Model predictive control: Recent developments and future promise

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

When to use parametric models in reinforcement learning? (2019, methodology): “This is commonly and successfully employed in model-predictive control [Richalet et al., 1978, Morari and Lee, 1999, Mayne, 2014, Wagener et al., 2019].”
Learning Visual Predictive Models of Physics for Playing Billiards (2015, background): “The idea of using internal models for planning actions is well known in the control literature (Mayne, 2014).”
From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020, methodology): “Linear model predictive control (MPC) is an advanced control technique able to deal with multiple input multiple output constrained linear systems (Mayne, 2014; Rawlings & Mayne, 2009).”
Stochastic Model Predictive Control: An Overview and Perspectives for Future Research (2016, positive): “M odel predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems [1], [2].”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, background): “Model predictive control has seen tremendous success in the industrial process control applications, due mainly to its simple concept and its ability to handle complicated process models with input constraints and nonlinearities [263].”