Robust gradient-based discrete-time iterative learning control algorithms

This paper considers the use of matrix models and the robustness of a gradient-based Iterative Learning Control (ILC) algorithm using both fixed learning gains and gains derived from parameter optimization. The philosophy of the paper is to ensure monotonic convergence with respect to the mean square value of the error time series. The paper provides a complete and rigorous analysis for the systematic use of matrix models in ILC. Matrix models make analysis clearer and provide necessary and sufficient conditions for robust monotonic convergence. They also permit the construction of sufficient frequency domain conditions for robust monotonic convergence on finite time intervals for both causal and non-causal controller dynamics. The results are compared with recent results for robust inverse-model based ILC algorithms and it is seen that the algorithm has the potential to improve robustness to high frequency modelling errors provided that resonances within the plant bandwidth have been suppressed by feedback or series compensation

CopyCAT: Taking Control of Neural Policies with Constant Attacks

We propose a new perspective on adversarial attacks against deep reinforcement learning agents. Our main contribution is CopyCAT, a targeted attack able to consistently lure an agent into following an outsider's policy. It is pre-computed, therefore fast inferred, and could thus be usable in a real-time scenario. We show its effectiveness on Atari 2600 games in the novel read-only setting. In this setting, the adversary cannot directly modify the agent's state -- its representation of the environment -- but can only attack the agent's observation -- its perception of the environment. Directly modifying the agent's state would require a write-access to the agent's inner workings and we argue that this assumption is too strong in realistic settings.Comment: AAMAS 202

Learning for Advanced Motion Control

Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presented, where fast learning is achieved through noncausal model inversion, and safe and robust learning is achieved by employing a contraction mapping theorem in conjunction with nonparametric frequency response functions. The approach is demonstrated on a desktop printer. Finally, a detailed analysis of industrial motion systems leads to several shortcomings that obstruct the widespread implementation of ILC algorithms. An overview of recently developed algorithms, including extensions using machine learning algorithms, is outlined that are aimed to facilitate broad industrial deployment.Comment: 8 pages, 15 figures, IEEE 16th International Workshop on Advanced Motion Control, 202