Title: Modified predictive control of continuum manipulators with learning-based model

Authors: Aida Parvaresh; S. Ali A. Moosavian

Addresses: Advanced Robotics & Automated Systems (ARAS) Laboratory, Center of Excellence in Robotics and Control, Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran ' Advanced Robotics & Automated Systems (ARAS) Laboratory, Center of Excellence in Robotics and Control, Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran

Abstract: Continuum manipulators are considered as systems with high intrinsic complexities, nonlinearities and uncertainties, which encounter several problems in accurate modelling and control purposes. However, model predictive control (MPC) strategy is highly dependent on the accuracy of the associated model used by the controller. Accordingly, any mismatch in parameters/structure of the model would result in degraded performance of MPC. To alleviate accuracy-related problems, data-driven adaptive model predictive control (DD-AMPC) is proposed. The proposed approach is based on deriving an explicit linearised model from data-driven identification, which is appropriate for model-based control purposes. In this approach, the continuum manipulator can be modelled and controlled without the requirement for comprehensive knowledge of the physical system. To reduce computational cost, receding horizon concept is modified by parameterising control trajectory. The proposed scheme is implemented and its functionality in point-to-point tracking, path following and presence of disturbances is assessed. Additionally, its superiority and effectiveness over conventional control schemes is evaluated and confirmed.

Keywords: adaptive model predictive control; AMPC; learning-based modelling; continuum manipulators; system identification; data-driven identification.

DOI: 10.1504/IJMIC.2022.124074

International Journal of Modelling, Identification and Control, 2022 Vol.40 No.1, pp.44 - 58

Received: 09 May 2021
Accepted: 12 Aug 2021
Published online: 12 Jul 2022 *

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