| Summary: | Model Predictive Control (MPC) has become an established control technology due
to its powerful ability in constraints handling. The ability to solve MPC problems
online becomes critical for application that requires fast response time especially
embedded system that has limited computational resource. The key component of
model predictive control is the solving of quadratic programming problem (QPP).
Interior point method (IPM) and active set method (ASM) appear to be the most
efficient approaches for solving general QPPs. This project compares the
performances of the methods on 32-bit microcontroller in 4 aspects, i.e.
computational complexity, storage, convergence speed, and numerical error. The
findings show that, in general, ASM gives lower complexity and shorter computation
time. However, it uses larger memory space and does not produce converged
results for some of the QPPs that have no feasible point at (0,0,…,0). On the other
hand, IPM uses less memory space and able to produce converged results for all the
QPPs that have feasible point at (0,0,…,0).
In addition, formulation of a basic MPC problem for Cessna Citation 500 aircraft is
discussed and it is used to verify the algorithm of the methods that are implemented
on the STM32 microcontroller.
Lastly, the peripherals’ configuration of the STM32 microcontroller has been set up
and ready for implementation of a MPC controller to balance the pendulum of
apparatus PP-300.
|
|---|