Model predictive control

Model predictive control (MPC) is already widely used in the process industries. An important advantage of MPC is its ability to handle input and state constraints for large scale multi-variable plants. The controller computes each control action by solving an optimal control problem on-line based on the current estimated state of the plant. With the advent of fast processing and optimised algorithms possibilities are opening up for the application of MPC to new technologies.

The Control Systems Centre has long been associated with the development and application of MPC. David Sandoz, founder of Predictive Control Ltd. and more recently Perceptive Engineering Ltd, is a visiting Professor at the Centre. Our current interest in MPC ranges from the efficient implementation of appropriate optimisation algorithms to the analysis of the dynamic performance and robustness properties of such controllers. An important part of our research is the transfer of advanced process control techniques to industry.

  • A.G. Wills and W.P. Heath. Application of barrier function based model predictive control to an edible oil refining process. To appear in Journal of Process Control, 2005.
  • B. Lennox. Integrated condition monitoring and model predictive control. To appear in International Journal of Adaptive Control and Signal Processing, 2004.
  • A.G. Wills and W.P. Heath. Barrier Function Based Model Predictive Control. Automatica, 40, pp1415-1422, 2004.
  • A.G. Wills and W.P. Heath: Interior-point methods for linear model predictive control. UKACC Control 2004, University of Bath, Sept 6th-9th, 2004.
  • W.P. Heath, A.G. Wills and J.A.G. Akkermans: A sufficient robustness condition for constrained model predictive control. UKACC Control 2004, Bath, Sept 6th-9th, 2004.
  • A.G. Wills and W.P Heath: Nonlinear MPC and self-concordant barrier functions. NOLCOS 2004, Stuttgart, Symposium on Nonlinear Control Systems, Sept 1-3rd, 2004.
  • A.G. Wills and W.P. Heath: An exterior/interior-point approach to infeasibility in model predictive control. CDC03, Hawaii, Dec 9th-12th, 2003.
  • O. Marjanovic, B. Lennox, P. Goulding and D. Sandoz: Minimising conservatism in infinite horizon LQR control. Systems and Control Letters, 46 (4), 271-279, 2002.
  • A.G. Wills and W.P. Heath: Using a modified predictor-corrector algorithm for model predictive control. IFAC World Congress, Barcelona, July 21st-26th, 2002.
  • D.J. Sandoz, M.J. Desforges, B. Lennox and P.R. Goulding. Algorithms for industrial model predictive control. Computing and Control Engineering Journal, 11 (3), p125-134, 2000.
  • D.J. Sandoz, B. Lennox, P.R. Goulding, P. Thorpe, T. Kurth, M.J. Desforges and I.S. Woolley: Innovation in industrial model predictive control. Computing and Control Engineering Journal, 10 (5), 189-197, 1999.
  • B. Lennox, G.A. Montague, A. Beaumont and A. Frith: Non-linear model based predictive control of a gasoline engine. Trans. Inst. Measurement and Control, 20 (2), 103-112, 1998.
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