Adaptive predictive control for pipelined multiprocessor image-based control systems considering workload variations

Sajid Mohamed, Nilay Saraf, Daniele Bernardini, Dip Goswami, A.A. (Twan) Basten, Alberto Bemporad

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
103 Downloads (Pure)


Image-based control (IBC) systems have a long sensing delay. The advent of multiprocessor platforms helps to cope with this delay by pipelining of the sensing task. However, existing pipelined IBC system designs are based on linear timeinvariant models and do not consider constraint satisfaction, system nonlinearities, workload variations and/or given interframe dependencies which are crucial for practical implementation. A pipelined IBC system implementation using a model predictive control (MPC) approach that can address these limitations making a step forward towards real-life adaptation is thus promising. We present an adaptive MPC formulation based on linear parameter-varying input/output models for a pipelined implementation of IBC systems. The proposed method maximizes quality-of-control by taking into account workload variations in the image processing for individual pipes in the sensing pipeline in order to exploit the latest measurements, besides explicitly considering given inter-frame dependencies, system nonlinearities and constraints on system variables. The practical benefits are highlighted through simulations using vision-based vehicle lateral control as a case study.

Original languageEnglish
Title of host publication59th IEEE Conference on Decision and Control (CDC 2020)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)978-1-7281-7447-1
Publication statusPublished - 11 Jan 2021
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual/Online, Virtual, Jeju Island, Korea, Republic of
Duration: 14 Dec 202018 Dec 2020
Conference number: 59


Conference59th IEEE Conference on Decision and Control, CDC 2020
Abbreviated titleCDC
Country/TerritoryKorea, Republic of
CityVirtual, Jeju Island
Internet address


FundersFunder number
EU’s H2020674875, H2020-ECSEL-2017-2-783162
European Union's Horizon 2020 - Research and Innovation Framework Programme783162


    Dive into the research topics of 'Adaptive predictive control for pipelined multiprocessor image-based control systems considering workload variations'. Together they form a unique fingerprint.

    Cite this