International Journal of Control, Automation and Systems 2010; 8(2): 187-197
Published online April 16, 2010
https://doi.org/10.1007/s12555-010-0202-z
© The International Journal of Control, Automation, and Systems
This paper revisits the stability issue of earlier model predictive control (MPC) algorithms where the performance index has a finite receding horizon and there is no terminal penalty in the performance index or other constraints added in online optimisation for the purpose of stability. Stability conditions are presented for MPC of constrained linear and nonlinear systems, and there is no restriction on the length of the horizon. These conditions can be used to test whether or not desired stability properties can be achieved under chosen state and control weightings.
Keywords Constrained control, finite horizon, Lyapunov theory, nonlinear systems, predictive control, stability.
International Journal of Control, Automation and Systems 2010; 8(2): 187-197
Published online April 1, 2010 https://doi.org/10.1007/s12555-010-0202-z
Copyright © The International Journal of Control, Automation, and Systems.
Wen-Hua Chen
Loughborough University, UK
This paper revisits the stability issue of earlier model predictive control (MPC) algorithms where the performance index has a finite receding horizon and there is no terminal penalty in the performance index or other constraints added in online optimisation for the purpose of stability. Stability conditions are presented for MPC of constrained linear and nonlinear systems, and there is no restriction on the length of the horizon. These conditions can be used to test whether or not desired stability properties can be achieved under chosen state and control weightings.
Keywords: Constrained control, finite horizon, Lyapunov theory, nonlinear systems, predictive control, stability.
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