Optimal Controller Design for Ultra-Precision Fast-Actuation Cutting Systems

Fast-actuation cutting systems are in high demand for machining of freeform optical parts. Design of such motion systems requires good balance between structural hardware and controller design. However, the controller tuning process is mostly based on human experience, and it is not feasible to predict positioning performance during the design stage. In this paper, a deterministic controller design approach is adopted to preclude the uncertainty associated with controller tuning, which results in a control law minimizing positioning errors based on plant and disturbance models. Then, the influences of mechanical parameters such as mass, damping, and stiffness are revealed within the closed-loop framework. The positioning error was reduced from 1.19 nm RMS to 0.68 nm RMS with the new controller. Under the measured disturbance conditions, the optimal bearing stiffness and damping coefficient are 1.1×105 N/m and 237.7 N/(m·s−1), respectively. We also found that greater moving inertia helps to reduce all disturbances at high frequencies, in agreement with the positioning experiments. A quantitative understanding of how plant structural parameters affect positioning stability is thus shown in this paper. This is helpful for the understanding of how to reduce error sources from the design point of view.

Relay Identification Using Shifting Method for PID Controller Tuning

The aim of this study was to present a relay shifting method for relay feedback identification of dynamical systems suitable for PID controller tuning. The proposed technique uses a biased relay to determine frequency response points from a single experiment without any assumptions about a model transfer function. The method is applicable for open-loop stable, unstable, and integration processes, even with a delay, and regardless of whether they are oscillating or non-oscillating. The core of this technique was formed by the so-called relay shifting filter. In this study, the method was applied to a parameter estimation of a second-order time-delayed (SOTD) model that can describe, with acceptable accuracy, the dynamics of most processes (even with a transport delay) near the operating point. Simultaneously, a parameter setting for the PID controller was derived based on the model parameters. The applicability of the proposed method was demonstrated on various simulated processes and tested on real laboratory apparatuses.