Constrained finite receding horizon linear quadratic control

Abstract

Issues of feasibility, stability and performance are considered for a finite horizon formulation of receding horizon control (RHC) for linear systems under mixed linear state and control constraints. It is shown that for a sufficiently long horizon, a receding horizon policy will remain feasible and result in stability, even when no end constraint is imposed. In addition, offline finite horizon calculations can be used to determine not only a stabilizing horizon length, but guaranteed performance bounds for the receding horizon policy. These calculations are demonstrated on two examples.

Discrete-Time Model Predictive Control

“…The most comprehensive and also most compact analysis has been presented by [13] and [14] whose arguments we will sketch here.…”

Nonlinear model predictive control enhanced by generalized pointwise min-norm scheme

“…We have mentioned in the preceding sections it will be inputs states 46th IEEE CDC, New Orleans, USA, Dec. [12][13][14]2007 ThC09.5…”

“…Different from the normal NMPC of (2) where we try to optimize the continuous control profile of u, the NMPC of (8) tries to achieve the optimal performance by the selection of inputs states inputs states 46th IEEE CDC, New Orleans, USA, Dec. [12][13][14]2007 ThC09.5…”

“…The proposed ENMPC algorithm, on the other hand, can guarantee that a control sequence always exists, because for any θ, from the theorem-I, we can always obtain a stable GPMN controller that meets all of the input and state 46th IEEE CDC, New Orleans, USA, Dec. [12][13][14]2007 ThC09.5 constraints, i.e., ( , ) ( , )…”

Control Predictivo Sujeto a Restricciones Poliédricas No Convexas: Solución Explícita Y Estabilidad

“…Este enfoque es utilizado, entre otros autores, por [BCM94], [GWB90], [CS82], [DMS96], [NMS96], [MS97], [Mea97] y [PN97].…”