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Data-Driven Predictive Control Beyon...

Nair, Siddharth,

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Data-Driven Predictive Control Beyond Linearity: An Autonomous Driving Perspective /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-Driven Predictive Control Beyond Linearity: An Autonomous Driving Perspective // Siddharth Nair.
Author:	Nair, Siddharth,
Description:	1 electronic resource (179 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
Subject:	Robotics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31297162
ISBN:	9798384450092

Data-Driven Predictive Control Beyond Linearity: An Autonomous Driving Perspective /
Nair, Siddharth,

Data-Driven Predictive Control Beyond Linearity: An Autonomous Driving Perspective /Siddharth Nair. - 1 electronic resource (179 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

Model Predictive Control (MPC) has been widely adopted in the industry for constrained optimal control, because of its straightforward transcription of the control problem and the availability of mature convex optimization solvers for efficient control synthesis. However as the name suggests, MPC requires a model of the underlying system and a quantification of the uncertainty in the assumed model for reliable performance and robustness. A vast body of work has been devoted to the analyses and design of robust and efficient MPC for linear systems with convex constraints. However, the world inherently involves nonlinear phenomena and non-convex decision-making, be it the dynamics of a bicycle, the multi-modality of a human driver, or the combinatorial problem of optimizing a route. While various MPC designs for nonlinear, non-convex settings have been studied, most suffer from at least one of the following shortcomings: (1) lack closed-loop performance guarantees or safety guarantees in the presence of uncertainty, (2) have small regions of feasibility due to conservative convexification of constraints or local linearization of dynamics, or (3) are intractable for real-time, high-frequency control.In this dissertation, we propose data-driven algorithms that exploit the nonlinearity and non-convexity in the optimal control problem for MPC designs that are robust to uncertainty and computationally efficient. The dissertation is divided into three parts focusing on robustness, performance, and computational efficiency. Each part presents algorithms that balance all three aspects, but are designed with an emphasis on the corresponding part's theme and demonstrated on practical applications in autonomous driving.

English

ISBN: 9798384450092Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Autonomous vehicles

Data-Driven Predictive Control Beyond Linearity: An Autonomous Driving Perspective /
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520 $a Model Predictive Control (MPC) has been widely adopted in the industry for constrained optimal control, because of its straightforward transcription of the control problem and the availability of mature convex optimization solvers for efficient control synthesis. However as the name suggests, MPC requires a model of the underlying system and a quantification of the uncertainty in the assumed model for reliable performance and robustness. A vast body of work has been devoted to the analyses and design of robust and efficient MPC for linear systems with convex constraints. However, the world inherently involves nonlinear phenomena and non-convex decision-making, be it the dynamics of a bicycle, the multi-modality of a human driver, or the combinatorial problem of optimizing a route. While various MPC designs for nonlinear, non-convex settings have been studied, most suffer from at least one of the following shortcomings: (1) lack closed-loop performance guarantees or safety guarantees in the presence of uncertainty, (2) have small regions of feasibility due to conservative convexification of constraints or local linearization of dynamics, or (3) are intractable for real-time, high-frequency control.In this dissertation, we propose data-driven algorithms that exploit the nonlinearity and non-convexity in the optimal control problem for MPC designs that are robust to uncertainty and computationally efficient. The dissertation is divided into three parts focusing on robustness, performance, and computational efficiency. Each part presents algorithms that balance all three aspects, but are designed with an emphasis on the corresponding part's theme and demonstrated on practical applications in autonomous driving.
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650 4 $a Computer science. $3 523869
650 4 $a Automotive engineering. $3 2181195
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653 $a Autonomous vehicles
653 $a Machine learning
653 $a Nonlinear systems
653 $a Optimization
653 $a Model Predictive Control
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