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Economic Model Predictive control for Cryogenic Air Separation Unit Startup

IFAC-PapersOnLine 2024年第14期58卷 761-766页

作者： Anthony W.K. Quarshie José Matias Christopher L.E. Swartz Department of Chemical Engineering McMaster University 1280 Main Street West Hamilton Ontario Canada L8S 4L7

Current energy market trends incentivize frequent optimal load changes, including the startup of cryogenic air separation units (ASUs), which are large electricity consumers. In this study, we assess the potential benefits of using an economic nonlinear model predictive control (ENMPC) framework for the optimal startup of ASUs in the presence of a measured process disturbance. We also considered strategies for improving the solution computational speed of the ENMPC problem. Our case study shows substantial profit recovery by the control strategy relative to offline pre-computed optimal inputs in response to the disturbance.

关键词： Disturbance rejection optimization-based control discontinuity smoothing

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A Koopman-based approach for torque vectoring in electric vehicles

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IFAC-PapersOnLine 2024年第28期58卷 546-551页

作者： Andrea Sassella Basilio Lenzo Department of Industrial Engineering University of Padua Via Venezia 1 Padua Italy

Torque Vectoring plays a pivotal role in enhancing vehicle dynamics and performance. In electric vehicles with multiple motors, the torque at each wheel may be controlled independently, offering significant opportunity to enhance safety and stability. This study explores the application of Koopman-based Model Predictive control (MPC) in torque vectoring systems. By considering an electric vehicle equipped with four in-wheel motors, a linear Koopman model is identified from data. The model is then used to formulate a linear MPC. The performance of the scheme is assessed through numerical simulations.

关键词： Koopman control Vehicle control Torque Vectoring Nonlinear predictive control Model predictive optimization-based control Nonlinear optimal automotive control

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Design of cost functions for the real-time control of microgrids hosting distributed energy-storage systems

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2023年 35卷

作者： Grammatikos, Plouton Paolone, Mario Le Boudec, Jean-Yves Ecole Polytech Fed Lausanne Distributed Elect Syst Lab Lausanne Switzerland Ecole Polytech Fed Lausanne Lab Commun & Applicat Lausanne Switzerland

The research of this paper is framed in the context of multi-layered, optimization-based approaches for microgrid control. Specifically, we focus on the design of real-time objectives that are typically proxies of long-term ones. Examples include tracking a predetermined dispatch plan or a battery state of-charge trajectory while satisfying the grid's operational constraints. If there is no formalized way of choosing the cost functions that reflect these objectives, an arbitrary choice could lead to a biased control in favor of certain objectives. The common approaches of configuring microgrid controllers usually rely on "oracle-based"approaches assumed to know exactly the studied scenarios. In this paper, we formalize the design goals of a real-time microgrid-control system by employing distributed energy-storage systems, and we give guidelines on how to design cost functions that satisfy them. Our method requires only certain parameters that can be chosen intuitively and gives a priori insight on the controller's behavior. The application of the theoretical work is verified in multiple scenarios by simulations performed using a realistic model of a microgrid.& COPY;2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Real-time control optimization-based control Objective balancing Energy storage systems Microgrids

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Regularized Interior Point Methods for Constrained optimization and control

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IFAC-PapersOnLine 2023年第2期56卷 1247-1252页

作者： Alberto De Marchi University of the Bundeswehr Munich Department of Aerospace Engineering Institute of Applied Mathematics and Scientific Computing Neubiberg/Munich Germany

Regularization and interior point approaches offer valuable perspectives to address constrained nonlinear optimization problems in view of control applications. This paper discusses the interactions between these techniques and proposes an algorithm that synergistically combines them. Building a sequence of closely related subproblems and approximately solving each of them, this approach inherently exploits warm-starting, early termination, and the possibility to adopt subsolvers tailored to specific problem structures. Moreover, by relaxing the equality constraints with a proximal penalty, the regularized subproblems are feasible and satisfy a strong constraint qualification by construction, allowing the safe use of efficient solvers. We show how regularization benefits the underlying linear algebra and a detailed convergence analysis indicates that limit points tend to minimize constraint violation and satisfy suitable optimality conditions. Finally, numerical results indicate that the combined approach compares favorably, in terms of robustness, against both interior point and augmented Lagrangian codes.

关键词： Nonlinear programming interior point methods augmented Lagrangian proximal algorithms optimization-based control

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System Identification for Safe controllers using Inverse optimization

System Identification for Safe Controllers using Inverse Opt...

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Modeling, Estimation and control Conference (MECC)

作者： Grover, Jaskaran Liu, Changliu Sycara, Katia Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA

This paper presents algorithms for learning parameters of optimization-based controllers used in multiagent systems based on their position-velocity measurements. The motivation to learn these parameters stems from the need to infer an agent's intent (human or robot) to facilitate accurate predictions of motion as well as efficient interactions in a multiagent system. In this work, we demonstrate how to perform inference using algorithms based on the theory of inverse optimization (IO). We propose QP-based reformulations of I0 algorithms for faster processing of batch-data to facilitate quicker inference. In our prior work, we used persistency of excitation analysis for deriving conditions under which conventional estimators such as a Kalman filter can successfully perform such inference. In this work, we demonstrate that whenever these conditions are violated, inference of parameters will fail, be it using IO-based algorithms or a UKF. We provide numerical simulations to infer desired goal locations and controller gains of each robot in a multirobot system and compare performance of IO-based algorithms with a UKF and an adaptive observer. In addition to these, we also conduct experiments with Khepera-4 robots and demonstrate the power of IO-based algorithms in inferring goals in the presence of perception noise. Copyright (C) 2021 The Authors.

关键词： Multiagent systems optimization-based control Robotics

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Fixed-time control under spatiotemporal and input constraints: A Quadratic Programming based approach

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AUTOMATICA 2022年 141卷

作者： Garg, Kunal Arabi, Ehsan Panagou, Dimitra Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA Ford Motor Co Res & Adv Engn Dearborn MI 48121 USA

This paper presents a control synthesis framework for a general class of nonlinear, control-affine systems under spatiotemporal and input constraints. First, we study the problem of fixed-time convergence in the presence of input constraints. The relation between the domain of attraction for fixed-time stability with respect to input constraints and the required time of convergence is established. It is shown that increasing the control authority or the required time of convergence can expand the domain of attraction for fixed-time stability Then, we consider the problem of finding a control input that confines the closed-loop system trajectories in a safe set and steers them to a goal set within a fixed time. To this end, we present a Quadratic Programming (QP) formulation to compute the corresponding control input. We use slack variables to guarantee the feasibility of the proposed QP under input constraints. Furthermore, when strict complementary slackness holds, we show that the solution of the QP is a continuous function of the system states and establish the uniqueness of closed-loop solutions to guarantee forward invariance using Nagumo's theorem. To corroborate our proposed methods, we present two case studies, an example of an adaptive cruise control problem and a two-robot motion planning problem. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Fixed-time stability Constrained control Nonlinear systems optimization-based control

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Model Predictive control for a Mecanum-wheeled Robot Navigating among Obstacles 7

Model Predictive Control for a Mecanum-wheeled Robot Navigat...

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7th IFAC Conference on Nonlinear Model Predictive control (NMPC)

作者： Moreno-Caireta, Inigo Celaya, Enric Ros, Lluis UPC FME Barcelona Spain UPC CSIC Inst Robot & Informat Ind Barcelona Spain

Mecanum-wheeled robots have been thoroughly used to automate tasks in many different applications. However, they are usually controlled by neglecting their dynamics and relying only on their kinematic model. In this paper, we model the behaviour of such robots by taking into account both their equations of motion and the electrodynamic response of their actuators, including dry and viscous friction at their shafts. This allows us to design a model predictive controller aimed to minimise the energy consumed by the robot. The controller also satisfies a number of non-linear inequalities modelling motor voltage limits and obstacle avoidance constraints. The result is an agile controller that can quickly adapt to changes in the environment, while generating fast and energy-efficient manoeuvres towards the goal. Copyright (C) 2021 The Authors.

关键词： Dynamic modelling of wheeled robots model predictive control motion control mobile robot trajectory and path planning optimization-based control obstacle avoidance energy efficiency Mecanum wheels directional sliding wheels

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

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IFAC-PapersOnLine 2022年第16期55卷 236-241页

作者： Sholom Schechtman Daniil Tiapkin Eric Moulines Michael I. Jordan Michael Muehlebach CMAP Ecole Polytechnique HDILAB HSE University Moscow UC Berkeley MPI-IS Tuebingen

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.

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

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A Unified MPC Envelope control Formulation for Toyota Guardian and Chauffeur

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IFAC-PapersOnLine 2022年第16期55卷 19-24页

作者： Carrie G. Bobier-Tiu Sarah M. Koehler Matthew Brown Manuel Ahumada Toyota Research Institute 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.

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

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A Review of the Integrated Design and control of Electrified Vehicles

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ENERGIES 2020年第20期13卷 5454-5454页

作者： Wei, Caiyang Hofman, Theo Ilhan Caarls, Esin van Iperen, Rokus Eindhoven Univ Technol Dept Mech Engn POB 513 NL-5600 MB Eindhoven Netherlands Bosch Transmiss Technol Postbus 500 NL-5000 AM Tilburg Netherlands

From a control perspective, the energy management system and the thermal management system of an electrified vehicle are often developed separately, which may not yield the optimal solution. Moreover, an optimal system design requires concurrent plant (topology and size) and controller optimization, which should apply to both energy and thermal domains. This paper originally provides a comprehensive analysis of design and control optimization layers to reveal the interconnections between them and how they influence the optimality of an electrified vehicle design considering both energy and thermal domains. It was found that energy and cost savings can be achieved by integrating these optimization layers, and the energy and thermal domains with four coordination schemes, namely, sequential, iterative, nested and simultaneous. There is a trade-off between optimality, causality, complexity and computational time. Additionally, future research directions in terms of reducing energy consumption and system costs of electrified vehicles are identified herein, such as using integrated design and control methods, employing electrified actuators, exchanging heat between powertrain components and utilizing waste heat recovery systems.

关键词： electrified vehicle energy management thermal management topology optimization component sizing rule-based control optimization-based control

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