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 210 articles , 1 standard article )

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  1. 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
  2. Koeln, Justin; Raghuraman, Vignesh; Hencey, Brandon: Vertical hierarchical MPC for constrained linear systems (2020)
  3. Liao-McPherson, Dominic; Nicotra, Marco M.; Kolmanovsky, Ilya: Time-distributed optimization for real-time model predictive control: stability, robustness, and constraint satisfaction (2020)
  4. Li, Ben: Exact values of quantum violations in low-dimensional Bell correlation inequalities (2020)
  5. Ossareh, Hamid R.: Reference governors and maximal output admissible sets for linear periodic systems (2020)
  6. 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)
  7. Pavlov, Andrei; Shames, Iman; Manzie, Chris: Minimax strategy in approximate model predictive control (2020)
  8. Xiu, Xiaojie; Zhang, Ju: Grid (k)-(d) tree approach for point location in polyhedral data sets -- application to explicit MPC (2020)
  9. Brunner, Florian David; Heemels, W. P. M. H.; Allgöwer, Frank: Event-triggered and self-triggered control for linear systems based on reachable sets (2019)
  10. Ciripoi, Daniel; Löhne, Andreas; Weißing, Benjamin: Calculus of convex polyhedra and polyhedral convex functions by utilizing a multiple objective linear programming solver (2019)
  11. Eilbrecht, Jan; Stursberg, Olaf: Hierarchical solution of non-convex optimal control problems with application to autonomous driving (2019)
  12. Ferranti, Laura; Pu, Ye; Jones, Colin N.; Keviczky, Tamás: SVR-AMA: an asynchronous alternating minimization algorithm with variance reduction for model predictive control applications (2019)
  13. Fonseca, Daniel Guerra Vale da; Dantas, André Felipe O. de A.; Dórea, Carlos Eduardo Trabuco; Maitelli, André Laurindo; Shah, Umer H.: Explicit GPC control applied to an approximated linearized crane system (2019)
  14. Frick, Damian; Georghiou, Angelos; Jerez, Juan L.; Domahidi, Alexander; Morari, Manfred: Low-complexity method for hybrid MPC with local guarantees (2019)
  15. Gao, Yulong; Yu, Pian; Dimarogonas, Dimos V.; Johansson, Karl H.; Xie, Lihua: Robust self-triggered control for time-varying and uncertain constrained systems via reachability analysis (2019)
  16. Kvasnica, Michal; Bakaráč, Peter; Klaučo, Martin: Complexity reduction in explicit MPC: a reachability approach (2019)
  17. Maddalena, Emilio Tanowe; Galvão, Roberto Kawakami Harrop; Afonso, Rubens Junqueira Magalhães: Robust region elimination for piecewise affine control laws (2019)
  18. Oravec, Juraj; Holaza, Juraj; Horváthová, Michaela; Nguyen, Ngoc A.; Kvasnica, Michal; Bakošová, Monika: Convex-lifting-based robust control design using the tunable robust invariant sets (2019)
  19. Robin Verschueren, Gianluca Frison, Dimitris Kouzoupis, Niels van Duijkeren, Andrea Zanelli, Branimir Novoselnik, Jonathan Frey, Thivaharan Albin, Rien Quirynen, Moritz Diehl: acados: a modular open-source framework for fast embedded optimal control (2019) arXiv
  20. Tanaskovic, Marko; Fagiano, Lorenzo; Gligorovski, Vojislav: Adaptive model predictive control for linear time varying MIMO systems (2019)

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