Velocity and Acceleration Estimation for Optical Incremental Encoders

Abstract

Optical incremental encoders are extensively used for position measurements in motion systems. The position measurements suffer from quantization errors. Velocity and acceleration estimations obtained by numerical differentiation largely amplify the quantization errors. In this paper, the time stamping concept is used to obtain more accurate position, velocity and acceleration estimations. Time stamping makes use of stored events, consisting of the encoder counts and their time instants, captured at a high resolution clock. Encoder imperfections and the limited resolution of the capturing rate of the encoder events result in errors in the estimations. In this paper, we propose a method to extend the observation interval of the stored encoder events using a skip operation. Experiments on a motion system show that the velocity estimation is improved by 54% and the acceleration estimation by 92%.

Model based control strategies for a class of nonlinear mechanical sub-systems

“…However, these oscillations will be far more expressed in the speed ω [22]. If the integrated squared error (ISE) ofω m − ω wish increases, with ω wish the controller output, thenλ k will increase to, rendering the system to be slower, as K decreases.…”

Multicriteria fuzzy-polynomial observer design for a 3DoF nonlinear electromechanical platform

“…The position estimates are precise, but, as there is no direct access to the speed state variables, a noncausal zero-phase filter (filtfilt in MATLAB with 0.5/(1 − 0.5z −1 ) in forward and reverse time, plus further noncausal numerical differentiation (z − z −1 )/(2T s ) in the speed coordinates) has been used to compute [18] (off-line) a target "actual" value of speeds from clean position measurements (data in Figure 4). The resulting data have been assumed to be the "true" speeds to which observers should converge (note that observers are under the constraints of dealing with noise and being causal).…”

Smooth Output Reconstruction for Linear Systems with Quantized Measurements

“…There is another line of research on reconstructing the real system output from the noise-corrupted output by curve fitting methods. For instance, polynomial filtering approaches have been proposed to recover non-uniformly sampled signals [14] and position signals obtained from incremental encoders [15]. The methods can work well if the output signal can be locally approximated by low-degree polynomials.…”