Publications

@article{ALLAMAA2022385,

title = {Sim2real for Autonomous Vehicle Control using Executable Digital Twin},

author = {Jean Pierre Allamaa and Panagiotis Patrinos and Herman Van Auweraer and Tong Duy Son},

url = {https://www.sciencedirect.com/science/article/pii/S2405896322023461},

doi = {https://doi.org/10.1016/j.ifacol.2022.10.314},

issn = {2405-8963},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {IFAC-PapersOnLine},

volume = {55},

number = {24},

pages = {385-391},

abstract = {In this work, we propose a sim2real method to transfer and adapt a nonlinear model predictive controller (NMPC) from simulation to the real target system based on executable digital twin (xDT). The xDT model is a high fidelity vehicle dynamics simulator, executable online in the control parameter randomization and learning process. The parameters are adapted to gradually improve control performance and deal with changing real-world environment. In particular, the performance metric is not required to be differentiable nor analytical with respect to the control parameters and system dynamics are not necessary linearized. Eventually, the proposed sim2real framework leverages altogether online high fidelity simulator, data-driven estimations, and simulation based optimization to transfer and adapt efficiently a controller developed in simulation environment to the real platform. Our experiment demonstrates that a high control performance is achieved without tedious time and labor consuming tuning.},

note = {10th IFAC Symposium on Advances in Automotive Control AAC 2022},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bonassi2022imc,

title = {Recurrent Neural Network-based Internal Model Control Design for Stable Nonlinear Systems},

author = {Fabio Bonassi and Riccardo Scattolini},

url = {https://doi.org/10.1016/j.ejcon.2022.100632

http://arxiv.org/abs/2108.04585},

doi = {10.1016/j.ejcon.2022.100632},

issn = {0947-3580},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {European Journal of Control},

volume = {65},

pages = {100632},

abstract = {Owing to their superior modeling capabilities, gated Recurrent Neural Networks (RNNs), such as Gated Recurrent Units (GRUs) and Long Short-Term Memory networks (LSTMs), have become popular tools for learning dynamical systems. This paper aims to discuss how these networks can be adopted for the synthesis of Internal Model Control (IMC) architectures. To this end, a first gated RNN is used to learn a model of the unknown input-output stable plant. Then, another gated RNN approximating the model inverse is trained. The proposed scheme is able to cope with the saturation of the control variables, and it can be deployed on low-power embedded controllers since it does not require any online computation. The approach is then tested on the Quadruple Tank benchmark system, resulting in satisfactory closed-loop performances.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hermansQPALMProximalAugmented2022,

title = {QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs},

author = {Ben Hermans and Andreas Themelis and Panagiotis Patrinos},

doi = {10.1007/s12532-022-00218-0},

issn = {1867-2957},

year  = {2022},

date = {2022-01-01},

journal = {Math. Prog. Comp.},

volume = {14},

number = {3},

pages = {497--541},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{themelisDouglasRachfordSplitting2022,

title = {Douglas–Rachford Splitting and ADMM for Nonconvex Optimization: Accelerated and Newton-type Linesearch Algorithms},

author = {Andreas Themelis and Lorenzo Stella and Panagiotis Patrinos},

doi = {10.1007/s10589-022-00366-y},

issn = {1573-2894},

year  = {2022},

date = {2022-01-01},

journal = {Comput Optim Appl},

volume = {82},

number = {2},

pages = {395--440},

keywords = {},

pubstate = {published},

tppubtype = {article}

}