Model Predictive Control Toolbox

Model Predictive Control Toolbox™ provides functions, an app, and Simulink® blocks for designing and simulating model predictive controllers (MPCs). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance. You can adjust the behavior of the controller by varying its weights and constraints at run time. To control a nonlinear plant, you can implement adaptive and gain-scheduled MPCs. For applications with fast sample rates, you can generate an explicit model predictive controller from a regular controller or implement an approximate solution. For rapid prototyping and embedded system implementation, the toolbox supports C-code and IEC 61131-3 Structured Text generation

References in zbMATH (referenced in 13 articles )

Showing results 1 to 13 of 13.
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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. Cimini, Gionata; Bemporad, Alberto: Complexity and convergence certification of a block principal pivoting method for box-constrained quadratic programs (2019)
  3. Mönnigmann, Martin: On the structure of the set of active sets in constrained linear quadratic regulation (2019)
  4. Li, Zheng; Wang, Guoli: Generalized predictive control of linear time-varying systems (2017)
  5. Bemporad, Alberto; Bellucci, Leonardo; Gabbriellini, Tommaso: Dynamic option hedging via stochastic model predictive control based on scenario simulation (2014)
  6. Patrinos, Panagiotis; Bemporad, Alberto: An accelerated dual gradient-projection algorithm for embedded linear model predictive control (2014)
  7. Di Cairano, S.; Brand, M.; Bortoff, S. A.: Projection-free parallel quadratic programming for linear model predictive control (2013)
  8. Kishida, Masako; Versypt, Ashlee N. Ford; Pack, Daniel W.; Braatz, Richard D.: Optimal control of one-dimensional cellular uptake in tissue engineering (2013)
  9. Bemporad, Alberto; Oliveri, Alberto; Poggi, Tomaso; Storace, Marco: Ultra-fast stabilizing model predictive control via canonical piecewise affine approximations (2011)
  10. Pannocchia, Gabriele; Bemporad, Alberto: Combined design of disturbance model and observer for offset-free model predictive control (2007)
  11. Vanek, Bálint; Bokor, József; Balas, Gary J.; Arndt, Roger E. A.: Longitudinal motion control of a high-speed supercavitation vehicle (2007)
  12. Balluchi, A.; Benvenuti, L.; Ferrari, A.; Sangiovanni-Vincentelli, A. L.: Hybrid systems in automotive electronics design (2006)
  13. Balluchi, A.; Benvenuti, L.; Engell, S.; Geyer, T.; Johansson, K. H.; Lamnabhi-Lagarrigue, F.; Lygeros, J.; Morari, M.; Papafotiou, G.; Sangiovanni-Vincentelli, A. L.; Santucci, F.; Stursberg, O.: Hybrid control of networked embedded systems (2005)