# A Revised Mehrotra Predictor-Corrector algorithm for Model Predictive Control

## TL;DR

This paper introduces a revised Mehrotra predictor-corrector interior-point algorithm tailored for quadratic programming in input-constrained model predictive control, significantly reducing computational time compared to standard solvers.

## Contribution

A novel extension of Mehrotra's algorithm specifically designed for quadratic programming in MPC, enhancing computational efficiency for real-time applications.

## Key findings

- The new algorithm is faster than MATLAB's solver.
- It effectively handles input constraints in MPC.
- Demonstrated improved computational performance in case studies.

## Abstract

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.