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Advancements in machining technology demand higher speeds and precision, necessitating improved control systems in equipment like CNC machine tools. Due to lead errors, structural vibrations, and thermal deformation, commercial CNC controllers commonly use rotary encoders in the motor side to close the position loop, aiming to prevent insufficient stability and premature wear and damage of components. This paper introduces a multivariable iterative learning control (MILC) method tailored for flexible feed drive systems, focusing on enhancing dynamic positioning accuracy. The MILC employs error data from both the motor and table sides, enhancing precision by injecting compensation commands into both the reference trajectory and control command through a norm-optimization process. This method effectively mitigates conflicts between feedback control (FBC) and traditional iterative learning control (ILC) in flexible structures, achieving smaller tracking errors in the table side. The performance and efficacy of the MILC system are experimentally validated on an industrial biaxial CNC machine tool, demonstrating its potential for precision control in modern machining equipment. [ABSTRACT FROM AUTHOR]

This report documents the research results of Task Order 4205 (TO4205), Fault Tolerant Lateral Control for Transient Buses and Trucks performed during 2000-2003. In this task order, we studied the procedures for designing real-time lateral control systems for automated vehicles that are not sensitive to failures of the two key components: a set of magnetometers at the front bumper and another set at the rear bumpers. This problem is important because failures related to either front or rear magnetometers may have immediate effect on the stability of the closed loop control system.

Active noise cancellation in ducts using multiple feedforward sensors have been investigated by many researchers. When adaptive algorithms were applied, usually a lumped parameter model was assumed and the model includes the dynamics of the duct, sensors and actuators (e.g., microphone and loudspeaker). The distributed nature of the duct usually leads to a large parameter space and is difficult to implement direct adaptive control. This brief presents a control architecture which separates the duct's dynamics from those of sensors and actuators. An adaptive control algorithm is proposed to cope with the unknown dynamics of sensors and actuators. Experiments were conducted to show the effectiveness of the control law. [ABSTRACT FROM AUTHOR]