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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Becerra, Gerardo Minh Tu Pham Patino, Diego Lin-Shi, Xuefang UNAD Bogota Colombia Univ Claude Bernard Lyon 1 INSA Lyon Univ Lyon Ecole Cent LyonCNRSAmpereUMR5005 F-69621 Villeurbanne France Pontificia Univ Javeriana Dept Elect Engn Bogota Colombia

ISBN: (纸本)9781713872344

A method for control of discrete-time switched systems based on parameterized Lyapunov functions is presented. A polynomial representation of the switched system is formulated, and the method of moments is applied to obtain a relaxed model. The control problem is recast as an online receding horizon optimization problem where the Lyapunov function parameters and moment sequences are the decision variables. A novel procedure to synthesize the switching control signal from the recovered measure is described. The method is illustrated in simulation for the case of a pulse-width modulated multicellular converter, showing good reference tracking results.

关键词： control of switched systems Lyapunov methods model predictive and optimization-based control polynomial methods modeling and simulation of power systems

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Optimal privacy protection of mobility data: a predictive approach 22

Optimal privacy protection of mobility data: a predictive ap...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Molina, Emilio Fiacchini, Mirko Cerf, Sophie Robu, Bogdan Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France Univ Lille Inria CNRS Cent LilleUMR 9189CRIStAL F-59000 Lille France

ISBN: (纸本)9781713872344

Location data are extensively used to provide geo-personalized contents to mobile devices users. Sharing such personal data is a major threat to privacy, with risks of re-identification or inference of sensitive information. Location data broadcasted to services can be sanitized, i.e., by adding noise to spatial coordinates. Such protection mechanisms from the literature are widely generic, e.g., not specific to a user and mobility properties. In this work, we advocate that taking into account the specificities of location data (temporal correlation, human mobility patterns, etc.) enables to gain in the privacy-utility trade-off. Specifically, using future mobility prediction greatly improves privacy. We present a novel protection mechanism, based on model predictive control (MPC). The sanitized location is optimally computed so that it maximizes privacy while guaranteeing a utility loss constraint, for present and future locations. Our formulation explicitly takes into account non-constant sampling time, due to moments when no location data is broadcasted. We evaluate experimentally our control on real mobility dataset and compare to the state of the art. Results show that with knowledge of user's future mobility over a few of minutes, we can gain up to 10% of privacy compared to state of the art, while preserving data utility. Copyright (C) 2023 The Authors.

关键词： Security and privacy model predictive and optimization-based control predictive control

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An Effective Proposal to Reliable Forward Velocity Variation of NMPC-based Visual Path-Following control

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JOURNAL OF control AUTOMATION AND ELECTRICAL SYSTEMS 2022年第5期33卷 1376-1388页

作者： Ribeiro, Tiago T. Franco, Iago Jose P. B. Conceicao, Andre Gustavo S. Univ Fed Bahia Dept Elect & Comp Engn LaR Robot Lab Salvador BA Brazil

Vision-based control has become an interesting alternative for increasing the autonomy of mobile robot navigation in many real scenarios. Classic path-following models did not originally predict a metric for reference forward velocity variation, and this becomes an even more pronounced problem when using visual techniques that are very sensitive to parameter calibration, such as curvature. This paper proposes a novel approach to reliable forward velocity variation in NMPC (nonlinear model predictive control)-based visual path-following controllers, directly from the image plane. The main contribution arises as improvements in the image processing stage for the acquisition of practicable reference velocities and a new state capable of capturing the characteristics of the path and calculating, at runtime, an optimal forward velocity capable of safely driving the robot around the visual path. The new set of internal control inputs defined for the NMPC framework allows the application of a computationally efficient technique to handle feasibility through the relaxation of input and state constraints. Simulations and experimental results with the Husky UGV platform navigating on an imperfect visual reference path and with an arbitrary curvature profile demonstrate the correctness of the proposed method.

关键词： Mobile robots Vision-based control Robot navigation nonlinear predictive control model predictive and optimization-based control

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A Unified MPC Envelope control Formulation for Toyota Guardian and Chauffeur 18

A Unified MPC Envelope Control Formulation for Toyota Guardi...

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18th IFAC Workshop on control Applications of optimization (CAO)

作者： Bobier-Tiu, Carrie G. Koehler, Sarah M. Brown, Matthew Ahumada, Manuel Toyota Res Inst Los Altos CA 94022 USA

For a vehicle that can execute safety and autonomy technologies in two paradigms shared control (Guardian) and autonomous control (Chauffeur) we present a unified formulation for chassis control in both paradigms. The primary goal for both is to maintain the vehicle within a safe state space, and secondarily track either a human driver's intention through shared control, or a desired trajectory in autonomous operation. We introduce the notion of a pseudo-driver when in the autonomous mode, which mathematically mimics the human driver by using the desired path to generate a "driver's" steering angle and acceleration commands. This pseudo-driver allows for an identical controller formulation between both modes. Quantified experimental results are presented. Copyright (C) 2022 The Authors.

关键词： model predictive and optimization-based control Autonomous Vehicles Human and vehicle interaction Motion control Safety

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Hourly operation of a regulated lake via model predictive control 2nd

Hourly operation of a regulated lake via Model Predictive Co...

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2nd IFAC Workshop on control Methods for Water Resource Systems (CMWRS)

作者： Cestari, Raffaele G. Castelletti, Andrea Formentin, Simone Politecn Milan Dipartimento Elettron Informaz & Bioingn Via G Ponzio 34-5 I-20133 Milan Italy

The optimal operation of regulated lakes is a challenging task involving conflicting objectives, ranging from controlling lake levels to avoid floods and low levels to water supply downstream. The traditional approach to operation policy design is based on an offline optimization, where a feedback control rule mapping lake storage into daily release decisions is identified over a set of observational data. In this paper, we propose a receding-horizon policy for a more frequent, online regulation of the lake level, and we discuss its tuning as compared to benchmark approaches. As side contributions, we provide a daily alternative based on the same rationale, and we show that this is still valid under some assumptions on the water inflow. Numerical simulations are used to show the effectiveness of the proposed approach. We demonstrate the approach on the regulated lake Como, Italy. Copyright (C) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/).

关键词： model predictive and optimization-based control Optimal operation of water resources systems control of constrained systems

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First-Order Constrained optimization: Non-smooth Dynamical System Viewpoint 18

First-Order Constrained Optimization: Non-smooth Dynamical S...

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18th IFAC Workshop on control Applications of optimization (CAO)

作者： Schechtman, Sholom Tiapkin, Daniil Moulines, Eric Jordan, Michael, I Muehlebach, Michael Ecole Polytech CMAP Paris France HSE Univ HDILAB Moscow Russia Univ Calif Berkeley Berkeley CA USA MPI IS Tubingen Germany

In a recent paper, Muehlebach and Jordan (2021a) proposed a novel algorithm for constrained optimization that uses original ideals from nonsmooth dynamical systems. In this work, we extend Muehlebach and Jordan (2021a) in several important directions: (i) we provide existence and convergence results for continuous-time trajectories under general conditions, and (ii) we provide a convergence guarantee for a perturbed version of the discrete-time version of the algorithm (covering stochastic gradient updates), for nonconvex and nonsmooth objective functions. Our analysis framework rationalizes the continuous-time and discrete-time cases, which not only provides an important intuition but could also enable convergence proofs for accelerated or Newton-like versions of our algorithm. Copyright (C) 2022 The Authors.

关键词： Large scale optimization problems Static optimization problems model predictive and optimization-based control

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Stability of Progressively Tightening model predictive control in Continuous Time

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IFAC-PapersOnLine 2024年第18期58卷 349-355页

作者： Katrin Baumgärtner Moritz Diehl

We consider a continuous-time nonlinear model predictive control formulation that is progressively tightening in path costs and constraints. Under standard assumptions, we prove asymptotic stability of the origin for the corresponding closed-loop system and extend this result to formulations employing an auxiliary dynamic system. The theoretical results are illustrated on a numerical example.

关键词： model predictive and optimization-based control stability of nonlinear systems

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Approximate SDD-TMPC with Spiking Neural Networks: An Application to Wheeled Robots

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IFAC-PapersOnLine 2024年第18期58卷 323-328页

作者： F. Surma A. Jamshidnejad Control and Operations Department TU Delft 2629 HS Delft The Netherlands

model predictive control (MPC) optimizes an objective function within a prediction window under constraints. In the presence of bounded disturbances, robust versions are used. Recently, a promising robust MPC was introduced that outperforms SOTA approaches. However, solving the optimization problem online is computationally expensive. An efficient approximation method, such as neural networks (NN), can be substituted to accelerate the online computation. There are discrepancies between the control inputs due to the approximation. We propose to model them as bounded state-dependent disturbances to robustly control nonlinear wheeled robots. We consider a spiking NN to ensure that small robots could use it.

关键词： model predictive and optimization-based control Robust learning systems Robotics Nonlinear predictive control Neural networks

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Economic Nonlinear model predictive control of Continuous Viral Bioreactors

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IFAC-PapersOnLine 2024年第18期58卷 238-243页

作者： Pavan K. Inguva Luc T. Paoli Richard D. Braatz

Viral particle systems are integral parts of modern biotechnology, finding use in vaccines, drug delivery platforms, and recombinant protein production. Continuous manufacturing of these systems can offer improved manufacturability and quality control. However, viral systems often have complex kinetics which can introduce undesirable process dynamics and lower product titers in continuous operation. This article explores the use of economic nonlinear dynamic optimization and model predictive control to achieve multiple process objectives such as maximizing productivity and/or purity. Economic nonlinear model predictive control is also demonstrated to robustly control the bioreactor under plant-model mismatch in different scenarios.

关键词： Dynamics and control Nonlinear process control model predictive and optimization-based control Advanced manufacturing Industrial biotechnology

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model-predictive control of irrigation - a case study in India

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IFAC-PapersOnLine 2023年第2期56卷 9912-9917页

作者： Dilpreet Singh Sankaranarayanan Subramanian Sebastian Engell

Agriculture is a major consumer of freshwater, in particular in countries like India. From the perspectives of crop yield and water conservation, the development of advanced model-based irrigation scheduling methods is desirable. Most of the farming in India is done by small-scale farmers and to serve their needs, we are developing a simple model-based system that can be applied semi-manually and does not need expensive sensors and costly data. In this article, an optimal irrigation system, based on model predictive control (MPC), which uses a physics-based model to compute an optimal amount of irrigation for the next day, is developed. The robustness of the developed system is tested by comparing the effect of errors in a key model parameter as well as in the weather forecast.

关键词： modeling and control of agriculture model predictive and optimization-based control precision agriculture

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