  Input constrained Model predictive control (MPC) includes an optimization
problem which should iteratively be solved at each time-instance. The
well-known drawback of model predictive control is the computational cost of
the optimization problem. This results in restriction of the application of MPC
to systems with slow dynamics, e.g., process control systems and small-scale
problems. Therefore, implementing fast numerical optimization algorithms has
been a point of interest. Interior-point methods are proved to be appropriate
algorithms, from computational cost point-of-vie, to solve input-constrained
MPC. In this paper first a modified version of Mehrotra's predictor-corrector
algorithm, a famous interior-point algorithm, is extended for quadratic
programming problems and then is applied to the constrained model predictive
control problems. Results show that as expected, the new algorithm is faster
than Matlab solver's algorithm.
