Fault Tolerant Model Predictive Control of Quad-Rotor Helicopters with Actuator Fault Estimation

Model predictive control (MPC) at each time step minimizes a cost function subject to dynamical constraints to obtain a stabilizing control signal. Further, MPC is one of the few methodologies that can be used to design feedback control for nonlinear dynamical systems taking into consideration of actuator saturations. It can thus serve as a suitable fault tolerant control approach for quad-rotor helicopter governed by nonlinear dynamics. However, MPC needs a relatively accurate model of the post-failure system to calculate a stabilizing control signal. The problem becomes more critical where the system dynamics is described by a nonlinear model, because there exist few effective nonlinear parameter estimators with reasonable online computation time. To address this issue, for online actuator fault estimation, this paper investigates Moving Horizon Estimation (MHE) and Unscented Kalman Filter (UKF) as two methods for nonlinear parameter estimation. A framework is then formulated for integrating MHE/UKF based fault estimator with MPC to form an active fault tolerant control system for systems with nonlinear constrained dynamics. Performance and computation requirement of both algorithms are also investigated.