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Annual Review of Control, Robotics, and Autonomous Systems

Volume 1, 2018

Data-Driven Predictive Control for Autonomous Systems

Abstract

In autonomous systems, the ability to make forecasts and cope with uncertain predictions is synonymous with intelligence. Model predictive control (MPC) is an established control methodology that systematically uses forecasts to compute real-time optimal control decisions. In MPC, at each time step an optimization problem is solved over a moving horizon. The objective is to find a control policy that minimizes a predicted performance index while satisfying operating constraints. Uncertainty in MPC is handled by optimizing over multiple uncertain forecasts. In this case, performance index and operating constraints take the form of functions defined over a probability space, and the resulting technique is called stochastic MPC. Our research over the past 10 years has focused on predictive control design methods that systematically handle uncertain forecasts in autonomous and semiautonomous systems. In the first part of this article, we present an overview of the approach we use, its main advantages, and its challenges. In the second part, we present our most recent results on data-driven predictive control. We show how to use data to efficiently formulate stochastic MPC problems and autonomously improve performance in repetitive tasks. The proposed framework is able to handle a large set of predicted scenarios in real time and learn from historical data.

Keyword(s): data-driven control, learning, model predictive control, MPC, stochastic predictive control
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/content/journals/10.1146/annurev-control-060117-105215
2018-05-28
2024-04-27
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Literature Cited

  1. 1.  Murray RM 2003. Control in an Information Rich World: Report of the Panel on Future Directions in Control, Dynamics, and Systems Philadelphia: SIAM
  2. 2.  Manchester IR, Mettin U, Iida F, Tedrake R 2011. Stable dynamic walking over uneven terrain. Int. J. Robot. Res. 30:265–79
     [Google Scholar]
  3. 3.  Kuindersma S, Deits R, Fallon M, Valenzuela A, Dai H et al. 2016. Optimization-based locomotion planning, estimation, and control design for the Atlas humanoid robot. Auton. Robots 40:429–55
     [Google Scholar]
  4. 4.  Metz C 2016. How Google's AI viewed the move no human could understand. Wired Mar. 14. https://www.wired.com/2016/03/googles-ai-viewed-move-no-human-understand
  5. 5.  Borrelli F, Bemporad A, Morari M 2017. Predictive Control for Linear and Hybrid Systems Cambridge, UK: Cambridge Univ. Press
  6. 6.  Zhang X, Grammatico S, Schildbach G, Goulart P, Lygeros J 2015. On the sample size of random convex programs with structured dependence on the uncertainty. Automatica 60:182–88
     [Google Scholar]
  7. 7.  Zhang X 2016. Robust and stochastic control of uncertain systems: from scenario optimization to adjustable uncertainty sets PhD Thesis, ETH Zurich
  8. 8.  Rosolia U, Borrelli F 2018. Learning model predictive control for iterative tasks. A data-driven control framework. IEEE Trans. Autom. Control. In press. https://doi.org/10.1109/TAC.2017.2753460
    [Crossref]
  9. 9.  Rosolia U, Borrelli F 2017. Learning model predictive control for iterative tasks: a computationally efficient approach for linear system. IFAC-PapersOnLine 50:3142–47
     [Google Scholar]
  10. 10.  Garcia CE, Prett DM, Morari M 1989. Model predictive control: theory and practice—a survey. Automatica 25:335–48
     [Google Scholar]
  11. 11.  Morari M, Lee JH 1999. Model predictive control: past, present and future. Comput. Chem. Eng. 23:667–82
     [Google Scholar]
  12. 12.  Mayne DQ, Rawlings JB, Rao CV, Scokaert PO 2000. Constrained model predictive control: stability and optimality. Automatica 36:789–814
     [Google Scholar]
  13. 13.  Rawlings J, Mayne D 2009. Model Predictive Control: Theory and Design Madison, WI: Nob Hill
  14. 14.  Camacho EF, Bordons C 2013. Model Predictive Control Berlin: Springer
  15. 15.  Broadhurst A, Baker S, Kanade T 2005. Monte Carlo road safety reasoning. 2005 IEEE Intelligent Vehicles Symposium319–24 New York: IEEE
     [Google Scholar]
  16. 16.  Eidehall A, Petersson L 2008. Statistical threat assessment for general road scenes using Monte Carlo sampling. IEEE Trans. Intell. Transp. Syst. 9:137–47
     [Google Scholar]
  17. 17.  McCall J, Trivedi M 2007. Driver behavior and situation aware brake assistance for intelligent vehicles. Proc. IEEE 95:374–87
     [Google Scholar]
  18. 18.  Bestick A, Bajcsy R, Dragan A 2016. Implicitly assisting humans to choose good grasps in robot to human handovers. 2016 International Symposium on Experimental Robotics341–54 Cham, Switz.: Springer
     [Google Scholar]
  19. 19.  Dragan AD 2017. Robot planning with mathematical models of human state and action. arXiv:170504226D
  20. 20.  Lam C, Yang A, Driggs-Campbell K, Bajcsy R, Sastry S 2015. Improving human-in-the-loop decision making in multi-mode driver assistance systems using hidden mode stochastic hybrid systems. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems5776–83 New York: IEEE
     [Google Scholar]
  21. 21.  Liu C, Hamrick J, Fisac J, Dragan A, Hedrick K et al. 2016. Goal inference improves objective and perceived performance in human-robot collaboration. AAMAS '16: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems940–48 Richland, SC: Int. Found. Auton. Agents Multiagent Syst.
     [Google Scholar]
  22. 22.  Kehoe B, Patil S, Abbeel P, Goldberg K 2015. A survey of research on cloud robotics and automation. IEEE Trans. Autom. Sci. Eng. 12:398–409
     [Google Scholar]
  23. 23.  Löfberg J 2004. YALMIP: a toolbox for modeling and optimization in MATLAB. 2004 IEEE International Conference on Robotics and Automation284–89 New York: IEEE
     [Google Scholar]
  24. 24.  Grant M, Boyd S 2016. CVX: Matlab software for disciplined convex programming http://cvxr.com/cvx
  25. 25.  Zhang X, Liniger A, Borrelli F 2017. Optimization-based collision avoidance. arXiv:1711.03449
  26. 26.  Pirnay H, López-Negrete R, Biegler L 2012. Optimal sensitivity based on IPOPT. Math. Program. Comput. 4:307–31
     [Google Scholar]
  27. 27.  Mueller MW, Hehn M, D'Andrea R 2013. A computationally efficient algorithm for state-to-state quadrocopter trajectory generation and feasibility verification. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems3480–86 New York: IEEE
     [Google Scholar]
  28. 28.  Mueller MW, Hehn M, D'Andrea R 2015. A computationally efficient motion primitive for quadrocopter trajectory generation. IEEE Trans. Robot. 31:1294–310
     [Google Scholar]
  29. 29.  LaValle S 1998. Rapidly-exploring random trees: a new tool for path planning Tech. Rep., Iowa State Univ., Ames
  30. 30.  Otte M, Frazzoli E 2015. RRTX: real-time motion planning/replanning for environments with unpredictable obstacles. Algorithmic Foundations of Robotics XI HL Akin, NM Amato, V Isler, AF van der Stappen 461–78 Cham, Switz.: Springer
     [Google Scholar]
  31. 31.  Optim Gurobi 2017. Gurobi Optimizer reference manual https://www.gurobi.com/documentation/7.5/refman
  32. 32.  MOSEK ApS 2017. The MOSEK optimization toolbox for MATLAB manual. Version 7.1 (revision 63) http://docs.mosek.com/7.1/toolbox/index.html
    [Google Scholar]
  33. 33.  Goulart P, Kerrigan E, Maciejowski J 2006. Optimization over state feedback policies for robust control with constraints. Automatica 42:523–33
     [Google Scholar]
  34. 34.  Bemporad A 1998. Reducing conservativeness in predictive control of constrained systems with disturbances. Proceedings of the 37th IEEE Conference on Decision and Control1384–89 New York: IEEE
     [Google Scholar]
  35. 35.  Chisci L, Rossiter J, Zappa G 2001. Systems with persistent disturbances: predictive control with restricted constraints. Automatica 37:1019–28
     [Google Scholar]
  36. 36.  Richards A, How J 2006. Robust stable model predictive control with constraint tightening. 2006 American Control Conference New York: IEEE https://doi.org/10.1109/ACC.2006.1656440
     [Crossref] [Google Scholar]
  37. 37.  Mayne D, Seron M, Raković S 2005. Robust model predictive control of constrained linear systems with bounded disturbances. Automatica 41:219–24
     [Google Scholar]
  38. 38.  Lofberg J 2003. Approximations of closed-loop minimax MPC. 42nd IEEE International Conference on Decision and Control1438–42 New York: IEEE
     [Google Scholar]
  39. 39.  Zhang X, Kamgarpour M, Georghiou A, Goulart P, Lygeros J 2017. Robust optimal control with adjustable uncertainty sets. Automatica 75:249–59
     [Google Scholar]
  40. 40.  Batina I, Stoorvogel AA, Weiland S 2002. Optimal control of linear, stochastic systems with state and input constraints. Proceedings of the 41st IEEE Conference on Decision and Control1564–69 New York: IEEE
     [Google Scholar]
  41. 41.  Boyd S, Vandenberghe L 2004. Convex Optimization Cambridge, UK: Cambridge Univ. Press
  42. 42.  Kouvaritakis B, Cannon M, Rakovic S, Cheng Q 2010. Explicit use of probabilistic distributions in linear predictive control. Automatica 46:1719–24
     [Google Scholar]
  43. 43.  Cannon M, Cheng Q, Kouvaritakis B, Raković S 2012. Stochastic tube MPC with state estimation. Automatica 48:536–41
     [Google Scholar]
  44. 44.  Farina M, Giulioni L, Magni L, Scattolini R 2013. A probabilistic approach to model predictive control. 52nd IEEE Conference on Decision and Control7734–39 New York: IEEE
     [Google Scholar]
  45. 45.  Fagiano L, Khammash M 2012. Nonlinear stochastic model predictive control via regularized polynomial chaos expansions. 2012 IEEE 51st Conference on Decision and Control142–47 New York: IEEE
     [Google Scholar]
  46. 46.  Mesbah A, Streif S, Findeisen R, Braatz R 2014. Stochastic nonlinear model predictive control with probabilistic constraints. 2014 American Control Conference2413–19 New York: IEEE
     [Google Scholar]
  47. 47.  Paulson J, Mesbah A, Streif S, Findeisen R, Braatz R 2014. Fast stochastic model predictive control of high-dimensional systems. 53rd IEEE Conference on Decision and Control2802–9 New York: IEEE
     [Google Scholar]
  48. 48.  Lucia S, Zometa P, Kogel M, Findeisen R 2015. Efficient stochastic model predictive control based on polynomial chaos expansions for embedded applications. 2015 54th IEEE Conference on Decision and Control3006–12 New York: IEEE
     [Google Scholar]
  49. 49.  Calafiore G, Fagiano L 2013. Stochastic model predictive control of LPV systems via scenario optimization. Automatica 49:1861–66
     [Google Scholar]
  50. 50.  Schildbach G, Fagiano L, Frei C, Morari M 2014. The scenario approach for Stochastic Model Predictive Control with bounds on closed-loop constraint violations. Automatica 50:3009–18
     [Google Scholar]
  51. 51.  Grammatico S, Zhang X, Margellos K, Goulart P, Lygeros J 2016. A scenario approach for nonconvex control design. IEEE Trans. Autom. Control 61:334–45
     [Google Scholar]
  52. 52.  Lorenzen M, Dabbene F, Tempo R, Allgower F 2017. Stochastic MPC with offline uncertainty sampling. Automatica 81:176–83
     [Google Scholar]
  53. 53.  Vitus MP, Zhou Z, Tomlin CJ 2016. Stochastic control with uncertain parameters via chance constrained control. IEEE Trans. Autom. Control 61:2892–905
     [Google Scholar]
  54. 54.  Margellos K, Goulart P, Lygeros J 2014. On the road between robust optimization and the scenario approach for chance constrained optimization problems. IEEE Trans. Autom. Control 59:2258–63
     [Google Scholar]
  55. 55.  Zhang X, Georghiou A, Lygeros J 2015. Convex approximation of chance-constrained MPC through piecewise affine policies using randomized and robust optimization. 2015 54th IEEE Conference on Decision and Control3038–43 New York: IEEE
     [Google Scholar]
  56. 56.  Carrau J, Liniger A, Zhang X, Lygeros J 2016. Efficient implementation of randomized MPC for miniature race cars. 2016 European Control Conference957–62 New York: IEEE
     [Google Scholar]
  57. 57.  Liniger A, Zhang X, Aeschbach P, Georghiou A, Lygeros J 2017. Racing miniature cars: enhancing performance using stochastic MPC and disturbance feedback. 2017 American Control Conference5642–47 New York: IEEE
     [Google Scholar]
  58. 58.  Calafiore G, Campi MC 2005. Uncertain convex programs: randomized solutions and confidence levels. Math. Program. 102:25–46
     [Google Scholar]
  59. 59.  Calafiore G, Campi M 2006. The scenario approach to robust control design. IEEE Trans. Autom. Control 51:742–53
     [Google Scholar]
  60. 60.  Campi M, Garatti S 2008. The exact feasibility of randomized solutions of robust convex programs. SIAM J. Optim. 19:1211–30
     [Google Scholar]
  61. 61.  Calafiore G 2010. Random convex programs. SIAM J. Optim. 20:3427–64
     [Google Scholar]
  62. 62.  Zhang X, Grammatico S, Schildbach G, Goulart P, Lygeros J 2014. On the sample size of randomized MPC for chance-constrained systems with application to building climate control. 2014 European Control Conference478–83 New York: IEEE
     [Google Scholar]
  63. 63.  Schildbach G, Fagiano L, Morari M 2013. Randomized solutions to convex programs with multiple chance constraints. SIAM J. Optim. 23:2479–501
     [Google Scholar]
  64. 64.  Rosolia U, Carvalho A, Borrelli F 2017. Autonomous racing using learning model predictive control. 2017 American Control Conference5115–20 New York: IEEE
     [Google Scholar]
  65. 65.  Rosolia U, Zhang X, Borrelli F 2018. Robust learning model predictive control for uncertain iterative tasks: learning from experience. 2017 IEEE Conference on Decision and Control New York: IEEE. In press
     [Google Scholar]
  66. 66.  Brunner M, Rosolia U, Gonzales J, Borrelli F 2018. Repetitive learning model predictive control: an autonomous racing example. 2017 IEEE Conference on Decision and Control New York: IEEE. In press
     [Google Scholar]
  67. 67.  Zhang X, Schildbach G, Sturzenegger D, Morari M 2013. Scenario-based MPC for energy-efficient building climate control under weather and occupancy uncertainty. 2013 European Control Conference1029–34 New York: IEEE
     [Google Scholar]
  68. 68.  Zhang X, Vrettos E, Kamgarpour M, Andersson G, Lygeros J 2015. Stochastic frequency reserve provision by chance-constrained control of commercial buildings. 2015 European Control Conference1134–40 New York: IEEE
     [Google Scholar]
  69. 69.  Parisio A, Varagnolo D, Molinari M, Pattarello G, Fabietti L, Johansson K 2014. Implementation of a scenario-based MPC for HVAC systems: an experimental case study. IFAC Proc. Vol. 47:599–605
     [Google Scholar]
  70. 70.  Zhang X, Grammatico S, Margellos K, Goulart P, Lygeros J 2014. Randomized nonlinear MPC for uncertain control-affine systems with bounded closed-loop constraint violations. IFAC Proc. Vol. 47:1649–54
     [Google Scholar]
  71. 71.  Ioli D, Falsone A, Prandini M 2016. Energy management of a building cooling system with thermal storage: a randomized solution with feedforward disturbance compensation. 2016 American Control Conference2346–51 New York: IEEE
     [Google Scholar]
  72. 72.  Bezanzon J, Karpinski S, Shah V, Edelman A 2012. Julia: a fast dynamic language for technical computing. arXiv:1209.5145
/content/journals/10.1146/annurev-control-060117-105215
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Data-Driven Predictive Control for Autonomous Systems
Annual Review of Control, Robotics, and Autonomous Systems 1, 259 (2018); https://doi.org/10.1146/annurev-control-060117-105215
/content/journals/10.1146/annurev-control-060117-105215
/content/journals/10.1146/annurev-control-060117-105215
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