1,720,966 research outputs found
Learning model predictive control for quadrotors minimum-time flight in autonomous racing scenarios
Get PDFIn this paper, we design a Learning Model Predictive Control (LMPC) algorithm for quadrotors autonomous racing. The proposed algorithm allows to define a highly customizable 3D race track, in which multiple types of obstacles can be inserted. The controller is then able to autonomously find the best trajectory minimizing the quadrotor lap time, by learning from data coming from previous flights within the track, ensuring also the avoidance of all the obstacles therein. We also present novel relaxation approaches for the LMPC optimization problem, that allow to reduce it from a mixed-integer nonlinear program to a quadratic program. The LMPC algorithm is tested via several software-in-the-loop simulations, showing that the algorithm has learned to fly the quadrotor aggressively and dexterously, managing to both find the minimum-time trajectory and avoid the obstacles inside the track
Pseudo-Transient Continuation for Enhanced Quadratic Programming and Optimal Control
Get PDFQuadratic programming (QP) solvers that join effectiveness with a simple implementation are becoming essential in the field of optimal control, specifically when dealing with real-time applications with strict timing constraints and limited computational resources. To address this need, we present a novel high-performance QP solution method based on pseudo-transient continuation (PTC). PTC is a numerical technique that transforms multivariate nonlinear equations into autonomous systems that converge to the solution sought. In our approach, we recast the general QP Karush-Kuhn-Tucker (KKT) conditions into a system of equations and employ PTC to solve the latter to attain the optimal solution. Importantly, we provide theoretical guarantees demonstrating the global convergence of our PTC-based solver to the optimal solution of any given QP. To showcase the effectiveness of PTC, we employ it within the domain of Model Predictive Control (MPC). Specifically, numerical simulations are carried out on the MPC control of a quadrotor - a demanding dynamical system - highlighting excellent results in accurately executing the control task and ensuring lower computational times compared to conventional QP solvers
Solving Nonlinear MPC Problems in the Koopman Lifted Space: The Case Study of Mobile Robot Navigation in Cluttered Environments
Get PDFThe Koopman operator framework allows to transform nonlinear dynamical systems into equivalent linear ones within a higher-dimensional state space. Its application can be extended to nonlinear optimal control problems, enabling their efficient solution in the Koopman lifted space.
Here, we present a comprehensive analytical framework to lift general Nonlinear Model Predictive Control (NMPC) problems in the Koopman space, converting them into equivalent quadratic programs (QPs) - referred to as Koopman NMPC (K-NMPC) - that can be solved with superior computational performance.
Moreover, we advance analytical Koopman operator methods by proposing an algorithmic procedure to generate an invariant basis of Koopman observables to lift both the nonlinear prediction model and the nonlinear state constraints of NMPC; additionally, we present a general method to arbitrarily reduce the dimensionality of the Koopman lifted space, lowering the K-NMPC complexity and handling the infinite-dimensional case.
Our K-NMPC approach is validated through hardware-in-the-loop experiments on the case study of mobile robot navigation in cluttered environments, showcasing its solid performance and a ten-fold reduction in computation times
An Economic Nonlinear Model Predictive Control Approach for Mitigating Epidemic Spreading on Networks
Get PDFWe consider a discrete-time susceptible-infected-susceptible epidemic model on a network, in which we incorporate two control actions: vaccination of part of the population and implementation of non-pharmaceutical interventions. Then, we formulate the problem of devising an optimal control strategy for the epidemic disease using the two actions, with a tradeoff between public healthcare impact of the disease and social and economic costs associated with interventions. The control problem is solved by leveraging an economic nonlinear model-predictive control scheme, for which we prove the closed-loop stability using a dissipativity argument
Economic Nonlinear MPC for Conflicting Control Objectives: The Case of Adaptive Cruise Control
Get PDFOptimizing the energy consumption of electric vehicles (EVs) during operation is a key factor in mitigating their overall environmental impact. Autonomous vehicle functions, such as Adaptive Cruise Control (ACC), typically disregard economic criteria such as energy optimization, being in general not trivial to conciliate tracking and economic control tasks. Within the domain of optimal control, Economic Nonlinear MPC (E-NMPC) is designed to deliver an economically optimal control action, optimizing the economic profit of the plant. However, E-NMPC does not allow to include additional adversarial tasks, such as tracking, and its closed-loop stability is not easy to guarantee. In this work, we propose a novel E-NMPC formulation for conflicting control objectives - such as tracking and economic tasks - that attains the optimal trade-off between them. Furthermore, we propose a constructive procedure to design stabilizing terms for E-NMPC, ensuring its closed-loop stability with minimal impact on the economic performance. We apply the proposed E-NMPC strategy to the ACC case study, proving its effectiveness in simulation: the E-NMPC-based ACC proficiently attains the conflicting tasks, delivering a higher economic profit than standard NMPC, while ensuring closed-loop stability
Economic Nonlinear MPC for Conflicting Control Objectives with Constructive Stability Guarantees: A Two-Fold Application Case Study
No full textEconomic Nonlinear Model Predictive Control (E-NMPC) is a peculiar variant of classic NMPC, which directly includes an economic criterion within its stage cost function, allowing to steer the system under control towards an economically optimal equilibrium and ensuring a profitable economic performance during the transient.
While its formulation provides significant advantages for many practical applications, E-NMPC suffers from two main drawbacks: first, it only accounts for the economic objective, disregarding additional conflicting tasks, such as tracking; second, it forfeits classical Lyapunov-based stability guarantees, since, in general, its stage cost is non-minimal at the optimal economic equilibrium.
To address these limitations, we present a novel E-NMPC formulation, accounting for both economic and tracking tasks together. Additionally, we propose a general constructive procedure to design suitable stabilizing terms for E-NMPC, ensuring its closed-loop stability with minimal impact on the economic performance.
Our E-NMPC approach is validated on two different case studies: energy-efficient Adaptive Cruise Control (ACC) in electric vehicles, and mitigation of epidemic spreading on networks via vaccination and non-pharmaceutical interventions (NPIs)
A General Analytical Framework for Fast Solving Nonlinear MPC Problems in the Linear Koopman Space
Get PDFThe Koopman operator stands as a powerful framework to transform nonlinear dynamical systems into equivalent linear ones within a lifted state space. Its application can be extended to nonlinear optimal control problems, enabling their efficient solution in the linear Koopman space. However, a systematic methodology to analytically derive a suitable basis of Koopman observables and handle the operator infinite-dimensionality is still lacking. In this paper, we propose a comprehensive analytical framework to efficiently solve Nonlinear Model Predictive Control (NMPC) problems in the linear Koopman space. We present a general procedure to derive a basis of observables that lifts both the nonlinear prediction model and nonlinear state constraints of NMPC, obtaining a quadratic program in the Koopman lifted space (denoted as Koopman NMPC, in short K-NMPC) that closely approximates the original NMPC solution. Additionally, we propose a general method to arbitrarily reduce the dimensionality of the Koopman lifted space, lowering the K-NMPC complexity and handling the infinite-dimensional case. We validate our K-NMPC approach in simulation, showcasing its solid performance and execution times, which are over ten times lower than classic NMPC
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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