Position Estimation Using Magnetic Fields

Abstract

This thesis develops position estimation systems based on magnetic fields and addresses a number of challenges related to making such systems accurate and robust for real-world applications. The thesis first addresses one-dimensional position estimation using the measurement of piston position inside a cylinder as a benchmark application. The piston is equipped with a permanent magnet and one or more magnetic sensors are embedded on a compact circuit board located on top of the cylinder. Due to large distances between the moving piston and the stationary sensor, the magnetic field as a function of piston position is highly nonlinear. This magnetic field is modeled either analytically or emperically and a nonlinear estimation algorithm, namely the truncated interval unscented Kalman Filter (TIUKF), is utilized for real-time estimation of the position of the piston. Piston position estimation can be useful on hydraulic actuators, pneumatic actuators, IC engines, and a number of other cylinder piston products. The developed estimation algorithm is implemented experimentally on a microprocessor. A compact sensor board containing sensors, the microprocessor, and other components is developed. The developed position estimation system is first evaluated experimentally on pneumatic actuators. The estimation system performs well and an estimation accuracy better that 1% is achieved on pneumatic actuators with stroke lengths of 5 cm and 10 cm. Next an auto-calibration system is developed in order to enable the sensor board to estimate position accurately when installed on new cylinders. Small misalignments and offsets in location can occur on each installation. The new auto-calibration method allows the position estimation system to perform robustly and accurately by identification of new parameters on each installation. This auto-calibration is done without requiring any additional external reference position sensors. A significant challenge to magnetic field based position estimation comes from disturbances due to unexpected ferromagnetic objects coming close to the sensors. A new disturbance estimation method based on modeling the magnetic disturbance as a dipole with unknown location, magnitude, and orientation is developed. A TIUKF is used to estimate all the parameters of this unknown dipole, in addition to estimating piston position from nonlinear magnetic field models. Experimental data from a pneumatic actuator is used to verify the performance of the developed estimator. Experimental results show that the developed estimator is significantly superior to a linear magnetic field model based disturbance estimator. It can reliably estimate piston position and the unknown dipole parameters in the presence of a variety of unknown disturbances. Next the estimation system is implemented for a large hydraulics actuator used on construction machines. The ferromagnetic material of the hydraulic cylinder leads to significant hysteresis, since this material is magnetized and demagnetized repeatedly with the motion of the magnet. A method to model and compensate for the hysteresis in the system is developed. In particular, a modified Preisach model and associated estimation algorithm developed is shown to provide excellent performance. An accuracy better than 2\% is achieved on the large-stroke hydraulic cylinder in spite of significant hysteresis. Finally, the one-dimensional position estimation tools are extended in an attempt to enable 3D position estimation of a magnet. The objective is to estimate magnet position in real-time from a moving sensor board in the neighborhood of the magnet. Applications for this 3D position estimation system include a breast cancer surgery application in which a small magnet can be used to mark tumor location. The significant challenges in the 3D position estimation application are handled by using an accelerometer and gyroscope in addition to magnetic sensors for orientation estimation, by using a particle filter for the estimation task, and by using a neural network for modeling the functional relationships between magnetic field and 3D position and orientation. While the developed system provides reasonable experimental performance, further work with more sensitive magnetic sensors and a better reference 3D position sensor for modeling are needed.