Anti-locking Braking systems are crucial safety systems in modern vehicles. In this work, we investigate the possibility to use modelpredictivecontrol (MPC) for braking systems by considering three different models ...
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ISBN:
(纸本)9781713872344
Anti-locking Braking systems are crucial safety systems in modern vehicles. In this work, we investigate the possibility to use modelpredictivecontrol (MPC) for braking systems by considering three different models identified from data. Specifically, we consider two models, whose structure and the identification procedure are driven by physics principles, and a third black-box modeling approach that relies on Koopman theory. By comparing the effectiveness of the three resulting MPC schemes in a high-fidelity simulation environment, we show that Koopman-based MPC can generally be a viable solution for the design of braking controllers, which might not be the case of nonlinear MPC or approximated scheme like the second one we test. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
Anti-locking Braking systems are crucial safety systems in modern vehicles. In this work, we investigate the possibility to use modelpredictivecontrol (MPC) for braking systems by considering three different models ...
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Anti-locking Braking systems are crucial safety systems in modern vehicles. In this work, we investigate the possibility to use modelpredictivecontrol (MPC) for braking systems by considering three different models identified from data. Specifically, we consider two models, whose structure and the identification procedure are driven by physics principles, and a third black-box modeling approach that relies on Koopman theory. By comparing the effectiveness of the three resulting MPC schemes in a high-fidelity simulation environment, we show that Koopman-based MPC can generally be a viable solution for the design of braking controllers, which might not be the case of nonlinear MPC or approximated scheme like the second one we test.
modelpredictivecontrol (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 intr...
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modelpredictivecontrol (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. Copyright (C) 2024 The Authors.
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 opportunit...
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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-basedmodelpredictivecontrol (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. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
Many large-scale multi-input multi-output systems are treated as a combination of single-input single-output systems in reality. At such times, interference from input signals not focused is regarded as observable dis...
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Many large-scale multi-input multi-output systems are treated as a combination of single-input single-output systems in reality. At such times, interference from input signals not focused is regarded as observable disturbances. For observable disturbances, feed-forward controllers are effective in rejecting the influence. A simple feed-forward controller construction is a combination of transfer functions of controlled and disturbance systems. This paper proposes an extension of the simple feed-forward controller and its parameter-tuning method. The controller is designed based on a predictive Functional controller (PFC), one of the modelpredictivecontrol (MPC). The effectiveness of the proposed scheme is verified by some simulation examples.
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 man...
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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 modelpredictivecontrol to achieve multiple process objectives such as maximizing productivity and/or purity. Economic nonlinear modelpredictivecontrol is also demonstrated to robustly control the bioreactor under plant-model mismatch in different scenarios. Copyright (C) 2024 The Authors.
We consider a continuous-time nonlinear modelpredictivecontrol formulation that is progressively tightening in path costs and constraints. Under standard assumptions, we prove asymptotic stability of the origin for ...
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We consider a continuous-time nonlinear modelpredictivecontrol 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. Copyright (C) 2024 The Authors.
A predictivemodeling framework for silicon production in fluidized bed reactors is proposed to characterize the particle size distribution of the product and the powder loss. Two different flow regime modeling approa...
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A predictivemodeling framework for silicon production in fluidized bed reactors is proposed to characterize the particle size distribution of the product and the powder loss. Two different flow regime modeling approaches are considered to describe the silane pyrolysis reaction and characterize the deposition rate that contributes to particle growth. A discrete population balance equation is used to estimate the particle size distribution as a function of the deposition rate. A nonlinear modelpredictivecontrol is then utilized to regulate the system at the desired operating conditions. Detailed open-loop and closed-loop simulation studies demonstrate the successful integration of nonlinear MPC and the proposed predictivemodeling approach.
Image-basedcontrol and sensing has been applied in a wide variety of next generation manufacturing fields. Utilizing methods of simulating closed-loop image-basedcontrol may be advantageous for improving control per...
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Image-basedcontrol and sensing has been applied in a wide variety of next generation manufacturing fields. Utilizing methods of simulating closed-loop image-basedcontrol may be advantageous for improving control performance and design without the need for an experimental setup. One software capable of these simulations is the open-source 3D modeling software Blender, which has many capabilities aided by a Python API. This work explores the use of Blender as an image-basedcontrol test bed, where both the process and the controller are simulated, in the context of a greenhouse supplemental lighting control system.
In some sporting events, including baseball, the ball flew into the spectator area, resulting in serious injuries. Having witnessed such situations, we felt that the safety of the spectator environment needed to be im...
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In some sporting events, including baseball, the ball flew into the spectator area, resulting in serious injuries. Having witnessed such situations, we felt that the safety of the spectator environment needed to be improved. The aim is, therefore, to prevent such accidents by utilizing drones to catch flying balls. Monte Carlo modelpredictivecontrol (MCMPC) is a Monte Carlo-basedcontrol method. MCMPC is sample-based and does not require a gradient in the cost function, allowing discontinuous events such as collisions with obstacles to be incorporated into the predictivemodel. This study aims to utilize MCMPC to reduce the risk of drone collision after successful flying ball capture. Copyright (C) 2024 The Authors.
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