检索结果-内蒙古大学图书馆

22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Zarzycki, Krzysztof Lawrynczuk, Maciej Warsaw Univ Technol Inst Control & Computat Engn Fac Elect & Informat Technol Ul Nowowiejska 15-19 PL-00665 Warsaw Poland

ISBN: (纸本)9781713872344

This work describes a novel Physics-Informed Hybrid Neural Network (PIHNN) model structure based on the Gated Recurrent Unit (GRU) neural network that combines the first principle and black-box data-driven approaches. We recommend the presented modeling method when the measurement of process variables is possible, but only in some limited neighborhood of the operating points, and the available first principle model describing the process is generally correct but not accurate. We discuss three methods of data fusion. Two of them utilize the fuzzy approach, while the third one relies on an additional neural network of the Multi-Layer Perceptron (MLP) type. The first method assumes a simplified data fusion block, while the next two use machine learning to minimize the overall model error. The efficiency of the presented approach and data fusion techniques is demonstrated for a benchmark chemical process (the pH reactor). Finally, we consider applying the PIHNN model in Model Predictive control (MPC). It gives much better control quality than the MPC controller relying on the entirely black-box GRU model. Copyright (c) 2023 The Authors.

关键词： computational intelligence in control Physics-Informed Neural Networks GRU Model Identification Model Predictive control

来源：评论

学校读者我要写书评

暂无评论

Neural-Adaptive Switching control of Task-Space Objectives on Strict-Feedback Robot 22

Neural-Adaptive Switching Control of Task-Space Objectives o...

引用

22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Doctolero, Samuel C. Westwick, David T. Univ Calgary Schulich Sch Engn Calgary AB Canada

ISBN: (纸本)9781713872344

Switching between tasks in robot applications is necessary for autonomous behaviour, requiring a control framework for switching between objectives. Thus, this paper proposes a switching control framework that allows transitions in position-dependent objectives for robot systems in strict-feedback form with adaptive capabilities. The proposed controller integrates artificial neural networks in two ways;one approximates control transforms while another approximates additive terms. Furthermore, the proposed method includes a dynamic damped inverse to ensure the invertible control transform matrices. The proposed controller is derived using the backstepping method and is stable in a Lyapunov sense. The proposed task-switching controller is first demonstrated on a generic robot manipulator. Then, a simulated two-degree-of-freedom manipulator with a camera at the end-effector tasked with position tracking and visual servoing verifies the capabilities of the proposed controller. A baseline switching controller shows that the proposed controller has superior performance, specifically, root mean square (RMS) error and control effort. Also, executing the simulation for multiple cycles shows that the proposed controller's artificial neural network and adaptive laws are *** (c) 2023 The Authors.

关键词： Adaptive and Learning Systems Non-Linear control Systems computational intelligence in control Robotics control Design

来源：评论

学校读者我要写书评

暂无评论

Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework 22

Safe Optimization of an Industrial Refrigeration Process Usi...

引用

22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Korkmaz, Buse Sibel Zagorowska, Marta Mercangoz, Mehmet Imperial Coll London London SW7 2AZ England Swiss Fed Inst Technol CH-8092 Zurich Switzerland

ISBN: (纸本)9781713872344

Real-time optimization can play a key role for improving the performance of industrial processes, but it becomes challenging in cases where the process characteristics are uncertain, particularly when the uncertain characteristics impact safety-related constraints. In this study, we present an adaptive and explorative real-time optimization framework that can effectively learn the characteristics of an industrial refrigeration plant through externally driven changes in cooling load targets and through self-exploration. We utilize Gaussian processes (GP) to facilitate learning unknown compressor characteristics of the plant and we leverage the uncertainty quantification of the GP to drive exploration using a weighted sum term in the objective function for real-time optimization. Furthermore, the uncertainty information is used to probabilistically enforce the maximum total power consumption constraint for the compressors with high confidence at all times. Our simulated experiments demonstrate that the proposed approach safely enhances the energy efficiency of the refrigeration process, closely approximating the performance of a best-case solution that has complete information about the plant performance characteristics. We also demonstrate the impact of varying the exploration term in the solution and how the uncertainty of plant behaviour is reduced even in the absence of cooling load target changes. Copyright (c) 2023 The Authors.

关键词： Bayesian methods Statistical inference computational intelligence in control Machine learning Optimal operation and control of power systems.

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Model Predictive control with L1 Cost-Function Using Neural Networks for Multivariable Processes 22

Nonlinear Model Predictive Control with L1 Cost-Function Usi...

引用

22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Nebeluk, Robert Lawrynczuk, Maciej Warsaw Univ Technol Inst Control & Computat Engn Fac Elect & Informat Technol Ul Nowowiejska 15-19 PL-00665 Warsaw Poland

ISBN: (纸本)9781713872344

This work presents a computationally efficient Model Predictive control (MPC) algorithm for nonlinear Multiple Input Multiple Output (MIMO) processes in which the sum of absolute values of predicted control errors (the L-1 norm) is minimized rather than the typically used sum of squared errors (the L-2 norm). An approximator of the absolute value function combined with an advanced online trajectory linearization scheme is used to obtain a computationally uncomplicated algorithm that requires solving online quadratic optimization tasks. For a nonlinear MIMO neutralization process, we show that the described algorithm gives control quality comparable to that possible in MPC with nonlinear optimization. We examine the effectiveness of polynomial and neural approximation of the absolute value function. Moreover, we show that the described algorithm gives better control quality than the classical approach to MPC with the L-2 cost-function.

关键词： computational intelligence in control Neural Networks Model Predictive control

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Battery Cycle Life Early Prediction Using Machine Learning Pipeline 22

Comparative Analysis of Battery Cycle Life Early Prediction ...

引用

22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Zhang, Huang Altaf, Faisal Wik, Torsten Gros, Sebastien Volvo Grp Trucks Technol Dept Electromobil Gothenburg Sweden Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden Norwegian Univ Sci & Technol Dept Engn Cybernet Trondheim Norway

ISBN: (纸本)9781713872344

Lithium-Ion battery system is one of the most critical but expensive components for both electric vehicles and stationary energy storage applications. In this regard, accurate and reliable early prediction of battery lifetime is important for optimizing life cycle management of batteries from cradle to grave. In particular, accurate aging diagnostics and prognostics is crucial for ensuring longevity, performance, safety, uptime, productivity, and profitability over a battery's lifetime. However, current state-of-art methods do not provide satisfactory prediction performance (lack of uncertainty quantification) using early degradation data. In the present work, to produce the best model for both battery cycle life point prediction and range prediction (i.e., confidence intervals or prediction intervals), a pipeline-based approach is proposed, in which a full 33-feature set is generated manually based on battery degradation knowledge, and then used to learn the best model among five machine learning (ML) models that have been reported in the battery lifetime prediction literature, and two quantile regression models for battery cycle life prediction. The calibration and sharpness property of battery cycle life range prediction is properly evaluated by their coverage probability and width respectively. The experimental results show that the gradient boosting regression tree model provides the best point prediction performance, while the quantile regression forest model provides the best range prediction performance with both full 33-feature set and the MIT 6-feature set.

关键词： computational intelligence in control lithium-ion battery cycle life early prediction uncertainty quantification prediction intervals

来源：评论

学校读者我要写书评

暂无评论

Fast Training of Neural Affine Models for Model Predictive control: An Explicit Solution 22

Fast Training of Neural Affine Models for Model Predictive C...

引用

22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Lawrynczuk, Maciej Warsaw Univ Technol Fac Elect & Informat Technol Inst Control & Computat Engn Ul Nowowiejska 15-19 PL-00665 Warsaw Poland

ISBN: (纸本)9781713872344

This work describes a nonlinear affine model developed especially for prediction in Model Predictive control (MPC). A multi-model structure is used in which independent sub-models compute predictions for the consecutive sampling instants. All sub-models are affine with respect to the future values of the manipulated variable that are calculated in MPC. Model coefficients are time-varying;they are determined online by a neural network of the Radial Basis Function type. It is proved that the described model configuration makes it possible to formulate the training task as a least squares problem that has an explicit solution (the global optimum). A chemical reactor benchmark is considered to show advantages of the discussed modeling approach and the resulting MPC scheme. As a result of model affinity, MPC requires solving online simple quadratic optimization tasks.

关键词： computational intelligence in control Neural Networks Affine Models Model Identification Model Predictive control

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Model Predictive control with L1 Cost-Function Using Neural Networks for Multivariable Processes*

引用

IFAC-PapersOnLine 2023年第2期56卷 1591-1596页

作者： Robert Nebeluk Maciej Ławryńczuk Warsaw University of Technology Faculty of Electronics and Information Technology Institute of Control and Computation Engineering ul. Nowowiejska 15/19 00-665 Warsaw Poland

This work presents a computationally Efficient Model Predictive control (MPC) algorithm for nonlinear Multiple Input Multiple Output (MIMO) processes in which the sum of absolute values of predicted control errors (the L1 norm) is minimized rather than the typically used sum of squared errors (the L2 norm). An approximator of the absolute value function combined with an advanced online trajectory linearization scheme is used to obtain a computationally uncomplicated algorithm that requires solving online quadratic optimization tasks. For a nonlinear MIMO neutralization process, we show that the described algorithm gives control quality comparable to that possible in MPC with nonlinear optimization. We examine the effectiveness of polynomial and neural approximation of the absolute value function. Moreover, we show that the described algorithm gives better control quality than the classical approach to MPC with the L2 cost-function.

关键词： computational intelligence in control Neural Networks Model Predictive control

来源：评论

学校读者我要写书评

暂无评论

Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework

引用

IFAC-PapersOnLine 2023年第2期56卷 1400-1405页

作者： Buse Sibel Korkmaz Marta Zagórowska Mehmet Mercangöz Imperial College London London SW7 2AZ UK ETH Zürich 8092 Zürich Switzerland

关键词： Bayesian methods Statistical inference computational intelligence in control Machine learning Optimal operation and control of power systems

来源：评论

学校读者我要写书评

暂无评论

Neural-Adaptive Switching control of Task-Space Objectives on Strict-Feedback Robot

引用

IFAC-PapersOnLine 2023年第2期56卷 9215-9220页

作者： Samuel C. Doctolero David T. Westwick Schulich School of Engineering University of Calgary Calgary Alberta Canada

Switching between tasks in robot applications is necessary for autonomous behaviour, requiring a control framework for switching between objectives. Thus, this paper proposes a switching control framework that allows transitions in position-dependent objectives for robot systems in strict-feedback form with adaptive capabilities. The proposed controller integrates artificial neural networks in two ways; one approximates control transforms while another approximates additive terms. Furthermore, the proposed method includes a dynamic damped inverse to ensure the invertible control transform matrices. The proposed controller is derived using the backstepping method and is stable in a Lyapunov sense. The proposed task-switching controller is first demonstrated on a generic robot manipulator. Then, a simulated two-degree-of-freedom manipulator with a camera at the end-effector tasked with position tracking and visual servoing verifies the capabilities of the proposed controller. A baseline switching controller shows that the proposed controller has superior performance, specifically, root mean square (RMS) error and control effort. Also, executing the simulation for multiple cycles shows that the proposed controller's artificial neural network and adaptive laws are stable.

关键词： Adaptive and Learning Systems Non-Linear control Systems computational intelligence in control Robotics control Design

来源：评论

学校读者我要写书评

暂无评论

Fast Training of Neural Affine Models for Model Predictive control: An Explicit Solution

引用

IFAC-PapersOnLine 2023年第2期56卷 1578-1583页

作者： Maciej Lawryńczuk Warsaw University of Technology Faculty of Electronics and Information Technology Institute of Control and Computation Engineering ul. Nowowiejska 15/19 00-665 Warsaw Poland

This work describes a nonlinear affine model developed especially for prediction in Model Predictive control (MPC). A multi-model structure is used in which independent sub-models compute predictions for the consecutive sampling instants. All sub-models are affine with respect to the future values of the manipulated variable that are calculated in MPC. Model coefficients are time-varying; they are determined online by a neural network of the Radial Basis Function type. It is proved that the described model configuration makes it possible to formulate the training task as a least squares problem that has an explicit solution (the global optimum). A chemical reactor benchmark is considered to show advantages of the discussed modeling approach and the resulting MPC scheme. As a result of model affinity, MPC requires solving online simple quadratic optimization tasks.

关键词： computational intelligence in control Neural Networks Affine Models Model Identification Model Predictive control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：