{"id":2383,"date":"2023-12-19T14:36:59","date_gmt":"2023-12-19T14:36:59","guid":{"rendered":"https:\/\/elo-x.eu\/?p=2383"},"modified":"2023-12-23T12:29:43","modified_gmt":"2023-12-23T12:29:43","slug":"real-time-methods-for-uncertainty-aware-predictive-control","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=2383","title":{"rendered":"Real-Time Methods for Uncertainty-Aware Predictive Control"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"2383\" class=\"elementor elementor-2383\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-90bc3ec elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"90bc3ec\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f2b556f\" data-id=\"f2b556f\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-86c9a5c elementor-widget elementor-widget-heading\" data-id=\"86c9a5c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Real-Time Methods for Uncertainty-Aware \nPredictive Control<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1085760 my-divider elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"1085760\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-beeb054 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"beeb054\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-38efd90\" data-id=\"38efd90\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a52d48f elementor-widget elementor-widget-text-editor\" data-id=\"a52d48f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"color: #352a87;\"><span style=\"font-size: 24px;\">Amon Lahr, ETH Z\u00fcrich<br \/><\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-31bbb35 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"31bbb35\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d843487\" data-id=\"d843487\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-391b193 elementor-widget elementor-widget-text-editor\" data-id=\"391b193\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Propagating uncertainties arising from inexact measurements and computations in the control feedback loop has the potential to improve the robustness and reliability of the control loop, as well as to distribute computational resources among subcomponents of the controller for maximum closed-loop performance. The goal of this thesis is to explore these potentials, developing real-time capable algorithms factoring in different sources of uncertainty. Some specific projects are outlined in the following.<br><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6c37975 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6c37975\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2c92c57\" data-id=\"2c92c57\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b7a88c2 my-divider elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"b7a88c2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f16671 elementor-widget elementor-widget-heading\" data-id=\"6f16671\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Zero-Order Optimization for Gaussian Process-based Model Predictive Control<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-80ad4c4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"80ad4c4\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-66cbae4\" data-id=\"66cbae4\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-82d499d elementor-widget elementor-widget-text-editor\" data-id=\"82d499d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><a href=\"https:\/\/doi.org\/10.1016\/j.ejcon.2023.100862\">Lahr, Zanelli, Carron, and Zeilinger, \u201cZero-order optimization for Gaussian process-based model predictive control,\u201d <i>European Journal of Control<\/i>, 2023.<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8722eb8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8722eb8\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-72a2899\" data-id=\"72a2899\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-957f7c1 elementor-widget elementor-widget-text-editor\" data-id=\"957f7c1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>By enabling constraint-aware online model adaptation, model predictive control using Gaussian process (GP) regression has exhibited impressive performance in real-world applications and received considerable attention in the learning-based control community. Yet, solving the resulting optimal control problem in real-time generally remains a major challenge, due to the increased number of augmented states in the optimization problem, as well as computationally expensive evaluations of the posterior mean, covariance and their respective derivatives. To tackle these challenges, we employ a tailored Jacobian approximation in a sequential quadratic programming approach (<a href=\"https:\/\/doi.org\/10.1016\/j.ifacol.2021.08.523\">zoRO-21<\/a>) and combine it with a parallelizable GP inference and automatic differentiation framework. The proposed algorithm computes suboptimal, yet feasible, solutions at drastically reduced computation times and exhibits favorable local convergence properties. Numerical experiments verify the scaling properties and investigate the runtime distribution across different parts of the algorithm.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-73c1594\" data-id=\"73c1594\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-705973c elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"705973c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"525\" height=\"370\" src=\"https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Zero-Order-GP-MPC-Graphical-Abstract-1024x721.png\" class=\"attachment-large size-large wp-image-2386\" alt=\"\" srcset=\"https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Zero-Order-GP-MPC-Graphical-Abstract-1024x721.png 1024w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Zero-Order-GP-MPC-Graphical-Abstract-300x211.png 300w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Zero-Order-GP-MPC-Graphical-Abstract-768x541.png 768w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Zero-Order-GP-MPC-Graphical-Abstract.png 1342w\" sizes=\"100vw\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3d736fa elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3d736fa\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3aee274\" data-id=\"3aee274\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0393100 my-divider elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"0393100\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2d893c3 elementor-widget elementor-widget-heading\" data-id=\"2d893c3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Efficient Zero-Order Robust Optimization for Real-Time Model Predictive Control with acados<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3d870f3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3d870f3\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2ecb947\" data-id=\"2ecb947\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d38de93 elementor-widget elementor-widget-text-editor\" data-id=\"d38de93\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div style=\"line-height: 1.35;\"><div style=\"clear: left;\"><a href=\"https:\/\/doi.org\/10.48550\/arXiv.2311.04557\">Frey, Gao, Messerer, Lahr, Zeilinger, and Diehl, \u201cEfficient zero-order robust optimization for real-time model predictive control with acados.\u201d arXiv, 2023.<\/a><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fff9796 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fff9796\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-24920a1\" data-id=\"24920a1\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5a37d73 elementor-widget elementor-widget-text-editor\" data-id=\"5a37d73\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>This work tackles the high-performance implementation of the zero-order optimization method for robust and stochastic model predictive control in the open-source real-time optimization framework acados. By utilizing BLASFEO for the linear algebra operations involved in the covariance propagation, as well as optimizing the execution of the code for its use in the Real-Time Iteration, the novel implementation (zoRO-24) drastically reduces the computational overhead of the original implementation (<a href=\"https:\/\/doi.org\/10.1016\/j.ifacol.2021.08.523\">zoRO-21<\/a>).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-643dfc9\" data-id=\"643dfc9\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ac5f642 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"ac5f642\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"525\" height=\"162\" src=\"https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Fast-zoRO-Speedups-1024x316.png\" class=\"attachment-large size-large wp-image-2415\" alt=\"\" srcset=\"https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Fast-zoRO-Speedups-1024x316.png 1024w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Fast-zoRO-Speedups-300x93.png 300w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Fast-zoRO-Speedups-768x237.png 768w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Fast-zoRO-Speedups-1536x474.png 1536w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Fast-zoRO-Speedups-2048x632.png 2048w\" sizes=\"100vw\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-140e3a1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"140e3a1\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-87fcd68\" data-id=\"87fcd68\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7d0dc38 my-divider elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"7d0dc38\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b1a323b elementor-widget elementor-widget-heading\" data-id=\"b1a323b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Real-time Gaussian Process-based Model Predictive Control for Autonomous Racing<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-089405f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"089405f\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4771765\" data-id=\"4771765\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3ef1342 elementor-widget elementor-widget-text-editor\" data-id=\"3ef1342\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Ongoing semester project by Joshua N\u00e4f, under the supervision of AL and Andrea Carron.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6003210 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6003210\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-b97f701\" data-id=\"b97f701\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e361371 elementor-widget elementor-widget-text-editor\" data-id=\"e361371\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>By extending the zoRO-24 method to incorporate state-dependent uncertainties, the zero-order GP-MPC method can benefit from the high-performance zoRO-24 implementation. The resulting method, which is currently under development, has been applied successfully to an autonomous miniature racing platform, allowing for increased GP model complexity compared to state-of-the-art implementations. <\/p><p>\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-57a39b9\" data-id=\"57a39b9\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4651714 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"4651714\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"525\" height=\"394\" src=\"https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/crs_sim_zoro-1024x769.png\" class=\"attachment-large size-large wp-image-2391\" alt=\"\" srcset=\"https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/crs_sim_zoro-1024x769.png 1024w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/crs_sim_zoro-300x225.png 300w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/crs_sim_zoro-768x576.png 768w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/crs_sim_zoro.png 1443w\" sizes=\"100vw\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-b52f852\" data-id=\"b52f852\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c902962 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"c902962\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"525\" height=\"331\" src=\"https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Screenshot-from-2023-12-17-12-43-15-1024x645.png\" class=\"attachment-large size-large wp-image-2392\" alt=\"\" srcset=\"https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Screenshot-from-2023-12-17-12-43-15-1024x645.png 1024w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Screenshot-from-2023-12-17-12-43-15-300x189.png 300w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Screenshot-from-2023-12-17-12-43-15-768x484.png 768w, https:\/\/elo-x.eu\/wp-content\/uploads\/2023\/12\/Screenshot-from-2023-12-17-12-43-15.png 1115w\" sizes=\"100vw\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Real-Time Methods for Uncertainty-Aware Predictive Control Amon Lahr, ETH Z\u00fcrich Propagating uncertainties arising from inexact measurements and computations in the control feedback loop has the potential to improve the robustness and reliability of the control loop, as well as to distribute computational resources among subcomponents of the controller for maximum closed-loop performance. The goal of &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/elo-x.eu\/?p=2383\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Real-Time Methods for Uncertainty-Aware Predictive Control&#8221;<\/span><\/a><\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[22],"tags":[],"class_list":["post-2383","post","type-post","status-publish","format-standard","hentry","category-projects"],"_links":{"self":[{"href":"https:\/\/elo-x.eu\/index.php?rest_route=\/wp\/v2\/posts\/2383","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/elo-x.eu\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/elo-x.eu\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/elo-x.eu\/index.php?rest_route=\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/elo-x.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2383"}],"version-history":[{"count":43,"href":"https:\/\/elo-x.eu\/index.php?rest_route=\/wp\/v2\/posts\/2383\/revisions"}],"predecessor-version":[{"id":2447,"href":"https:\/\/elo-x.eu\/index.php?rest_route=\/wp\/v2\/posts\/2383\/revisions\/2447"}],"wp:attachment":[{"href":"https:\/\/elo-x.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2383"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/elo-x.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2383"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/elo-x.eu\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2383"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}