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 179 articles , 1 standard article )

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  1. Kvasnica, Michal; Bakaráč, Peter; Klaučo, Martin: Complexity reduction in explicit MPC: a reachability approach (2019)
  2. Maddalena, Emilio Tanowe; Galvão, Roberto Kawakami Harrop; Afonso, Rubens Junqueira Magalhães: Robust region elimination for piecewise affine control laws (2019)
  3. Akbari, Amir; Barton, Paul I.: An improved multi-parametric programming algorithm for flux balance analysis of metabolic networks (2018)
  4. Dantas, Amanda Danielle O. da S.; Dantas, André Felipe O. de A.; Campos, João Tiago L. S.; de Almeida Neto, Domingos L.; Dórea, Carlos Eduardo T.: PID control for electric vehicles subject to control and speed signal constraints (2018)
  5. Falconì, Guillermo P.; Angelov, Jorg; Holzapfel, Florian: Adaptive fault-tolerant position control of a hexacopter subject to an unknown motor failure (2018)
  6. Ghaffarinasab, Nader; Van Woensel, Tom; Minner, Stefan: A continuous approximation approach to the planar hub location-routing problem: modeling and solution algorithms (2018)
  7. Ghasemi, Mohammad S.; Afzalian, Ali A.: Invariant convex approximations of the minimal robust invariant set for linear difference inclusions (2018)
  8. Hill, Robin; Luo, Yousong; Schwerdtfeger, Uwe: Exact recursive updating of state uncertainty sets for linear SISO systems (2018)
  9. Lv, Jianfeng; Gao, Yan: The computation of the viability kernel for switched systems (2018)
  10. Nguyen, Ngoc Anh: Stochastic output feedback control: convex lifting approach (2018)
  11. Nguyen, Ngoc Anh; Olaru, Sorin: A family of piecewise affine control Lyapunov functions (2018)
  12. Rastegar, Saeid; Araújo, Rui; Sadati, Jalil: Robust synergetic control design under inputs and states constraints (2018)
  13. Sheikhbahaei, Reza; Alasty, Aria; Vossoughi, Gholamreza: Robust fault tolerant explicit model predictive control (2018)
  14. Wang, Hao; Kolmanovsky, Ilya V.; Sun, Jing: Zonotope-based recursive estimation of the feasible solution set for linear static systems with additive and multiplicative uncertainties (2018)
  15. Zhang, Ju; Xiu, Xiaojie: K-d tree based approach for point location problem in explicit model predictive control (2018)
  16. Al Khatib, Mohammad; Girard, Antoine; Dang, Thao: Stability verification and timing contract synthesis for linear impulsive systems using reachability analysis (2017)
  17. Ariño, Carlos; Sala, Antonio; Pérez, Emilio; Bedate, Fernando; Querol, Andrés: Asymptotically exact stabilisation for constrained discrete Takagi-Sugeno systems via set-invariance (2017)
  18. Borrelli, Francesco; Bemporad, Alberto; Morari, Manfred: Predictive control for linear and hybrid systems (2017)
  19. Coogan, Samuel; Arcak, Murat: Finite abstraction of mixed monotone systems with discrete and continuous inputs (2017)
  20. Dreossi, Tommaso; Dang, Thao; Piazza, Carla: Reachability computation for polynomial dynamical systems (2017)

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