Graduation Year

2008

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Electrical Engineering

Major Professor

Wilfrido A. Moreno, Ph.D.

Co-Major Professor

Kimon P. Valavanis, Ph.D.

Committee Member

James T. Leffew, Ph.D.

Committee Member

Paris Wiley, Ph.D.

Committee Member

Fernando Falquez, Ph.D.

Keywords

UAVs, Nonlinear estimation, Receding horizon control, VTOL, Parameter estimation

Abstract

A novel application is presented for a fault-tolerant adaptive model predictive control system for small-scale helicopters. The use of a joint Extended Kalman Filter, (EKF), for the estimation of the states and parameters of the UAV, provided the advantage of implementation simplicity and accuracy. A linear model of a small-scale helicopter was utilized for testing the proposed control system. The results, obtained through the simulation of different fault scenarios, demonstrated that the proposed scheme was able to handle different types of actuator and system faults effectively. The types of faults considered were represented in the parameters of the mathematical representation of the helicopter.

Benefits provided by the proposed fault-tolerant adaptive model predictive control systems include:

  • The use of the joint Kalman filter provided a straightforward approach to detect and handle different types of actuator and system faults, which were represented as changes of the system and input matrices.
  • The built-in adaptability provided for the handling of slow time-varying faults, which are difficult to detect using the standard residual approach.
  • The successful inclusion of fault tolerance yielded a significant increase in the reliability of the UAV under study.

A byproduct of this research is an original comparison between the EKF and the Unscented Kalman Filter, (UKF). This comparison attempted to settle the conflicting claims found in the research literature concerning the performance improvements provided by the UKF. The results of the comparison indicated that the performance of the filters depends on the approximation used for the nonlinear model of the system. Noise sensitivity was found to be higher for the UKF, than the EKF. An advantage of the UKF appears to be a slightly faster convergence.

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