Model predictive control applied to A 2-DOF helicopter.

This work presents an embedded model predictive control application to a 2-DOF Helicopter Process. The mathematical modeling of the plant is first presented along with an analysis of the linear model. Then, the incremental state-space representations used in the MPC formulation are derived. The MPC technique is then defined, along with how to rewrite the physical constraints into the problem formulation. After that, a discussion on the utilized Quadratic Programming solver is presented along with possible alternatives to it, showing some considerations on which matrices to calculate beforehand for an embedded application. Finally, system identification is performed and the experimental results are presented.

[2]  Alberto Bemporad,et al.  Model Predictive Control Tuning by Controller Matching , 2010, IEEE Transactions on Automatic Control.

[3]  B. Moor,et al.  Subspace identification for linear systems , 1996 .

[4]  Edward N. Hartley,et al.  Designing Output-Feedback Predictive Controllers by Reverse-Engineering Existing LTI Controllers , 2013, IEEE Transactions on Automatic Control.

[5]  Urban Forssell.  Closed-loop Identification : Methods, Theory, and Applications , 1999 .

[6]  Alberto Bemporad,et al.  A Quadratic Programming Algorithm Based on Nonnegative Least Squares With Applications to Embedded Model Predictive Control , 2016, IEEE Transactions on Automatic Control.

[7]  Ginalber Luiz de Oliveira Serra,et al.  Frontiers in Advanced Control Systems , 2012 .

[8]  Akbar Rahideh,et al.  Robust model predictive control of a twin rotor MIMO system , 2009, 2009 IEEE International Conference on Mechatronics.

[9]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[10]  Fatiha Nejjari,et al.  Reliable control of a twin rotor MIMO system using actuator health monitoring , 2014, 22nd Mediterranean Conference on Control and Automation.

[11]  Alberto Bemporad,et al.  Model predictive control applications for planetary rovers , 2012 .

[12]  Alberto Bemporad,et al.  Model predictive control for powered descent guidance and control , 2015, 2015 European Control Conference (ECC).

[13]  Wen-Hua Chen,et al.  Tracking control of small-scale helicopters using explicit nonlinear MPC augmented with disturbance observers , 2012 .

[14]  Charles R. Kelley,et al.  Predictive and Adaptive Processes in the Control of Air Force Systems. , 1970 .

[15]  Lennart Ljung,et al.  A projection method for closed-loop identification , 2000, IEEE Trans. Autom. Control..

[16]  Susy Thomas,et al.  Practically Implementable Model Predictive Controller for a Twin Rotor Multi-Input Multi-Output System , 2017 .

[17]  Akbar Rahideh.  Model Identification and Robust Nonlinear Model Predictive Control of a Twin Rotor MIMO System , 2009 .

[18]  Anthony Tzes,et al.  Switching model predictive attitude control for a quadrotor helicopter subject to atmospheric disturbances , 2011 .

[19]  Magnus Jansson,et al.  Subspace Identification and ARX Modeling , 2003 .

[20]  B. Wahlberg.  Model reductions of high-order estimated models : the asymptotic ML approach , 1989 .

[21]  Radu-Emil Precup,et al.  Model predictive control of a nonlinear laboratory twin rotor aero-dynamical system , 2017, 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI).

[22]  Francesco Borrelli,et al.  MPC-Based Approach to Active Steering for Autonomous Vehicle Systems , 2005 .

[23]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[24]  Stephen P. Boyd,et al.  Fast Model Predictive Control Using Online Optimization , 2010, IEEE Transactions on Control Systems Technology.

[25]  David A. Wismer,et al.  Introduction to nonlinear optimization : a problem solving approach , 1978 .

[26]  Bruno Augusto Angélico,et al.  Discrete LQG/LTR control augmented by integrators applied to a 2-DOF helicopter , 2016, 2016 IEEE Conference on Control Applications (CCA).

[27]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[28]  P. R. Villas-Boas,et al.  Modelagem e simulação , 2014 .