Adaptive Stochastic Disturbance Accommodating Control
Abstract
This paper presents a Kalman filter based adaptive disturbance accommodating stochastic control scheme for linear uncertain systems to minimize the adverse effects of both model uncertainties and external disturbances. Instead of dealing with system uncertainties and external disturbances separately, the disturbance accommodating control scheme lumps the overall effects of these errors in a to-be-determined model-error vector, and then utilizes a Kalman filter in the feedback loop for simultaneously estimating the system states and the model-error vector from noisy measurements. Since the model-error dynamics is unknown, the process noise covariance associated with the model-error dynamics is used to empirically tune the Kalman filter to yield accurate estimates. A rigorous stochastic stability analysis reveals a lower bound requirement on the assumed system process noise covariance to ensure the stability of the controlled system when the nominal control action on the true plant is unstable. An adaptive law is synthesized for the selection of stabilizing system process noise covariance. Simulation results are presented where the proposed control scheme is implemented on a two degree-of-freedom helicopter.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2010
- Accession Number
- ADA548576
Entities
People
- Jemin George
- John L. Crassidis
- Puneet Singla
Organizations
- United States Army Research Laboratory