Abstract:
In this report, continuous model predictive control using orthonormal
functions has been used to control landing trajectory of the aircraft.
The purpose of this thesis was to implement constraint control
on unmanned aerial vehicle and get improved results as compared
to PID technique.
For the simulations in MATLAB, longitudinal state space model
of reliance 0.46 trainer aircraft has been used because of its stable
natural dynamics. The model has two inputs four states and four
outputs. The model is linearized at speci c trim conditions.
Model predictive control(MPC) is used for designing controller required
in landing for unmanned aerial vehicle. MPC can be simulated
in real time and online optimization at every step can be performed.
State space model is used and selected outputs are tracked.
Landing control requires more than one state to be controlled at the
same time so MPC proved to be a good candidate for designing such
multi input multi output(MIMO)system.
In real life applications, constraints are always present in all dynamical
systems. MPC gives the possibility of adding constraints on
inputs, input derivatives, states and output of a system. This gives
MPC a priority when designing constraints systems.
Simulation done in MATLAB shows good performance of landing
phase of the
ight by simply tuning the parameters of the laguerre
functions.Also controller handled constraint very well while slightly
compromising on the output. But over all it showed improved performance
when compared to PID controllers.
