Systems with persistent disturbances: predictive control with restricted constraints

By Luigi Chisci, J. Rossiter, Giovanni Zappa. at - Automatisierungstechnik, 2001

680 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/0d437c70a46d9e9ab1373569f4240a0a82c9a894

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In Control and Systems Engineering

Citations in the corpus

Offset‐free receding horizon control of constrained linear systems (2005, methodology): “As an example we chose the approach in [17], which is similar to the one proposed in this paper, in the sens e that a pre-stabilizing gain matrix is used and the plant state prediction at the end of the horizon in restri cted to be in the maximal disturbance invariant set O∞.”
Architectures for distributed and hierarchical Model Predictive Control - A review (2009, methodology): “Constraint satisfaction is guaranteed by a constraint tightening procedure similar to the one already used in [15] and in [55]; feasibility and convergence are guaranteed.”
Input-to-state stable MPC for constrained discrete-time nonlinear systems with bounded additive uncertainties (2002, background): “This idea is related to the robustness constraint defined for linear systems in [3] where the Kolmanovski set is subtracted to the state constraint set X .”
Safe Reinforcement Learning Using Robust MPC (2019, methodology): “However, this problem has been studied in control theory and one successful design technique is robust MPC [20], [21], [22].”
Model predictive control: Recent developments and future promise (2014, methodology): “It was initially used in MPC in Chisci et al. (2001) and Mayne and Langson (2001). In contrast to the methods discussed above that enforce, in solving PN , satisfaction of the constraints, by the open-loop trajectories (nominal and perturbed), tube-based methods ensure the closedloop trajectories (t”
Robust MPC for tracking zone regions based on nominal predictions (2012, positive): “The reachable set in j steps, R j , is given by This is the set of states of the nominal closed-loop systems which are reachable in j steps from the origin, under the disturbance input w [20].”
Postface to “ Model Predictive Control : Theory and Design ” (2012, methodology): “invariant sets are employed to construct tubes as shown in (Chisci et al., 2001) and (Mayne and Langson, 2001).”
Robust distributed model predictive control (2007, methodology): “( 2001 ) and Richards and How ( 2006 ) to accommodate uncertainty in future behaviour, is applied in the new algorithm to account for uncertainty in the actions of other subsystems.”
Robust model predictive control using tubes (2004, background): “The properties of the disturbance invariant controller are summarized in Proposition 2 that is proven in Mayne and Langson (2001) (related results appear in Chisci et al., 2001).”
Robust constrained model predictive control (2005, methodology): “[14]: the controller K(j) may be time varying and the terminal law K(x) is a general nonlinear law, known to exist, according to (2.”
Learning an Approximate Model Predictive Controller With Guarantees (2018, background): “This constraint tightening can be thought of as an over approximation of the constraint tightening used in [19], with the difference that it can be easily applied to nonlinear systems, compare [17].”