High performance implementation of MPC schemes for fast systems

Correa Córdova, Max Leo

Publicación:

High performance implementation of MPC schemes for fast systems

dc.contributor.author	Correa Córdova, Max Leo	es_PE
dc.date.accessioned	2024-05-30T23:13:38Z
dc.date.available	2024-05-30T23:13:38Z
dc.date.issued	2016-03
dc.description.abstract	In recent years, the number of applications of model predictive control (MPC) is rapidly increasing due to the better control performance that it provides in comparison to traditional control methods. However, the main limitation of MPC is the computational e ort required for the online solution of an optimization problem. This shortcoming restricts the use of MPC for real-time control of dynamic systems with high sampling rates. This thesis aims to overcome this limitation by implementing high-performance MPC solvers for real-time control of fast systems. Hence, one of the objectives of this work is to take the advantage of the particular mathematical structures that MPC schemes exhibit and use parallel computing to improve the computational e ciency. Firstly, this thesis focuses on implementing e cient parallel solvers for linear MPC (LMPC) problems, which are described by block-structured quadratic programming (QP) problems. Speci cally, three parallel solvers are implemented: a primal-dual interior-point method with Schur-complement decomposition, a quasi-Newton method for solving the dual problem, and the operator splitting method based on the alternating direction method of multipliers (ADMM). The implementation of all these solvers is based on C++. The software package Eigen is used to implement the linear algebra operations. The Open Message Passing Interface (Open MPI) library is used for the communication between processors. Four case-studies are presented to demonstrate the potential of the implementation. Hence, the implemented solvers have shown high performance for tackling large-scale LMPC problems by providing the solutions in computation times below milliseconds. Secondly, the thesis addresses the solution of nonlinear MPC (NMPC) problems, which are described by general optimal control problems (OCPs). More precisely, implementations are done for the combined multiple-shooting and collocation (CMSC) method using a parallelization scheme. The CMSC method transforms the OCP into a nonlinear optimization problem (NLP) and de nes a set of underlying sub-problems for computing the sensitivities and discretized state values within the NLP solver. These underlying sub-problems are decoupled on the variables and thus, are solved in parallel. For the implementation, the software package IPOPT is used to solve the resulting NLP problems. The parallel solution of the sub-problems is performed based on MPI and Eigen. The computational performance of the parallel CMSC solver is tested using case studies for both OCPs and NMPC showing very promising results. Finally, applications to autonomous navigation for the SUMMIT robot are presented. Specially, reference tracking and obstacle avoidance problems are addressed using an NMPC approach. Both simulation and experimental results are presented and compared to a previous work on the SUMMIT, showing a much better computational e ciency and control performance.
dc.description.sponsorship	Fondo Nacional de Desarrollo Científico y Tecnológico - Fondecyt
dc.identifier.uri	https://hdl.handle.net/20.500.12390/269
dc.language.iso	eng
dc.publisher	Technische Universitat ILMENAU
dc.rights	info:eu-repo/semantics/openAccess
dc.rights.uri	http://creativecommons.org/licenses/by-nc/4.0/
dc.subject	Programación lineal
dc.subject.ocde	https://purl.org/pe-repo/ocde/ford#2.02.00
dc.title	High performance implementation of MPC schemes for fast systems
dc.type	info:eu-repo/semantics/masterThesis
dspace.entity.type	Publication
oairecerif.author.affiliation	#PLACEHOLDER_PARENT_METADATA_VALUE#
thesis.degree.discipline	Ingeniería Eléctrica, Electrónica e informática
thesis.degree.grantor	Pontificia Universidad Católica del Perú. Escuela de Postgrado
thesis.degree.name	Magíster en Ingeniería de Control y Automatización