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Stochastic MPC.pdf

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Preview of Stochastic MPC
🔗 Source: dcsc.tudelft.nl
📊 Size: 299 KB
📄 Pages: 7 pages
⬇️ Downloads: 108

Summary

Model predictive control for stochastic max-plus linear systems with chance constraints is developed, where uncertainties are characterized as random variables and constraints are formulated as probabilistic chance constraints. Two methods are proposed to solve the chance-constrained MPC problem: one using Boole's inequality to convert multivariate chance constraints into univariate ones, and another employing Chebyshev's inequality to transform chance constraints into linear constraints on the inputs. Simulation results show that these methods are faster and yield lower closed-loop costs than the Monte Carlo simulation method and nominal MPC method.

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Description

Model predictive control for stochastic max-plus linear systems with chance constraints is developed, where uncertainties are characterized as random variables...

Technical Information

  • File Format: PDF
  • File Size: 299 KB
  • Pages: 7
  • Language: EN
  • Total Downloads: 108
  • Last Updated: 1 week ago

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This PDF document about Stochastic MPC provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Stochastic MPC.

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