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Surface-constrained continuous-time extended Kalman filter: optimal estimation of a state of a constrained dynamic system-ball-rover rolling on a known surface

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thesis
posted on 24.05.2021, 11:55 by Maksims Demjanenko
The Surface-constrained Continuous-time Extended Kalman Filter (SCEKF), derived in thesis, contains a novel approach for handling surface or equality constraints, in which the surface-constrained CEKF is the projection of the unconstrained CEKF onto the set of state estimate rates that satisfy the constraints. The filter is used for optimal estimation of a state of a ball rolling on a known surface with uneven elevation. The state consists of surface contact point and geometrical center positions, attitude and angular velocity of the ball. The dynamics of the ball is affected by "unknown" to the filter disturbances, due to off-center point mass and variable wind. Thesis includes derivations of the SCEKF and the constraint dynamics of a rolling ball. The numerical computation results show that the surface-constrained filter can produce an accurate state estimate of the rolling ball and demonstrate that the estimate is significantly better than that produced by unconstrained filter.

History

Language

eng

Degree

Master of Applied Science

Program

Aerospace Engineering

Granting Institution

Ryerson University

LAC Thesis Type

Thesis