Automatic Control and System Dynamics
Automatic Control and System Dynamics

PCET (Polynomial Chaos Expansion Toolbox)

Overview

The PCET (Polynomial Chaos Expansion Toolbox) is a MATLAB®-based toolbox for the model-based, stochastic analysis and synthesis of nonlinear control systems. It includes

  • uncertainty analysis
  • parameter estimation
  • state estimation and prediction
  • experimental design
  • (active) fault diagnosis
  • stochastic nonlinear MPC
  • stochastic constraints

 

Features

  • simple syntax and usability
  • visualization of results
  • very efficient code
  • link to extern integrators
  • typical problem size: 5 states, 5 uncertain variables

System Requirements

PCET needs MATLAB® R2010b or later.

Download

PCET is currently under review and will soon be released open-scource.
For an unsupported version please contact (25.03.2019).

Documentation

The documentation is available here.

Installation Instruction

For installation simply unpack the compressed file and run the installation script (PCET.m).

Contact

If you have any questions or comments, please feel free to send us an e-mail ( ).

Related Publications

Stochastic model predictive control

  • Streif, S.; Karl, M.; Mesbah, A.: Stochastic Nonlinear Model Predictive Control with Efficient Sample Approximation of Chance Constraints. [URL]
  • Paulson, J.A.; Streif, S.; Mesbah, A.: Stability for Receding-horizon Stochastic Model Predictive Control with Chance Constraints. In Proc. American Control Conference (ACC). Chicago, IL. July 2015. In press..
  • Mesbah, A.; Streif, S.; Findeisen, R.; Braatz, R.D.: Stochastic Nonlinear Model Predictive Control with Probabilistic Constraints. In Proc. American Control Conference (ACC), pp. 2413-2419. Portland, Oregon. June 2014. [URL]
  • Paulson, J.A.; Mesbah, A.; Streif, S.; Findeisen, R.; Braatz, R.D.: Fast Stochastic Model Predictive Control of High-dimensional Systems. In Proc. 53rd IEEE Conference on Decision and Control (CDC), pp. 2802-2809. Los Angeles, CA. December 2014.

Active fault fiagnosis

  • Mesbah, A.; Streif, S.; Findeisen, R.; Braatz, R.D.: Active Fault Diagnosis for Nonlinear Systems with Probabilistic Uncertainties. In Proc. 19th IFAC World Congress, pp. 7079-7084. Cape Town, South Africa. August 2014. [URL]
  • Paulson, J.A.; Raimondo, D.M.; Braatz, R.D.; Findeisen, R.; Streif, S.: Guaranteed Active Fault Diagnosis for Uncertain Nonlinear Systems. In Proc. European Control Conference (ECC), pp. 926-931. Strasbourg, France. June 2014. [URL]
  • Streif, S.; Hast, D.; Braatz, R.D.; Findeisen, R.: Certifying robustness of separating inputs and outputs in active fault diagnosis for uncertain nonlinear systems. In Proc. 10th IFAC International Symposium on Dynamics and Control of Process Systems (DyCoPS), pp. 837-842. Mumbai, India. December 2013. [URL]

Experimental design

  • Mesbah, A.; Streif, S.: A Probabilistic Approach to Robust Optimal Experiment Design with Chance Constraints. In International Symposium on Advanced Control of Chemical Processes (ADCHEM). Whistler, British Columbia, Canada. June 2015. In press.. [URL]
  • Streif, S.; Petzke, F.; Mesbah, A.; Findeisen, R.; Braatz, R.D.: Optimal Experimental Design for Probabilistic Model Discrimination Using Polynomial Chaos. In Proc. 19th IFAC World Congress, pp. 4103-4109. Cape Town, South Africa. August 2014. [URL]