Model predictive control: past, present and future

By Manfred Morari, Jay H. Lee. 1999

Subfields: Optimization & Training, Robotics & Control

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Citations in the corpus

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).”
OptNet: Differentiable Optimization as a Layer in Neural Networks (2017, background): “…our scope (Boyd & Van-denberghe, 2004) but these methods have bound applicability in control frameworks (Morari & Lee, 1999; Sastry & Bodson, 2011); numerous statistical and mathematical formalisms (Sra et al., 2012), and physical simulation problems like rigid body dynamics (L ¨ otstedt, 1984).”
ROBUST LINEAR PROGRAMMING AND OPTIMAL CONTROL (2002, methodology): “Numerical algorithms for linear and quadratic programming have been applied to optimal control since the 60s, and are widely used in model-predictive control, see ( Morari and Lee 1999, Rawlings 2000).”
Predictive Active Steering Control for Autonomous Vehicle Systems (2007, background): “The main concept of MPC is to use a model of the plant to predict the future evolution of the system [6], [14], [17], [26], [27].”
Reference and command governors for systems with constraints: A survey on theory and applications (2017, unknown): “One route is to re-design the controller within the Model Predictive Control (MPC) framework [35, 109, 160, 166, 176].”
Invariant approximations of the minimal robust positively Invariant set (2005, background): “Set invariance is fundamental in the synthesis of reference governors [4], [5] and predictive controllers [6]–[9] with guaranteed invariance, stability and convergence properties.”
Stabilization with guaranteed safety using Control Lyapunov-Barrier Function (2016, methodology): “For example, Model Predictive Controlbased approach has been proposed in Bemporad, Borelli, and Morari (2002), Maciejowski (2002), Morari and Lee (1999) and ✩ The work of M.Z. Romdlony is supported by the Indonesian DIKTI scholarship program and the work of B. Jayawardhana is supported by the EU…”
Stochastic Inequality Constrained Closed-loop Model Predictive Control: With Application To Chemical Process Operation (2004, background): “In the vast amount of MPC literature one considers the following standard open-loop MPC control problem (Garcia et al., 1989; Rawlings, 2000; Morari and Lee, 1997).”
Model Predictive Control for Power Converters and Drives: Advances and Trends (2017, background): “MPC with a long prediction horizon improves the system’s performance and stability as compared to short prediction horizons [4].”
Simulation based strategy for nonlinear optimal control: application to a microbial cell reactor (2003, methodology): “An example is the popular method of model predictive control (MPC) [1, 2, 3], which requires a nonlinear dynamic optimization problem cast over a prediction window to be solved at each sample time when applied on a nonlinear process model.”
Nonlinear offset-free model predictive control (2012, background): “Key words: model predictive control, reference tracking, no offset, integral control 1 Introduction The main concept of MPC is to use a model of the plant to predict the future evolution of the system [12,13,15,21,23].”
Factorial design and simulation for the optimization and determination of control structures for an extractive alcoholic fermentation (2001, background): “This control algorithm has great potential for industrial application [20].”