Sentence examples for mean square tracking from inspiring English sources

The control filter in the IMC controller was a digital FIR device designed to minimize the expectation of the mean square tracking error.

Stability analysis based on Lyapunov functions proves that all the error variables are bounded in probability; simultaneously, the mean square tracking error enters in finite-time in an arbitrarily selected small region around the origin wherein it remains thereafter.

It is proved that the design scheme ensures that all the error variables are bounded in probability while the mean square tracking error becomes semiglobally uniformly ultimately bounded SGUUB) in an arbitrarily small area around the origin.

Experiments on the constructed parallel kinematic machine show that the proposed two-loop controller can successfully eliminate the effects of the variant friction force and achieve high positioning accuracy with a mean square tracking error within 0.02 mm, which is superior to that of the H∞ controller (0.15 0.28 mm), the PD controller (0.11 0.22 mm) and the PD controller with DOB (0.09 0.19 mm).

It is shown that the closed-loop system is stable and the mean-square tracking errors can be made arbitrarily small by choosing design parameters.

The servo system has both a millimeter operating range and a root mean square (RMS) tracking error of about 80 nm for a circular trajectory.

The objective of the tracking problem is to find the filter parameters such that the tracking performance, defined as the least mean square of the tracking error, is optimized.

Based on the observer, an output feedback tracking controller is designed such that the mean square of the tracking error converges to an arbitrarily small neighborhood of zero by tuning design parameters.

Under some milder assumptions, an adaptive tracking controller is designed such that the mean square of the tracking error converges to an arbitrarily small neighborhood of zero by tuning design parameters.

Root mean square (RMS) trajectory tracking error was used to evaluate system performance, while system efficiency was assessed by measuring pneumatic fuel consumed during each task.

By constructing a new Lyapunov Krasovskii (L K) functional and modifying the adding a power integrator method, an output tracking controller is well designed such that all signals of the closed-loop system are bounded in probability and the mean square of the tracking error can be adjusted as small as possible.