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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

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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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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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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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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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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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An autonomous emergency braking strategy based on non-linear model predictive deceleration control

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第3期17卷 562-574页

作者： Mu, Hongyuan Li, Zebin Sun, Guozheng Li, Liang Zhao, Yongtao Mei, Mingming Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Dongfeng Motor Corp Tech Ctr Dept Intelligent Software Ctr Wuhan Peoples R China China Univ Geosci Sch Engn & Technol Beijing Peoples R China

Surrogate safety measures (SSM) are used to assess the risk for autonomous emergency braking system (AEBS). Developing appropriate SSM and accurately executing the braking request are the key issues. Time-to-collision (TTC) is a typical time-based SSM with limitations. By analyzing the braking process, this paper proposes a new SSM based on deceleration rate to avoid collision (DRAC). As the brake actuator, vehicle electronic stability control (ESC) system has many problems, such as large overshoot and pressure fluctuation. Considering the model of hydraulic control unit (HCU) and vehicle, a deceleration controller based on non-linear model predictive control (NMPC) is proposed. Based on this, a layered AEBS architecture is proposed. The upper-layer AEBS controller calculates the expected deceleration based on modified DRAC (MDRAC), and transmits it to the lower-layer NMPC deceleration controller. Finally, the simulation and experimental tests are carried out. The results show that the system has a fast and stable response. In addition, the performance of the proposed AEBS strategy is tested according to the Euro-NCAP test protocol. Comparing the results with the TTC method, the proposed method can improve the stability of the distance margin by more than 0.55 m, which ensures the safety and improves the stability of the vehicle.

关键词： autonomous emergency braking deceleration control deceleration rate to avoid collision non-linear model predictive control

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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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An Assistive Strategy for Compliantly Actuated Exoskeletons using non-linear model predictive control Method

An Assistive Strategy for Compliantly Actuated Exoskeletons ...

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Iranian Conference on Electrical Engineering

作者： S. M. Tahamipour-Z S. K. Hosseini Sani A. Akbarzadeh I. Kardan Electrical Engineering Department Ferdowsi University of Mashhad Mashhad Iran Mechanical Engineering Department Ferdowsi University of Mashhad Mashhad Iran

Assistive exoskeletons are a category of exoskeleton robots designed to provide extra powers and energies for elderlies or healthy subjects. The control algorithms of these robots, termed as assistive strategies, should control the robot in way that a portion of the user's required torques is provided by the robot actuators. This paper uses non-linear model predictive control (NMPC) method to propose a new assistive strategy for compliantly actuated exoskeletons. The user's required torques are estimated by using an identified model of the human-exoskeleton system. The NMPC method is then used to control the series elastic actuators of the robot to provide a user-selected portion of the estimated torques. The successful performance of the proposed assistive strategy is verified through multiple simulations. The force tracking performance of the NMPC method is also compared with a PID controller. The results clearly show the outperformance of the NMPC method in providing lower tracking errors and less control efforts.

关键词： Assistive control non-linear model predictive control Series elastic actuators

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