SIMPLIFIED PROGRAMMING ALGORITHM FOR UNCONSTRAINED STATE SPACE MPC WITH STATE ESTIMATOR

Authors

  • M. T. Jimoh Department of Mechanical Engineering, Bayero University, Kano
  • A. Dan-Isa Department of Electrical Engineering, Bayero University, Kano

Keywords:

Model predictive control, Optimal Control law, Output prediction equation, State estimator, State space

Abstract

This paper develops a simplified simulation algorithm unconstrained state space MPC that
incorporates a state estimator for control of multivariable systems. A state space algorithm
based on the augmented states with five tuning parameters (prediction and control horizon,
output and input weights, and output filters), are presented. A block diagram showing the
simulation plan is presented, together with algorithms for calculating the constant matrices
of the MPC. Using Matlab and Matlab Simulink, the developed simulation plan is
implemented on continuous models of two plants, a SISO system and a MIMO system. The
implementation is simple and straight forward, presenting a very transparent state space
MPC alternative for use by researchers.

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Published

2020-09-02

Issue

Vol. 9 No. 2 (2020)

Section

Articles