TR2018-123

Contract-based Predictive Control for Modularity in Hierarchical Systems


    •  Baethge, T., Kogel, M., Di Cairano, S., Findeisen, R., "Contract-based Predictive Control for Modularity in Hierarchical Systems", IFAC Conference on Nonlinear Model Predictive Control (NMPC), DOI: 10.1016/j.ifacol.2018.11.040, August 2018, vol. 51, pp. 499-504.
      BibTeX TR2018-123 PDF
      • @inproceedings{Baethge2018aug,
      • author = {Baethge, Tobias and Kogel, Markus and Di Cairano, Stefano and Findeisen, Rolf},
      • title = {Contract-based Predictive Control for Modularity in Hierarchical Systems},
      • booktitle = {IFAC Conference on Nonlinear Model Predictive Control (NMPC)},
      • year = 2018,
      • volume = 51,
      • number = 20,
      • pages = {499--504},
      • month = aug,
      • doi = {10.1016/j.ifacol.2018.11.040},
      • url = {https://www.merl.com/publications/TR2018-123}
      • }
  • MERL Contact:
  • Research Area:

    Control

Hierarchical control architectures pose challenges for control, as lower-level dynamics, such as from actuators, are often unknown or uncertain. If not considered correctly in the upper layers, requested and applied control signals will differ. Thus, the actual and the predicted plant behavior will not match, likely resulting in constraint violation and decreased control performance. We propose a model predictive control scheme in which the upper and lower levels-the controller and the actuator- agree on a "contract" that allows to bound the error due to neglected dynamics. The contract allows to guarantee a desired accuracy, enables modularity, and breaks complexity: Components can be exchanged, vendors do not need to provide in-depth insights into the components' working principle, and complexity is reduced, as upper-level controllers do not need full model information of the lower level- the actuators. The approach allows to consider uncertain actuator dynamics with flexible, varying sampling times. We prove repeated feasibility and input-to-state stability and illustrate the scheme in an example for a hierarchical controller/plant cascade.

 

  • Related News & Events

    •  NEWS   MERL Control and Dynamical Systems Group presented 8 papers at IFAC NMPC conference
      Date: August 19, 2018 - August 22, 2018
      Where: IFAC NMPC, Madison, WI
      MERL Contacts: Claus Danielson; Stefano Di Cairano; Rien Quirynen
      Research Area: Control
      Brief
      • The 6th IFAC Conference on Nonlinear Model Predictive Control (NMPC), http://www.nmpc2018.org/, is a highly focused conference that attracts experts in this area from around the world. Members of the Control and Dynamical Systems group presented 8 papers (out of the 149 at the conference!) Stefano Di Cairano delivered one of the 7 plenary lectures entitled "Contract-Based Design of Control Architectures by Model Predictive Control."
    •  
    •  NEWS   Stefano Di Cairano to give invited address at 3rd IAVSD Workshop on Dynamics of Road Vehicles: Connected and Automated Vehicles.
      Date: April 28, 2019
      Where: 3rd IAVSD Workshop on Dynamics of Road Vehicles: Connected and Automated Vehicles
      MERL Contact: Stefano Di Cairano
      Research Areas: Control, Optimization, Robotics
      Brief
      • Stefano Di Cairano, Distinguished Scientist and Senior Team Leader in the Control and Dynamical Systems Group, will give an invited talk entitled: "Modularity, integration and synergy in architectures for autonomous driving" that covers recent work in the lab concerning building a modular, robust control framework for autonomous driving.
    •  
    •  NEWS   MERL researcher Stefano Di Cairano taught short course for European Embedded Control Institute.
      Date: June 10, 2019 - June 14, 2019
      Where: Paris
      MERL Contact: Stefano Di Cairano
      Research Areas: Control, Dynamical Systems, Optimization
      Brief
      • MERL researcher Stefano Di Cairano and Prof. Ilya Kolmanovsky, Dept. Aerospace Engineering, the University of Michigan, were invited to teach a class on "Predictive and Optimization Based Control for Automotive and Aerospace Application" at the 2019 International Graduate School in Control, of the European Embedded Control Institute (EECI). Every year EECI invites world renown experts to teach 21-hours class modules, mostly for PhD students but also for professionals, on selected control subjects. Stefano and Ilya's class was attended by 30 "students" from both academia and industry, from all around the world, interested in automotive and aerospace control. The module described the fundamentals of modeling and control design in automotive and aerospace through lectures, real world examples and exercises, and placed particular emphasis on techniques such as MPC, reference governors, and optimal control.
    •