International Journal of Robust and Nonlinear Control Special Issue
Model Predictive Control (MPC) under Disturbances and Uncertainties: Safety, Stability and Learning
Conceptual simplicity and the potential for optimal performance in the presence of constraints have made Model Predictive Control (MPC) arguably the most important advanced control strategy, in terms of both theory and applications, to emerge over the past three decades. The ability to cope with disturbances and uncertainty is a central requirement of control system design. MPC schemes can usually not achieve satisfactory robustness and disturbance rejection performance unless one accounts directly for disturbances and system uncertainties in the controller design. Developing MPC schemes with adequate robustness and disturbance rejection capabilities are essential and challenging under realistic assumptions on the information available to the designer. MPC's specific issues include robust constraint satisfaction, computational tractability, and feasibility of online optimization problems under disturbance and uncertainty.
Recent advances in sensor technology and learning techniques enable, in many cases, to measure or estimate disturbance/uncertainty. This creates opportunities for new methods of dealing with disturbance and uncertainty within the MPC framework. For example, previewing near-future disturbances, estimating, or learning them enables new MPC schemes to minimize the influence of or take advantage of future known disturbances to improve performance. Integrating machine learning techniques in MPC allows estimating uncertainty or reducing uncertainty in information or modelling through online learning. MPC is also penetrating rapidly into autonomous systems and robotics for decision making or planning in the presence of significant uncertainty in information or operational environments.
This special issue aims to collect and advance the state-of-the-art of MPC under disturbance and uncertainty. It aims to compile new theoretical concepts, new design and analysis tools, and novel application cases.
Potential topics of interest include, but are not limited to the following:
Novel techniques in disturbance/uncertainty modelling and quantification
Constraint specification and abstraction (temporal logic or reachability analysis) within MPC
Disturbance estimation and rejection in MPC
Robust, stochastic, and adaptive MPC
MPC with disturbance/uncertainty preview
Methods for safety verification of MPC systems under uncertainty
New stability and robustness analysis tools for MPC under disturbance/uncertainty
Computational issues for MPC under disturbance/uncertainty
Hierarchical MPC schemes that allow tackling complex, uncertain problems
Learning-based MPC and data-driven approaches with a focus on disturbance/uncertainty
MPC for planning and decision making under uncertainty
Possible fields of applications are aerospace, mechatronics, robotics, automotive, chemical systems, information systems, biological systems, energy systems, healthcare…
A tentative schedule for this special issue is listed below:
First Submission Deadline: 31 January 2022
Notification of First Round Decision: 31 March 2022
Revised Paper Submission Deadline: 30 June 2022
Notification of Final Decision: 31 August 2022
Final Paper Submission Deadline: 30 September 2022
Tentative Publication Date: Early 2023
Submission Guideline
All the submitted papers will be subject to peer review in accordance with the standard review procedures of the International Journal of Robust and Nonlinear Control. Prospective authors are invited to submit manuscripts prepared as per the International Journal of Robust and Nonlinear Control guidelines, no later than 31 January 2022. Manuscripts should be submitted electronically online at: https://mc.manuscriptcentral.com/rnc-wiley
For inquiries, authors may contact one of the guest editors below.
Guest Editors
Wen-Hua Chen (Corresponding Editor)
Department of Aeronautical and Automotive Engineering
Loughborough University
Leicestershire LE11 3TU, UK
Email: W.Chen@lboro.ac.uk
Mark Cannon
Department of Engineering Sciences
University of Oxford
Oxford, OX1 3PJ, UK
Email: Mark.Cannon@eng.ox.ac.uk
Rolf Findeisen
Control and Cyber Physical Systems Laboratory
TU Darmstadt, Germany
Email: rolf.findeisen@iat.tu-darmstadt.de
Jun Yang
Department of Aeronautical and Automotive Engineering
Loughborough University
Leicestershire LE11 3TU, UK
Email: j.yang3@lboro.ac.uk
