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 230 articles , 1 standard article )

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  1. Mammarella, Martina; Mirasierra, Victor; Lorenzen, Matthias; Alamo, Teodoro; Dabbene, Fabrizio: Chance-constrained sets approximation: a probabilistic scaling approach (2022)
  2. Abbasi, Y.; Momeni, H. R.; Ramezani, A.: Robust tube-based MPC with enlarging the region of attraction for tracking of switched systems (2021)
  3. Burnak, Baris; Katz, Justin; Pistikopoulos, Efstratios N.: A space exploration algorithm for multiparametric programming via Delaunay triangulation (2021)
  4. Cunis, Torbjørn; Kolmanovsky, Ilya: Viability, viscosity, and storage functions in model-predictive control with terminal constraints (2021)
  5. Danielson, Claus: Fundamental domains for symmetric optimization: construction and search (2021)
  6. Gleason, Joseph D.; Vinod, Abraham P.; Oishi, Meeko M. K.: Lagrangian approximations for stochastic reachability of a target tube (2021)
  7. Koch, Anne; Berberich, Julian; Köhler, Johannes; Allgöwer, Frank: Determining optimal input-output properties: a data-driven approach (2021)
  8. Lejarza, Fernando; Baldea, Michael: Economic model predictive control for robust optimal operation of sparse storage networks (2021)
  9. Lucia, Walter; Franzè, Giuseppe; Famularo, Domenico: A receding horizon event-driven control strategy for intelligent traffic management (2021)
  10. Müller-Hermes, Alexander: Decomposable Pauli diagonal maps and tensor squares of qubit maps (2021)
  11. Ning, Chao; You, Fengqi: Online learning based risk-averse stochastic MPC of constrained linear uncertain systems (2021)
  12. Pappas, Iosif; Diangelakis, Nikolaos A.; Pistikopoulos, Efstratios N.: The exact solution of multiparametric quadratically constrained quadratic programming problems (2021)
  13. Salamati, Ali; Soudjani, Sadegh; Zamani, Majid: Data-driven verification of stochastic linear systems with signal temporal logic constraints (2021)
  14. Son, Sang Hwan; Park, Byung Jun; Oh, Tae Hoon; Kim, Jong Woo; Lee, Jong Min: Move blocked model predictive control with guaranteed stability and improved optimality using linear interpolation of base sequences (2021)
  15. 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)
  16. Vinod, Abraham P.; Oishi, Meeko M. K.: Stochastic reachability of a target tube: theory and computation (2021)
  17. 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)
  18. Dantas, Amanda D. O. S.; Dantas, André F. O. A.; Almeida, Túlio F. D.; Dórea, Carlos E. T.: Design of reduced complexity controllers for linear systems under constraints using data cluster analysis (2020)
  19. 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
  20. Kalabić, Uroš V.; Kolmanovsky, Ilya V.: A constraint-separation principle in model predictive control (2020)

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