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 ut...
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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.
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 ...
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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
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 consist...
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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.
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 ut...
详细信息
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.
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-linearmodel predict...
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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.
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 ...
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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 modelpredictivecontroller 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.
This paper presents a predictivecontrol 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-con...
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This paper presents a predictivecontrol 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.
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 ...
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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.
In this work, the authors have proposed a constrained nonlinearmodelpredictivecontrol (NMPC) scheme for a boiler-turbine system. The proposed control scheme relies on the non-linear state space model proposed by Be...
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In this work, the authors have proposed a constrained nonlinearmodelpredictivecontrol (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.
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 tai...
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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.
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