Tutorial overview of model predictive control

By James B. Rawlings. 2000

1,224 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/72773b6c9fd5c380b11083d4ace72281dd9ace16

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In Control and Systems Engineering

Citations in the corpus

An Introduction to Nonlinear Model Predictive Control (2002, background): “Instead we refer to the following list for some excellent reviews [4, 16, 22, 52, 58, 68].”
An application of model predictive control to the dynamic economic dispatch of power generation (2011, methodology): “It has been shown that MPC algorithms approximate many complex system behaviors by using simplified models to a satisfactory extent (see, e.g., Camacho & Bordons, 2004; De Nicolao et al., 2000; Findeisen et al., 2003; Mayne et al., 2000; Qin & Badgwell, 2003; Rawlings, 2000).”
Dynamic Programming and Suboptimal Control: A Survey from ADP to MPC (2005, background): “give many references, see Morari and Lee [MoL99], Mayne et. al. [MRR00], Rawlings [ Raw00 ],”
A survey of industrial model predictive control technology (2003, background): “Rawlings (2000) provides an excellent introductory tutorial aimed at control practitioners.”
Closed-loop and activity-guided optogenetic control. (2015, neutral): “MPC (and stochastic MPC) uses estimated system dynamics to forecast where the system will be in several time steps and then chooses control inputs that will bring that forecast more in line with the target trajectory over that time horizon (Figure 5D; Table 1; Rawlings, 2000; Cheng et al., 2014).”
Hybrid predictive control of nonlinear systems: method and applications to chemical processes (2004, neutral): “see the tutorial paper [1]).”
A tutorial review of economic model predictive control methods (2014, background): “, [116,109,140]), a control strategy based on optimal control concepts, has been widely implemented in the chemical process industry.”
ROBUST LINEAR PROGRAMMING AND OPTIMAL CONTROL (2002, methodology): “The techniques discussed here extend to a variety of related problems, for example, problems with an 1- objective (Rao and Rawlings 2000) or a quadratic objective, problems with additional convex constraints such as slew rate constraints and terminal state constraints, and problems with ellipsoidal…”
Review on model predictive control: an engineering perspective (2021, methodology): “The weight Wξ is a trade-off between the amount and duration of a violation [101].”
Attacks against process control systems: risk assessment, detection, and response (2011, background): “Likewise, empirical models are often adjusted to account for known process physics [51, 52].”
State and Output Feedback Nonlinear Model Predictive Control: An Overview (2003, background): “However, most of the outlined approaches have dual discrete time counterparts, see for example [3, 22, 69, 80, 81].”
Automatic control of a 30 MWe SEGS VI parabolic trough plant (2004, methodology): “A structure of a MPC controller is shown in Figure 5 (Rawlings, 2000).”
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit (2017, background): “Model predictive control is particularly ubiquitous in industrial applications, as it enables the control of strongly nonlinear systems with constraints, which are difficult to handle using traditional linear control approaches [17, 38, 35, 40, 39, 18, 25, 33, 16].”