Predictive Control for Linear and Hybrid Systems

By Francesco Borrelli, Alberto Bemporad, Manfred Morari. 2017

1,490 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/54514b1bcb18402dc8e72902f097343576c0a89d

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In Control and Systems Engineering

Citations in the corpus

Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function (2020, background): “Recursive feasibility is generally not guaranteed for MPCDC defined in (2) [18], [20] and other general NLP [21, Chap.”
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control (2019, methodology): “In order to extend the iLQR paradigm into the closed-loop RL setting, we utilize the concept of model predictive control (MPC) (Rawlings & Mayne, 2009; Borrelli et al., 2017) and propose the following iLQR-MPC procedure.”
Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges (2020, methodology): “As a result, the state-feedback control law is obtained and stored in look-up tables [87], [88], while methods, such as branch-”
Optimization-Based Collision Avoidance (2017, background): “, when the obstacle avoidance is not possible [50].”
Kinematic and dynamic vehicle models for autonomous driving control design (2015, background): “I. INTRODUCTION Work in autonomous vehicles has grown dramatically in the past several years due to advances in computing and sensing technologies.”
Reinforcement learning for control: Performance, stability, and deep approximators (2018, methodology): “(2017) also highlight novel connections between MPC (Grimm et al., 2005; Grüne and Pannek, 2016; Borrelli et al., 2017) and RL.”
Behavioral systems theory in data-driven analysis, signal processing, and control (2021, methodology): “These can account for, e.g., input saturation, operational limits, or terminal constraints needed for closed-loop stability of the predictive control scheme (Borrelli et al., 2017; Rawlings et al., 2017).”
Data-Enabled Predictive Control: In the Shallows of the DeePC (2018, neutral): “safety considerations during control design [4]–[8].”
A tutorial on task-parameterized movement learning and retrieval (2015, methodology): “The tracking problem can be solved by different techniques, either exploiting tools from physics, dynamic programming or linear algebra [6, 10].”
Cooperative Look-Ahead Control for Fuel-Efficient and Safe Heavy-Duty Vehicle Platooning (2015, background): “The vehicle controller layer, instead, is solved though a distributed model predictive control formulation [14] that tracks the speed trajectory and a certain gap policy while guarantying fuel-efficiency and safety.”