The stability of constrained receding horizon control

Abstract

An infinite horizon controller is developed that allows incorporation of input and state constraints in a receding horizon feedback strategy. For both stable and unstable linear plants, feasibility of the constraints guarantees nominal closed-loop stability for all choices of the tuning parameters in the control law. The constraints' feasibility Manuscript

A steady-state target calculation method based on “point” model for integrating processes

“…In 1993, Rawlings and Muske imposed the states of unstable model to be zero at the end of the horizon in infinite horizon controllers and guaranteed the stability of the system [11]. In 2003, Rodrigues and Odloak proposed an infinite horizon model predictive control method for integrating process [12], and the stability was proved [13].…”

Linear MPC with optimal prioritized infeasibility handling: application, computational issues and stability

“…When the on-line optimization problem becomes infeasible, the lowest prioritized constraints are dropped (Qin and Badgwell 1997). In the research literature, the few contributions to this eld include (Rawlings and Muske 1993), (Scokaert and Rawlings 1999), (Garcia and Morshedi 1986), (Kerrigan et al 2000), (Tyler and Morari 1999), (Scokaert 1994), (Alvarez and de Prada 1997), and (Vada et al 2001). To the bestof the authors knowledge, the strategy presented in (Vada et al 2001) is the only optimal infeasibility handler which considers hard prioritized constraints without the use of a sequential solution approach.…”

“…Important stability results within the area of linear MPC are given in (Rawlings and Muske 1993) under the assumption of feasibility. In order to fully exploit this stabilizing property, a means to recover from infeasibility of the associated optimization problem whenever possible is required.…”

“…disturbances, operator intervention, modelling errors, or plant failures. Note that in the MPC controller proposed by Rawlings and Muske (1993), an approach for handling infeasibilities caused by the state constraints is included. ?…”

“…Note that the model de nes the nominal case, while the need for feasibility handling often arises from model/plant mismatch or disturbances. The presentation is based on the well known linear MPC problem (Rawlings and Muske 1993): min t (x t t ) = P 1 j=t x T jjt Qx jjt + u T jjt Ru jjt subject to:…”