OPTEC - K.U.Leuven Center of Excellence: Optimization in Engineering

"Model Predictive Control Algorithms for Applications with Millisecond Timescales"
Hans Joachim Ferreau

Promotors: Moritz Diehl, Joos Vandewalle

Abstract:

The last three decades have seen a rapidly increasing number of
applications where model predictive control (MPC) led to better control
performance than more traditional approaches. This thesis aims at
lowering the practical burden of applying fast MPC algorithms in the
real-world. To this aim, it contributes two software packages, which are
released as open-source code in order to stimulate their widespread use.
Both packages implement previously published methods but enrich them
with a number of new theoretical and algorithmic ideas.

The first part of this thesis focusses on efficiently solving quadratic
programs (QPs) as arising in linear MPC problems. To this end, it
reviews the author's previous work on developing an online active set
strategy to exploit the parametric nature of these QPs. This strategy is
extended with ideas for initialising the solution procedure and treating
QPs with semi-definite Hessian matrices. The software package qpOASES
implements the online active set strategy and its extensions together
with a number of tailored solution variants for special QP formulations.
It offers interfaces to third-party software like Matlab/Simulink and
has been successfully used in a number of academic real-world MPC
applications. Moreover, two industrial applications of qpOASES--dealing
with emission control of integral gas engines and feasibility management
for MPC in the process industry--are described. These industrial case
studies also led to further theoretical ideas, namely the use of MPC
with an asymmetric cost function and a novel method for handling
infeasible QPs based on the online active set strategy.

The second part addresses nonlinear MPC problems and presents the ACADO
Toolkit, a new software environment and algorithm collection for
automatic control and dynamic optimisation. It has been designed for
setting-up nonlinear optimal control and MPC problems in a user-friendly
way and solving them efficiently. In particular, the ACADO Toolkit
implements two algorithmic variants of the real-time iteration scheme: a
Gauss-Newton approach for nonlinear MPC formulations involving a
tracking objective function as well as an exact Hessian approach for
tackling time-optimal formulations. The underlying QP subproblems are
solved by means of the online active set strategy. The ACADO Toolkit
features an intuitive symbolic syntax for formulating MPC problems,
which offers a couple of advantageous possibilities. Most importantly,
it allows the user to automatically generate optimised, highly efficient
C code that is tailored to each respective MPC problem formulation.
Numerical results show that the exported code exhibits a promising
computational performance allowing application of nonlinear MPC to
non-trivial processes at kilohertz sampling rates.