via online linear system identification
This paper presents an adaptive tracking model predictive control (MPC) scheme to control unknown nonlinear systems based on an adaptively estimated linear model. The model is determined based on...
Machine learning based iterative learning control for non-repetitive time-varying systems
The repetitive tracking task for time-varying systems (TVSs) with non-repetitive time-varying parameters, which is also called non-repetitive TVSs, is realized in this paper using iterative...
Optimal Output Feedback Learning Control for Discrete-Time Linear Quadratic Regulation
This paper studies the linear quadratic regulation (LQR) problem of unknown discrete-time systems via dynamic output feedback learning control. In contrast to the state feedback, the optimality of...
Reinforcement Learning for Control of Valves
This paper is a study of reinforcement learning (RL) as an optimal-control strategy for control of nonlinear valves. It is evaluated against the PID (proportional-integral-derivative) strategy,...
Real-Time Progressive Learning: Accumulate Knowledge from Control with Neural-Network-Based Selective Memory
Memory, as the basis of learning, determines the storage, update and forgetting of knowledge and further determines the efficiency of learning. Featured with the mechanism of memory, a radial...
Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration
We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees such as stability and optimality...
Fault-Tolerant Individual Pitch Control of Floating Offshore Wind Turbines via Subspace Predictive Repetitive Control
Individual Pitch Control (IPC) is an effective and widely-used strategy to mitigate blade loads in wind turbines. However, conventional IPC fails to cope with blade and actuator faults, and this...
Fast data-driven iterative learning control for linear system with output disturbance
This paper studies data-driven iterative learning control (ILC) for linear time-invariant (LTI) systems with unknown dynamics, output disturbances and input box-constraints. Our main contributions...
Combining system identification with reinforcement learning-based MPC
In this paper we propose and compare methods for combining system identification (SYSID) and reinforcement learning (RL) in the context of data-driven model predictive control (MPC). Assuming a...
Robust Monotonic Convergent Iterative Learning Control Design: an LMI-based Method
This work investigates robust monotonic convergent iterative learning control (ILC) for uncertain linear systems in both time and frequency domains, and the ILC algorithm optimizing the...
Learning Model Predictive Control for Iterative Tasks: A Computationally Efficient Approach for Linear System
A Learning Model Predictive Controller (LMPC) for linear system in presented. The proposed controller is an extension of the LMPC [1] and it aims to decrease the computational burden. The control...
Output-Lifted Learning Model Predictive Control
We propose a computationally efficient Learning Model Predictive Control (LMPC) scheme for constrained optimal control of a class of nonlinear systems where the state and input can be...
Industrial Robot Trajectory Tracking Using Multi-Layer Neural Networks Trained by Iterative Learning Control
Fast and precise robot motion is needed in certain applications such as electronic manufacturing, additive manufacturing and assembly. Most industrial robot motion controllers allow externally...
System Equivalence Transformation: Robust Convergence of Iterative Learning Control with Nonrepetitive Uncertainties
For iterative learning control (ILC), one of the basic problems left to address is how to solve the contradiction between convergence conditions for the output tracking error and for the input...
Indirect Adaptive Control Using a Static Update Law
The update law in the indirect adaptive control scheme can be extended to include feedthrough of an error term. This reduces undesired oscillations of the calculated weights. When the...