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 156 articles , 1 standard article )

Showing results 1 to 20 of 156.
Sorted by year (citations)

1 2 3 ... 6 7 8 next

  1. Ghasemi, Mohammad S.; Afzalian, Ali A.: Invariant convex approximations of the minimal robust invariant set for linear difference inclusions (2018)
  2. Al Khatib, Mohammad; Girard, Antoine; Dang, Thao: Stability verification and timing contract synthesis for linear impulsive systems using reachability analysis (2017)
  3. Borrelli, Francesco; Bemporad, Alberto; Morari, Manfred: Predictive control for linear and hybrid systems (2017)
  4. Coogan, Samuel; Arcak, Murat: Finite abstraction of mixed monotone systems with discrete and continuous inputs (2017)
  5. Dreossi, Tommaso; Dang, Thao; Piazza, Carla: Reachability computation for polynomial dynamical systems (2017)
  6. Ghasemi, Mohammad S.; Afzalian, Ali A.: Robust tube-based MPC of constrained piecewise affine systems with bounded additive disturbances (2017)
  7. Goebel, Gregor; Allgöwer, Frank: Semi-explicit MPC based on subspace clustering (2017)
  8. Hernández-Mejías, Manuel A.; Sala, Antonio: Reliability and time-to-failure bounds for discrete-time constrained Markov jump linear systems (2017)
  9. Hofmann, Andreas G.; Williams, Brian C.: Temporally and spatially flexible plan execution for dynamic hybrid systems (2017)
  10. Lorenzen, Matthias; Dabbene, Fabrizio; Tempo, Roberto; Allgöwer, Frank: Stochastic MPC with offline uncertainty sampling (2017)
  11. Nguyen, Ngoc Anh; Olaru, Sorin; Rodríguez-Ayerbe, Pedro; Kvasnica, Michal: Convex liftings-based robust control design (2017)
  12. Sala, Antonio; Hernández-Mejías, Manuel; Ariño, Carlos: Stable receding-horizon scenario predictive control for Markov-jump linear systems (2017)
  13. Adelgren, Nathan; Wiecek, Margaret M.: A two-phase algorithm for the multiparametric linear complementarity problem (2016)
  14. Habibi, Jalal; Moshiri, Behzad; Sedigh, Ali Khaki; Morari, Manfred: Low-complexity control of hybrid systems using approximate multi-parametric MILP (2016)
  15. Hernández-Mejías, Manuel A.; Sala, Antonio; Ariño, Carlos; Querol, Andrés: Reliable controllable sets for constrained Markov-jump linear systems (2016)
  16. Nguyen, Ngoc Anh; Olaru, Sorin; Rodríguez-Ayerbe, Pedro; Bitsoris, George; Hovd, Morten: Explicit robustness and fragility margins for linear discrete systems with piecewise affine control law (2016)
  17. Scott, Joseph K.; Raimondo, Davide M.; Marseglia, Giuseppe Roberto; Braatz, Richard D.: Constrained zonotopes: a new tool for set-based estimation and fault detection (2016)
  18. Seyboth, Georg S.; Ren, Wei; Allgöwer, Frank: Cooperative control of linear multi-agent systems via distributed output regulation and transient synchronization (2016)
  19. Trottemant, E. J.; Scherer, C.W.; Mazo, M. jun.: Optimality of robust disturbance-feedback strategies (2016)
  20. Xu, Jun; van den Boom, Ton J.J.; De Schutter, Bart; Wang, Shuning: Irredundant lattice representations of continuous piecewise affine functions (2016)

1 2 3 ... 6 7 8 next

Further publications can be found at: