Model predictive control: Theory and practice - A survey

By Carlos E. Garcia, D. M. Prett, Manfred Morari. 1989

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Citations in the corpus

Dynamics-Aware Unsupervised Discovery of Skills (2019, methodology): “In order to perform planning, we employ the model-predictive-control (MPC) paradigm Garcia et al. (1989), which in a standard setting generates a set of action plans P k = ( a k, 1 , . . . a k,H ) ∼ P for a planning horizon H .”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, background): “Model predictive control [135] is a general control design methodology which can be very effective for this problem.”
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control (2018, background): “Trajectory optimization and model predictive control (MPC) have a long history in robotics and control systems [11, 12]1.”
Optimism-driven exploration for nonlinear systems (2015, methodology): “We control the dynamical system using model-predictive control (MPC) [14], [15], [16], [17].”
Review on model predictive control: an engineering perspective (2021, background): “The effort is shifted from controller design towards modeling [35, 104, 105].”
Data-Enabled Predictive Control: In the Shallows of the DeePC (2018, unknown): “trajectories [30], [31].”
Linear offset-free Model Predictive Control (2009, methodology): “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].”
A new neural networks based adaptive model predictive control for unknown multiple variable non-linear systems (2008, unknown): “1 Introduction Model predictive control (MPC) is the most widely used in the chemical process industries and in other areas (Morari et al., 1994; Eaton and Rawlings, 1992; Garcia et al., 1989).”
Parametric optimization and optimal control using algebraic geometry methods (2006, background): “This ability of dealing with constraints is one of the main reasons for the practical success of model predictive control (MPC) (Garcia et al. 1989).”
Space-indexed dynamic programming: learning to follow trajectories (2008, background): “Furthermore,MPC canoftentimes be computationally impractical to run real-time.”
Predictive reward signal of dopamine neurons. (1998, background): “This is the to predict and react to system states before they actually essence of ‘‘coming back for more’’ and is related to the occur (Garcia et al. 1989).”
Receding Horizon Differential Dynamic Programming (2007, methodology): “Here we propose a fixed-length nondiscounted Receding Horizon scheme in the spirit of Model Predictive Control [19].”