1,274 research outputs found
Model-based control for high-tech mechatronic systems
Motion systems are mechanical systems with actuators with the primary function to position a load. The actuator can be either hydraulic, pneumatic, or electric. The feedback controller is typically designed using frequency domain techniques, in particular via manual loop-shaping. In addition to the feedback controller, a feedforward controller is often implemented with acceleration, velocity, and friction feedforward for the reference signal. This chapter provides an overview of a systematic control design procedure for motion systems that has proven its use in industrial motion control practise. A step-by-step procedure is presented that gradually extends single-input single-output (SISO) loop-shaping to the multi-input multi-output (MIMO) situation. This step-by-step procedure consists of interaction analysis, decoupling, independent SISO design, sequential SISO design, and finally, norm-based MIMO design. Extreme ultraviolet is a key technology for next-generation lithography
Model-Free Synthesis via Adversarial Reinforcement Learning
Motivated by the recent empirical success of policy-based reinforcement
learning (RL), there has been a research trend studying the performance of
policy-based RL methods on standard control benchmark problems. In this paper,
we examine the effectiveness of policy-based RL methods on an important robust
control problem, namely synthesis. We build a connection between robust
adversarial RL and synthesis, and develop a model-free version of the
well-known -iteration for solving state-feedback synthesis with
static -scaling. In the proposed algorithm, the step mimics the
classical central path algorithm via incorporating a recently-developed
double-loop adversarial RL method as a subroutine, and the step is based on
model-free finite difference approximation. Extensive numerical study is also
presented to demonstrate the utility of our proposed model-free algorithm. Our
study sheds new light on the connections between adversarial RL and robust
control.Comment: Accepted to ACC 202
Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design
Many iterative learning control algorithms rely on a model of the system. Although only approximate model knowledge is required, the model quality determines the convergence and performance properties of the learning control algorithm. The aim of this paper is to remove the need for a model for a class of multivariable ILC algorithms. The main idea is to replace the model by dedicated experiments on the system. Convergence criteria are developed and the results are illustrated with a simulation on a multi-axis flatbed printer
Benelux meeting on systems and control, 23rd, March 17-19, 2004, Helvoirt, The Netherlands
Book of abstract
Magnetic Actuators and Suspension for Space Vibration Control
The research on microgravity vibration isolation performed at the University of Virginia is summarized. This research on microgravity vibration isolation was focused in three areas: (1) the development of new actuators for use in microgravity isolation; (2) the design of controllers for multiple-degree-of-freedom active isolation; and (3) the construction of a single-degree-of-freedom test rig with umbilicals. Described are the design and testing of a large stroke linear actuator; the conceptual design and analysis of a redundant coarse-fine six-degree-of-freedom actuator; an investigation of the control issues of active microgravity isolation; a methodology for the design of multiple-degree-of-freedom isolation control systems using modern control theory; and the design and testing of a single-degree-of-freedom test rig with umbilicals
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