3 research outputs found
Design of of model-based controllers via parametric programming
Imperial Users onl
Explicit/multi-parametric Moving Horizon Estimation and Model Predictive Control & their Application to Small Unmanned Aerial Vehicles
Moving horizon estimation (MHE) is a class of estimation methods in which
the system state and disturbance estimates are obtained by solving a constrained
optimization problem. The main advantage of MHE is that information
about the system can be explicitly considered in the form of constraints
and hence improve the estimates. In stochastic systems the estimation error
will inevitably be non-zero and the controller needs to explicitly account for
it to prevent constraint violations. In order for the controller to be robustified
against the estimation error, bounds on the error need to be known.
These bounds can be calculated if the dynamics that govern the estimation
error are known. This work presents those dynamics for the unconstrained
and the constrained case of the moving horizon estimator with a linear time-invariant
model, and also discusses how the bounds on the estimation error
can be obtained with set-theoretical methods. Those bounds are then used
for robust output-feedback model predictive control (MPC). The MHE and
the MPC are derived explicitly through multi-parametric programming. The
complete framework is demonstrated using simultaneous MHE and tubebased
MPC.
The possibility of solving MPC explicitly is very appealing for flight control
of small unmanned aerial vehicles (UAVs) because the behaviour of the
controller is known in advance and can be guaranteed. Flight control is
a challenging task that involves a multi-layer control structure where each
decision influences the other layers and the overall performance. This work
investigates the requirements on the different layers and their cross-effects.
A linear model of the UAV is derived such that it captures the wind which is
the most challenging disturbance for UAV flight. Particular focus is placed
on the design of a model predictive controller as the autopilot and on in-flight
wind estimation