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Number 1 - March 2020
Volume 30 - 2020
Nonlinear model predictive control for processes with complex dynamics: A parameterisation approach using Laguerre functions
Maciej Ławryńczuk
Abstract
Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex
dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each
sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC
algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as
well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of
classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is
shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good
quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.
Keywords
process control, nonlinear model predictive control, Laguerre functions, linearisation