Constrained model predictive control: Stability and optimality

By David Q. Mayne, James B. Rawlings, C. V. Rao, P. Scokaert. 2000

8,592 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mathematical Optimization, Robotics & Control

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Citations in the corpus

From Pixels to Torques: Policy Learning with Deep Dynamical Models (2015, background): “It is also common to include an addition penalty term penalizing the ﬁnal state (cid:98) x t + N | t in order to achieve stability (Mayne et al., 2000).”
MPC for tracking piecewise constant references for constrained linear systems (2008, positive): “Following standard steps in the stability proofs of MPC (Mayne et al., 2000), we get that V∗N(xk+1, x̂s) ≤ VN(xk+1, x̂s,u(k + 1), θ(k + 1)) ≤ V∗N(xk, x̂s) − ‖xk − x ∗ s (k)‖ 2 Q .”
Hybrid predictive control of nonlinear systems: method and applications to chemical processes (2004, background): “Furthermore, the issue of closed-loop stability is typically addressed by introducing penalties and constraints on the state at the end of the finite optimization horizon (see Reference [ 23 ] for a survey of different constraints proposed in the literature).”
Stochastic Inequality Constrained Closed-loop Model Predictive Control: With Application To Chemical Process Operation (2004, background): “…question is the open-loop nature of the optimal control problem and the implicit feedback produced by the receding horizon implementation” and of Mayne et al. (2000) “while the problem has been studied, the outcome of the research are conceptual controllers that work in principle but are hard…”
Fast Model Predictive Control Using Online Optimization (2010, background): “For discussion of MPC from the point of view of optimal control, see [21]–[23]; for a survey of stability and optimality results, see [24].”
Min-max control of constrained uncertain discrete-time linear systems (2003, background): “This type of problem has been addressed in the context of constrained optimal control, and, in particular, in the context of robust receding horizon control (RHC) and robust model predictive control (MPC); see, e.g., [ 1 ] and [2].”
TUBE-BASED ROBUST NONLINEAR MODEL PREDICTIVE CONTROL1 (2007, positive): “Uncertainty demands that the optimal control problem employed in model predictive control should have a (feedback) control policy rather than an (open-loop) control sequence as a decision variable (Mayne et al., 2000).”
Trajectory Free Linear Model Predictive Control for Stable Walking in the Presence of Strong Perturbations (2006, neutral): “The idea of Receding Horizon Control, or Model Predictive Control [7], [8], [9] is to execute only the first interval [kT, (k+1)T ] of this trajectory, then measure the actual state of the system, here the position and velocity of the CoM, and then recompute a new trajectory with the same QP (6) but”
Control: A perspective (2014, background): “They sponsored research and arranged workshops at Shell to interact with academia [545,544], which in turn initiated research on stability and robustness [241,480,457,60].”
Barrier Lyapunov functions for the output tracking control of constrained nonlinear switched systems (2013, background): “Model Predictive Control that represents an effective control design methodology for handling both constraints and performance issues has been investigated in [9,10].”
Stochastic Model Predictive Control: An Overview and Perspectives for Future Research (2016, neutral): “Terminal constraints are included in the stochastic OCP to guarantee recursive feasibility [3].”