qLPV predictive control : a benchmark study on state space vs input-output approach

Enthalten in
IFAC-PapersOnLine 
Volume
52
Issue
28
Start Page
146
End Page
151
Citation
IFAC-PapersOnLine 52 (28) 146–151 (2019)
Contribution to Conference
3rd IFAC Workshop on Linear Parameter-Varying Systems, November 4-6, 2019, Eindhoven, The Netherlands 
Publisher DOI
10.1016/j.ifacol.2019.12.362
Scopus ID
2-s2.0-85080073948
Publisher
Elsevier
This paper presents a comparison and evaluation of two approaches to Nonlinear Model Predictive Control (NMPC) via quasi-LPV modeling, by means of a benchmark problem: control of a 4 degree-of-freedom Control Moment Gyroscope (CMG). The use of quasi-LPV modeling allows us to recast the nonlinear optimization problem arising in NMPC, as a repeated quadratic program which can be solved efficiently. The difference between the two presented schemes lies in the modeling paradigm chosen to express the dynamics of the system, namely state space (SS) or input-output (IO) frameworks. In both cases, quasi-LPV models are obtained by performing a velocity-based linearization, which results in an exact representation of the nonlinear dynamics and enables offset free control. Both schemes are successfully implemented on a laboratory CMG, and the experimental results are compared and discussed. Furthermore, advantages and disadvantages of each control scheme are examined.
DDC Class
620: Ingenieurwissenschaften