{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/identification-of-ltv-dynamical-models-with","title":"Identification of LTV Dynamical Models with Smooth or Discontinuous Time Evolution by means of Convex Optimization","arxiv_id":"1802.09794","date":"2018-02-27","proceeding":null,"authors":["Fredrik Bagge Carlson","Anders Robertsson","Rolf Johansson"],"abstract":"We establish a connection between trend filtering and system identification\nwhich results in a family of new identification methods for linear,\ntime-varying (LTV) dynamical models based on convex optimization. We\ndemonstrate how the design of the cost function promotes a model with either a\ncontinuous change in dynamics over time, or causes discontinuous changes in\nmodel coefficients occurring at a finite (sparse) set of time instances. We\nfurther discuss the introduction of priors on the model parameters for\nsituations where excitation is insufficient for identification. The\nidentification problems are cast as convex optimization problems and are\napplicable to, e.g., ARX models and state-space models with time-varying\nparameters. We illustrate usage of the methods in simulations of jump-linear\nsystems, a nonlinear robot arm with non-smooth friction and stiff contacts as\nwell as in model-based, trajectory centric reinforcement learning on a smooth\nnonlinear system.","url_abs":"http://arxiv.org/abs/1802.09794v1","url_pdf":"http://arxiv.org/pdf/1802.09794v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"identification-of-ltv-dynamical-models-with","repo_url":"https://github.com/baggepinnen/LTVModels.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"friction","task_name":"Friction"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"state-space-models","task_name":"State Space Models"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}