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non-linear model predictive control of Cabin Temperature and Air Quality in Fully Electric Vehicles

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第2期70卷 1216-1229页

作者： Glos, Jan Otava, Lukas Vaclavek, Pavel Brno Univ Technol CEITEC Cent European Inst Technol Brno 61200 Czech Republic Brno Univ Technol Fac Elect Engn & Commun Brno 61200 Czech Republic

This article describes an application of non-linear model predictive control algorithms on energy efficient control of fully electric vehicle cabin temperature and air quality. Since fully electric vehicles can not utilize waste heat from a powertrain (or there is not enough waste heat) as ICE vehicles do, it is necessary to employ advanced control approaches (especially for cabin heating) due to the possible mileage lost by using energy from the batteries for cabin conditioning. The basic idea behind this is to avoid the heat losses caused by excessive air exchange and to ensure a satisfactory air quality in combination with a user defined temperature. The non-linear model predictive control algorithms were successfully implemented into an Infineon AURIX Tricore microcontroller and tested within a Processor in the Loop simulation.

关键词： Heating systems Resistance heating Waste heat Atmospheric modeling Air quality Heat pumps Temperature control Air quality control battery electric vehicle extended kalman filter fully electric vehicle non-linear model predictive control temperature control vehicle cabin model

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non-linear model predictive control based trajectory tracking of hand and wrist motion using functional electrical stimulation

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control ENGINEERING PRACTICE 2024年 146卷

作者： Karak, Tarun Basak, Souvik Joseph, Patrick A. Sengupta, Somnath Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur 721302 West Bengal India Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur 721302 West Bengal India

Functional Electrical Stimulation (FES) on muscles can recover voluntary motions of the upper limb paralysed patients, enabling them to conduct daily life activities like grasping, holding, etc. FES can stimulate the forearm muscles to track joint trajectory while avoiding hyper -stimulation. While using FES, model -based closed -loop control schemes can track the desired trajectory more effectively than open -loop control strategies. In literature, the model -based technique, namely, the iterative learning control (ILC)-based closed -loop control strategy, is used to track joint motion towards the intended trajectory through FES, utilizing a biomechanical model of the hand and wrist. However, such a technique has limitations pertaining to robust optimal design, identical initialization, sampling, etc. The non-linear model predictive control (NMPC) framework can address these issues, as it is suitable for capturing the system's inherent non-linear dynamics and handling multiinput multi -output (MIMO) systems along with constraints. In this work, two variants of NMPC, with fixed weights (NMPC 1) and fuzzy logic -based auto -tuned weights (NMPC 2) of the cost function, considering an existing planar biomechanical model of the hand and wrist, are proposed and demonstrated in a simulation environment. However, in NMPC 1, the weights of the cost function have to be tuned manually by the hitand -trial method. To address this issue, NMPC 2 has been developed with fuzzy logic -based adaptive weights in the cost function. The developed controllers actuate individual muscles using FES to simultaneously track the angular positions of three joints: wrist, Metacarpophalangeal (MCP), and Proximal Interphalangeal (PIP) of the non-linear MIMO system (hand and wrist) while considering input constraints. Both controllers' tracking performances are compared against an ILC-based control strategy. The results demonstrate that NMPC 1 and NMPC 2 outperform the ILC-based control approach r

关键词： Functional electrical stimulation non-linear model predictive control Biomechanical modelling Musculotendon system Iterative learning control

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non-linear model predictive control of a Quadruple-Tank Process 13

Non-linear Model Predictive Control of a Quadruple-Tank Proc...

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13th UK-Automatic-control-Council (UKACC) International Conference on control (control)

作者： Zhang, Haoran Prempain, Emmanuel Univ Leicester Sch Engn Leicester Leics England

ISBN: (纸本)9781665452007

This paper presents a non-linear model predictive controller for offset-free tracking and disturbance rejection of arbitrary constant (or piecewise-constant) set-points and/or disturbances. The control problem consists of regulating the non-linear plant dynamics augmented with the integral of the tracking error of the variables to be controlled. This simple approach offers tracking and disturbance rejection against unknown set-points and/or disturbances. The proposed approach is successfully applied to a highly non-linear, coupled, water-tank process which exhibits both minimum and non-minimum phase characteristics.

关键词： non-linear model predictive control Integral control Water-Tank Process

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non-linear model predictive control of Cabin Temperature and Air Quality in Fully Electric Vehicles

Non-Linear Model Predictive Control of Cabin Temperature and...

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作者： Glos, Jan Brno University of Technology

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Modified Volterra model-based non-linear model predictive control of IC engines with real-time simulations

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2017年第2期39卷 208-223页

作者： Shi, Yiran Yu, Ding-Li Tian, Yantao Shi, Yaowu Jilin Univ Coll Commun Engn Changchun Peoples R China Liverpool John Moores Univ Sch Engn Byrom St Liverpool L3 3AF Merseyside England

modelling of non-linear dynamics of an air manifold and fuel injection in an internal combustion (IC) engine is investigated in this paper using the Volterra series model. Volterra model-based non-linear model predictive control (NMPC) is then developed to regulate the air-fuel ratio (AFR) at the stoichiometric value. Due to the significant difference between the time constants of the air manifold dynamics and fuel injection dynamics, the traditional Volterra model is unable to achieve a proper compromise between model accuracy and complexity. A novel method is therefore developed in this paper by using different sampling periods, to reduce the input terms significantly while maintaining the accuracy of the model. The developed NMPC system is applied to a widely used IC engine benchmark, the mean value engine model. The performance of the controlled engine under real-time simulation in the environment of dSPACE was evaluated. The simulation results show a significant improvement of the controlled performance compared with a feed-forward plus PI feedback control.

关键词： Air-fuel ratio control IC engines Volterra model non-linear model predictive control real-time simulations

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Real-time lateral stability and steering characteristic control using non-linear model predictive control

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VEHICLE SYSTEM DYNAMICS 2023年第4期61卷 1063-1085页

作者： Botes, Wian Botha, Theunis R. Els, P. Schalk Univ Pretoria Vehicle Dynam Grp Pretoria South Africa

This paper presents a non-linear integrated control strategy that primarily focuses maintaining vehicle lateral stability using active front steering and differential braking. The proposed control strategy utilises a non-linear model predictive controller to improve lateral stability. A stable linear reference model is used for reference generation. By including the understeer gradient in the reference model, different kinematic responses are obtained from the controlled vehicle. The prediction model utilises the road friction estimate to create dynamic stability constraints that include rollover and sliding of the vehicle. The design of the model predictive controller allows easy activation of different control actuators and dynamic modification to the control behaviour. The control methodology is validated using MATLAB/Simulink and a validated MSC ADAMS model. A sensitivity analysis is conducted to identify the susceptibility of the control strategy to various parameters and states.

关键词： non-linear model predictive control integrated control advanced driver assist system lateral stability sideslip angle control

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Iterated non-linear model predictive control based on tubes and contractive constraints

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ISA TRANSACTIONS 2016年 62卷 120-128页

作者： Murillo, M. Sanchez, G. Giovanini, L. Natl Sci & Tech Res Council CONICET Res Inst Signals Syst & Computat Intelligence Sin Ciudad Univ UNL4 Piso FICHS3000 Santa Fe Argentina

This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle. (C) 2016 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Contractive constraint Iterative process non-linear model predictive control Robust model predictive control

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A non-linear model predictive control strategy to minimise mechanical effects of lithium-ion

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JOURNAL OF ENERGY STORAGE 2023年 71卷

作者： Samad, Hudair Malik, M. S. S. Gulzar, M. Queen Mary Univ London Sch Engn & Mat Sci London E1 4RB England Univ Engn & Technol Taxila Mech Engn Dept Taxila Pakistan

Mechanical stresses are not only developed inside the active material of the composite electrode, but also in the solid electrolyte interface layer (SEI) due to the graphite expansion. Due to diffusion induced stress (DIS) phenomena, stresses in the negative electrode are generated during charging and discharging cycles. This article presents the proposed optimal charging profile explicitly incorporating the effects of mechanical degradation. non-linear model predictive control approach along with Gauss pseudo-spectral method is used to optimise charging trajectories. It is assumed that active material is not the weakest material but system is modelled based on SEI break and repair effect. The dynamics of battery is represented by a single particle model. The simulated results are compared with benchmark constant current constant voltage (CCCV) strategy. Furthermore, proposed methodology is compared with optimal CCCV using cycle life ageing experimental scenario. It is estimated that stress amplitude decreased by 7% and nominal capacity increased by 11% using the proposed optimal charging profile.

关键词： Optimal charging non-linear model predictive control Lithium-ion battery Pseudo-spectral methods

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Derivative-free Estimator Based non-linear model predictive control of a Boiler-Turbine Unit

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IFAC Proceedings Volumes 2013年第32期46卷 660-665页

作者： S. Kapil Arasu J. Prakash Vinay Prasad Department of Instrumentation Engineering Madras Institute of Technology Campus Anna University Chennai-44.**India Department of Chemical and Materials Engineering University of Alberta Canada

In this work, the authors have proposed a constrained nonlinear model predictive control (NMPC) scheme for a boiler-turbine system. The proposed control scheme relies on the non-linear state space model proposed by Bell and Astrom, 1987 for prediction. A derivative-free Kalman filter has been designed to estimate the state variables of the boiler-turbine unit. These estimated values have been used as an initial condition for predicting the future states and outputs in the NMPC formulation. In order to account for model-plant mismatch and to achieve offset-free control, innovation based correction of predicted states and outputs as suggested by Ricker, 1990 has been incorporated in the proposed NMPC formulation. The extensive simulation studies show that the proposed control scheme effectively handles the input constraints of the boiler-turbine unit and meets the required electrical demand without requiring careful selection of operating points.

关键词： Derivative free Kalman Filter Boiler-turbine system non-linear model predictive control

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Developing a resilient and sustainable non-linear closed-loop supply chain management framework for the automotive sector industry using a gaussian fuzzy optimization based non-linear model predictive control approach

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2024年第2期41卷 101-120页

作者： Khot, Sachin B. Thiagarajan, S. Vellore Inst Technol Sch Mech Engn Vellore India Shivaji Univ Rajarambapu Inst Technol Dept Mech Engn Kolhapur India Vellore Inst Technol Sch Mech Engn Vellore 632014 India

Efforts to merge sustainability and resilience within the automotive industry's supply chain models have proven challenging. This paper proposes a novel non-linear closed-loop supply chain management framework tailored to the tire industry supply chain from the automotive sector to address the issue of exploring interrelationships. Framework employs trapezoidal linguistic cubic fuzzy Z-score technique for order of preference by similarity to the ideal solution ranking approach to prioritize resilience strategies to maintain sustainability performance during sudden disturbances. Furthermore, Gaussian fuzzy optimization-based non-linear model predictive control acts as a feedback controller to integrate sustainability and resilience by providing a stable output based on the objective function related to sustainability dimensions. An experimental study assesses the impact of resilience strategies on total supply chain costs, highlighting significant cost savings. Adopting strategies like multiple sourcing, information sharing, and improved design quality of the supply chain keeps total expected costs optimal for various sustainability levels.

关键词： Resilience strategy sustainable supply chain closed-loop supply chain non-linear model predictive control fuzzy optimal control

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