A multivariable motion sensor is presented that embeds into its onboard microcontroller a tailored algorithm, referred to here as the double-path (DP) algorithm, which estimates velocity in real time from position and acceleration signals simultaneously measured by the sensor itself. The multivariable motion sensor consists of a contactless magnetic linear position digital sensor and a triaxial digital accelerometer. The proposed algorithm estimates velocity by suitably mixing the integration of the acceleration and the linear fitting of the position, and it can operate under both trapezoidal and S-curve motion profiles. The velocity estimation accuracy has been assessed through simulations and experimental tests, which involved performance evaluation and a comparative analysis between the proposed algorithm and a Kalman filter (KF) both embedded into the sensor microcontroller. The experimental results are obtained by operating the sensor with a reference trapezoidal motion profile with a maximum velocity of 50 mm/s. The two root-mean-square estimation errors calculated for the sensor moving at constant acceleration and velocity are 1.32% and 0.58% of the maximum velocity, respectively.
A Multivariable Motion Sensor Embedding an Improved Velocity Estimation Algorithm
Mazzoli, Federico
;Ferrari, Vittorio
2025-01-01
Abstract
A multivariable motion sensor is presented that embeds into its onboard microcontroller a tailored algorithm, referred to here as the double-path (DP) algorithm, which estimates velocity in real time from position and acceleration signals simultaneously measured by the sensor itself. The multivariable motion sensor consists of a contactless magnetic linear position digital sensor and a triaxial digital accelerometer. The proposed algorithm estimates velocity by suitably mixing the integration of the acceleration and the linear fitting of the position, and it can operate under both trapezoidal and S-curve motion profiles. The velocity estimation accuracy has been assessed through simulations and experimental tests, which involved performance evaluation and a comparative analysis between the proposed algorithm and a Kalman filter (KF) both embedded into the sensor microcontroller. The experimental results are obtained by operating the sensor with a reference trapezoidal motion profile with a maximum velocity of 50 mm/s. The two root-mean-square estimation errors calculated for the sensor moving at constant acceleration and velocity are 1.32% and 0.58% of the maximum velocity, respectively.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
