Project
Estimation Theory and Kalman Filtering Final Project
Spring 2026 • School Project
Applied estimation theory principles to design and simulate Kalman filters in MATLAB and Simulink, focusing on accurate state estimation and noise reduction for dynamic suspension system.
Skills Used
MATLAB Simulink
Details
- Developed a non-linear state estimation strategy for an Electromagnetic Mountain Bike Response (EMBR) suspension system, integrating a custom magnetorheological (MR) fluid damper featuring an internal electromagnetic coil and annular gap with a measurement system utilizing a microcontroller, MOSFET power regulation, dual accelerometers, and a string potentiometer
- Formulated continuous and discrete-time non-linear state-space representations of the damper dynamics and built simulations in MATLAB and Simulink to model real-world unmeasured disturbance forces, representing terrain inputs as a superposition of harmonic trail roughness, stochastic surface noise, and sharp Gaussian-pulse discrete obstacle impacts
- Designed and implemented Extended (EKF) and Unscented (UKF) Kalman Filters to infer unmeasured internal states like displacement, velocity, and magnetic field intensity, demonstrating that the UKF provided tighter error bounds by using the unscented transform to eliminate analytical linearization errors inherent to the highly non-linear fluid yield transition