A survey of industrial model predictive control technology

By S. Qin, T. A. Badgwell. 2003

5,172 citations recorded by the source corpus.

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

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Citations in the corpus

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control (2018, background): “This approach has led to tremendous success in a variety of control systems such as power grids, chemical process control [13], and more recently in robotics [14].”
Stochastic Inequality Constrained Closed-loop Model Predictive Control: With Application To Chemical Process Operation (2004, background): “Linear model predictive control is well established in industry, (Qin and Badgewell, 2003), and standard software is sold off the shelf.”
Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation (2017, positive): “This presents a possibility of applying decision support systems based on MPC in realising a desired crop yield and also a water-saving goal [136].”
Architectures for distributed and hierarchical Model Predictive Control - A review (2009, background): “the survey papers [80,81] on the industrial applications of linear and nonlinear MPC.”
Control: A perspective (2014, background): “Another computational approach to optimal control, model predictive control, which emerged from industry is now widely used [148,447,551,131,559,563].”
Control allocation - A survey (2013, background): “…excessive degrees of freedom are commonly exploited via model predictive control (MPC) and real-time optimization, (Garcia, Prett, & Morari, 1989; Qin & Badgwell, 2003), which are multi-variable optimization-based control strategies where the functionality of control allocation is inherently…”
Wireless Network Design for Control Systems: A Survey (2017, methodology): “It basically employs a MPC, an actuator state, and an adaptive IEEE 802.11 MAC to reduce unbounded packet delay and improve the tolerance against the packet loss.”
Performance bounds for linear stochastic control (2009, methodology): “An even more sophisticated feedback control function is given by certainty-equivalent model predictive control (MPC) [16,21,10,15, 4,19], in which φ(z) is found by solving the optimization problem minimize Vmpc(x̃(T )) + T−1∑ τ=0 ( "x(x̃(τ )) + "u(v(τ )) ) subject to x̃(τ + 1) = Ax̃(τ ) + Bv(τ ), τ ”
State and Output Feedback Nonlinear Model Predictive Control: An Overview (2003, methodology): “The “certainty equivalence”method is often used in a somewhat ad-hoc manner in industry [ 78 ], e.g.”
Parametric optimization and optimal control using algebraic geometry methods (2006, methodology): “International Journal of Control Vol. 79, No. 11, November 2006, 1340–1358 Parametric optimization and optimal control using algebraic geometry methods I. A. FOTIOU*{, P. ROSTALSKI{, P. A. PARRILO{ and M. MORARI{ {Automatic Control Laboratory, Swiss Federal Institute of Technology (ETH), 8092…”
Optimal Perimeter Control for Two Urban Regions With Macroscopic Fundamental Diagrams: A Model Predictive Approach (2013, background): “found in [15], while theoretical issues of MPC can be found in [16]–[20].”
Model Predictive Control with Integral Action: A simple MPC algorithm (2013, background): “A survey of both linear and nonlinear MPC is given in, e.g. Maeder et al. (2009), Qin and Badgwell (2003).”