Model Predictive Control: Theory and Design

2014

Citations in the corpus

From Pixels to Torques: Policy Learning with Deep Dynamical Models (2015, neutral): “The textbooks by Maciejowski (2002) and Rawlings & Mayne (2009) also provide good introductions to MPC.”
Cautious Model Predictive Control Using Gaussian Process Regression (2017, unknown): “, by solving a finite horizon LQR problem [18].”
Distributed model predictive control: A tutorial review and future research directions (2013, unknown): “The main idea of MPC is to choose control actions by repeatedly solving an online constrained optimization problem, which aims at minimizing a performance index over a finite prediction horizon based on predictions obtained by a system model [13], [85], [128].”
Postface to “ Model Predictive Control : Theory and Design ” (2012, unknown): “This assumption also implies that V 0 N (·) is continuous by applying Theorem C.28 in Rawlings and Mayne (2009).”
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control (2019, unknown): “In order to extend the iLQR paradigm into the closed-loop RL setting, we utilize the concept of model predictive control (MPC) [23, 24] and propose the following iLQR-MPC procedure.”
Energy Management and Optimization Methods for Grid Energy Storage Systems (2018, unknown): “Receding horizon control, also known asMPC [195], [196] is a similar approach that utilizes deterministic optimization with a lookahead forecast.”
OSQP: an operator splitting solver for quadratic programs (2017, unknown): “In the ﬁeld of control engineering, model predictive control (MPC) [42,82] and moving horizon estimation (MHE) [2] techniques require the solution of a QP at each time instant.”
Data-Enabled Predictive Control: In the Shallows of the DeePC (2018, unknown): “One of the most celebrated and widely used control techniques for trajectory tracking is receding horizon Model Predictive Control (MPC), precisely because it allows one to include safety considerations during control design [4]–[8].”
Learning-Based Model Predictive Control for Safe Exploration (2018, unknown): “There, we iteratively optimize the performance along ﬁnite-length trajectories at each time step, based on a known model that incorporates uncertainties and disturbances acting on the system [3].”
Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges (2020, unknown): “optimal control problem, and the receding horizon policy [2].”
Control: A perspective (2014, unknown): “Another computational approach to optimal control, model predictive control, which emerged from industry is now widely used [148,447,551,131,559,563].”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, unknown): “Model Predictive Control (MPC) has become the most attractive approach in the control of autonomous vehicles, due to its capability to systematically include system constraints and future predictions in the controller [442], [486]– [489].”
Provably safe and robust learning-based model predictive control (2011, unknown): “Because the constraints (6) and (8) in LBMPC are linear, the constraint φ is continuous [30].”
Learning an Approximate Model Predictive Controller With Guarantees (2018, unknown): “Model predictive control (MPC) [1] is a modern control method which has been actively researched in the last years.”
Safe Reinforcement Learning Using Robust MPC (2019, unknown): “…D and a given set of initial conditions S 0 , a policy π θ is labeled as η -safe for initial states s 0 ∈ S 0 if it satisﬁes In general, there can exist initial states for which a safe policy cannot exist [24], and Deﬁnition 1 characterizes policies that preserve safety for a given initial state.”