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

CAutoCSD-evolutionary search and optimisation enabled computer automated control system design

This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of 'Computer-Aided Control System Design' (CACSD) to the novel 'Computer-Automated Control System Design' (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency-domains. Such performance-prioritised unification is aimed to relieve practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-committing to the adopted scheme. With the recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible, analytically and practically, and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, meets multiple objectives in designing an LTI controller for a non-minimum phase plant and offers a high-performing LTI controller network for a nonlinear chemical process

Investigations into implementation of an iterative feedback tuning algorithm into microcontroller

Includes abstract.Includes bibliographical references (leaves 73-75).Implementation of an Iterative Feedback Tuning (IFT) and Myopic Unfalsified Control (MUC) Algorithm into microcontroller is investigated in this dissertation. Motivation in carrying out this research emanates from successful results obtained in application of IFT algorithm to various physical systems since the method was originated in 1995 by Hjalmarsson [4]. The Motorola DSP56F807C microcontroller is selected for use in the investigations due to its matching characteristics with the requirements of IFT algorithm. Speed of program execution, large memory, in-built ADC & DAC and C compiler type are the key parameters qualifying for its usage. The Analog Devices ARM7024 microcontroller was chosen as an alternative to the DSP56F807C where it is not available. Myopic Unfalsified Control (MUC) is noted to be similar to IFT since it also employs ‘myopic’ gradient based steepest descent approach to parameter optimization. It is easier to implement in that its algorithm is not as complex as the IFT one, meaning that successful implementation of IFT algorithm in a microcontroller would obviously permit the implementation of MUC into microcontroller as well

Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study

This work investigates the feasibility of using input-output data-driven control techniques for building control and their susceptibility to data-poisoning techniques. The analysis is performed on a digital replica of the KTH Livein Lab, a non-linear validated model representing one of the KTH Live-in Lab building testbeds. This work is motivated by recent trends showing a surge of interest in using data-based techniques to control cyber-physical systems. We also analyze the susceptibility of these controllers to data-poisoning methods, a particular type of machine learning threat geared towards finding imperceptible attacks that can undermine the performance of the system under consideration. We consider the Virtual Reference Feedback Tuning (VRFT), a popular data-driven control technique, and show its performance on the KTH Live-In Lab digital replica. We then demonstrate how poisoning attacks can be crafted and illustrate the impact of such attacks. Numerical experiments reveal the feasibility of using data-driven control methods for finding efficient control laws. However, a subtle change in the datasets can significantly deteriorate the performance of VRFT