The Multi-Parametric Toolbox (MPT) is a free Matlab toolbox for design, analysis and deployment of optimal controllers for constrained linear, nonlinear and hybrid systems. Efficiency of the code is guaranteed by the extensive library of algorithms from the field of computational geometry and multi-parametric optimization. The toolbox offers a broad spectrum of algorithms compiled in a user friendly and accessible format: starting from different performance objectives (linear, quadratic, minimum time) to the handling of systems with persistent additive and polytopic uncertainties. Users can add custom constraints, such as polytopic, contraction, or collision avoidance constraints, or create custom objective functions. Resulting optimal control laws can either be embedded into your applications in a form of a C code, or deployed to target platforms using Real Time Workshop.

References in zbMATH (referenced in 221 articles , 1 standard article )

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  1. Danielson, Claus: Fundamental domains for symmetric optimization: construction and search (2021)
  2. Gleason, Joseph D.; Vinod, Abraham P.; Oishi, Meeko M. K.: Lagrangian approximations for stochastic reachability of a target tube (2021)
  3. Lejarza, Fernando; Baldea, Michael: Economic model predictive control for robust optimal operation of sparse storage networks (2021)
  4. Müller-Hermes, Alexander: Decomposable Pauli diagonal maps and tensor squares of qubit maps (2021)
  5. Ning, Chao; You, Fengqi: Online learning based risk-averse stochastic MPC of constrained linear uncertain systems (2021)
  6. Pappas, Iosif; Diangelakis, Nikolaos A.; Pistikopoulos, Efstratios N.: The exact solution of multiparametric quadratically constrained quadratic programming problems (2021)
  7. Tran, Hoang-Dung; Pal, Neelanjana; Manzanas Lopez, Diego; Musau, Patrick; Yang, Xiaodong; Nguyen, Luan Viet; Xiang, Weiming; Bak, Stanley; Johnson, Taylor T.: Verification of piecewise deep neural networks: a star set approach with zonotope pre-filter (2021)
  8. Vinod, Abraham P.; Oishi, Meeko M. K.: Stochastic reachability of a target tube: theory and computation (2021)
  9. Wang, Zheming; Jungers, Raphaël M.; Ong, Chong Jin: Computation of the maximal invariant set of discrete-time linear systems subject to a class of non-convex constraints (2021)
  10. Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson: NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems (2020) arXiv
  11. Kalabić, Uroš V.; Kolmanovsky, Ilya V.: A constraint-separation principle in model predictive control (2020)
  12. Koeln, Justin; Raghuraman, Vignesh; Hencey, Brandon: Vertical hierarchical MPC for constrained linear systems (2020)
  13. Liao-McPherson, Dominic; Nicotra, Marco M.; Kolmanovsky, Ilya: Time-distributed optimization for real-time model predictive control: stability, robustness, and constraint satisfaction (2020)
  14. Li, Ben: Exact values of quantum violations in low-dimensional Bell correlation inequalities (2020)
  15. Mitze, Ruth; Mönnigmann, Martin: A dynamic programming approach to solving constrained linear-quadratic optimal control problems (2020)
  16. Ossareh, Hamid R.: Reference governors and maximal output admissible sets for linear periodic systems (2020)
  17. Palma, Jonathan M.; Morais, Cecília F.; Oliveira, Ricardo C. L. F.: (\mathcalH_2) control and filtering of discrete-time LPV systems exploring statistical information of the time-varying parameters (2020)
  18. Pavlov, Andrei; Shames, Iman; Manzie, Chris: Minimax strategy in approximate model predictive control (2020)
  19. Xiu, Xiaojie; Zhang, Ju: Grid (k)-(d) tree approach for point location in polyhedral data sets -- application to explicit MPC (2020)
  20. Brunner, Florian David; Heemels, W. P. M. H.; Allgöwer, Frank: Event-triggered and self-triggered control for linear systems based on reachable sets (2019)

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