Resource-aware motion control:feedforward, learning, and feedback

Controllers with new sampling schemes improve motion systems’ performanc

HIGH-BANDWIDTH IDENTIFICATION AND COMPENSATION OF HYSTERETIC DYNAMICS

Ph.DDOCTOR OF PHILOSOPH

Robust periodic disturbance compensation via multirate control

Master'sMASTER OF ENGINEERIN

Enhanced Speed and Current Control of PMSM Drives by Perfect Tracking Algorithms

Abstract-Speed and current closed loops control represent the heart of any advanced AC servo drive. These inner loops usually feature high-dynamic feedback control, with possible axes decoupling and a straight feedforward action of the backelectromotive force (back-EMF). More sophisticated techniques as single-rate or multi-rate control could be exploited for both speed and current closed loops, and their performances compared to that of the classic cascade feedback controllers. This represents the goal of the present work, focusing on permanent magnet synchronous motor (PMSM) drives

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems

Frequency-Domain Data-Driven Adaptive Iterative Learning Control Approach: With Application to Wafer Stage

The feedforward control is becoming increasingly important in ultra-precision stages. However, the conventional model-based methods cannot achieve expected performance in new-generation stages since it is hard to obtain the accurate plant model due to the complicated stage dynamical properties. To tackle this problem, this article develops a model-free data-driven adaptive iterative learning approach that is designed in the frequency-domain. Explicitly, the proposed method utilizes the frequency-response data to learn and update the output of the feedforward controller online, which has benefits that both the structure and parameters of the plant model are not required. An unbiased estimation method for the frequency response of the closed-loop system is proposed and proved through the theoretical analysis. Comparative experiments on a linear motor confirm the effectiveness and superiority of the proposed method, and show that it has the ability to avoid the performance deterioration caused by the model mismatch with the increasing iterative trials

Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control

Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part

Causal Tracking Control of a Non-Minimum Phase HIL Transmission Test System

The automotive industry has long relied on testing powertrain components in real vehicles, which causes the development process to be slow and expensive. Therefore, hardware in the loop (HIL) testing techniques are increasingly being adopted to develop electronic control units (ECU) for engine and other components of a vehicle. In this thesis, HIL testing system is developed to provide a laboratory testing environment for continuously variable transmissions (CVTs). Two induction motors were utilized to emulate a real engine and vehicle. The engine and vehicle models, running in real-time, provide reference torque and speed signals for input and output dynamometers, respectively. To design torque and speed tracking controllers, linear models of the motor and drive systems were firstly identified from the test results. Feedforward controllers were then designed according to the inverse dynamics of the identified models. Because of the existence of unstable zeros in the model, design effort was focused on the stability and causality of the inverse process. Digital preview filters were formulated to approximate the stable inverse of unstable zeros as part of the feedforward controller. Normally, future information of input trajectory is required when implementing the digital preview filters, which makes the feedforward controller non-causal. Since the engine and vehicle model require current information to calculate the next output and no future value can be provided in advance, the application of non-causal digital controllers was limited. A novel method is proposed here to apply non-causal digital controllers causally. Robustness of the controllers is also considered when the two motors are coupled and the gear ratio between them was changed. The proposed coupled control method was tested and verified experimentally by using a manual gearbox before recommending its use for a CVT testing. A multifrequency test signal as well as simulation results of a whole vehicle model were used as torque and speed demand signals in the experiments. A HIL testing case was also presented. Frequency and time domain results showed the effectiveness of the method under both testing procedures to fully compensate for the dynamics of both actuators.EThOS - Electronic Theses Online ServiceGBUnited Kingdo