Model Predictive Control

By Sébastien Mariéthoz, S. Almér. 2012

188 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

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In Control and Systems Engineering

Citations in the corpus

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (2018, methodology): “anning, the choice of action a tcan depend on the state, forming a policy, ˇ: s t!a t. Otherwise, planning with actions independent of the state is typically framed as model predictive control (MPC) [Camacho and Alba, 2013]. MPC can be considered a special-case 4 policy, trained as a function of the”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, neutral): “ther the solutions to the right hand side of (V.25) exist, and when they do, the stability and robustness of the closed loop system. These issues are investigated in the predictive control literature [146], [147]. To implement an MPC on a driverless car, (V.25) must be solved several times per secon”
Deep predictive policy training using reinforcement learning (2017, methodology): “raints determines limitations on the control signals and states. The methods in optimal control theory most resembling our approach are trajectory optimization [22] and model predictive control (MPC) [23]. Trajectory optimization methods ﬁnd open-loop control trajectories by optimizing a given cost ”
Data-Enabled Predictive Control: In the Shallows of the DeePC (2018, unknown): “ s > 0reduces the number of computations, and in some cases may improve performance [43]. Under standard assumptions, it can be shown that the MPC algorithm is recursively feasible and stabilizing [5]. One crucial ingredient for MPC is an accurate model of the system; this is needed both to formu”
A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP (2015, methodology): “Building on insights from the control-theoretic formulation, we argue that model predictive control (MPC) [22] is”
An Online Learning Approach to Model Predictive Control (2019, methodology): “Various MPC algorithms have been proposed, using tools ranging from constrained optimization techniques [7, 20, 27] to sampling-based techniques [30].”
Data-driven discovery of Koopman eigenfunctions for control (2017, unknown): “Generally applicable frameworks, such as dynamic programming [15], Pontryagin’s maximum principle [92], and model-predictive control (MPC) [2, 26], often suffer from the curse of dimensionality and require considerably computational effort, e.”
Black-box data-efficient policy search for robotics (2017, background): “end of each episode. One of the best ways to take advantage of this sequential state recording is to learn a dynamical model of the robot [11], and then exploit it either for model-predictive control [12] or to ﬁnd an optimal policy ofﬂine [7]. However, such approaches assume that the model is “good”
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit (2017, result): “ improved control performance in the low-data limit, compared with other leading data-driven methods, including linear response models and neural networks. Model-based control techniques, such as MPC [3,4] and optimal control [5,6], are cornerstones of advanced process control, and are well-position”