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A Sparse Quadratic Programming Approach to Supervisory control of Multi-Zone HVAC Systems with Consideration of Peak Demand Shaving and Thermal Comfort

A Sparse Quadratic Programming Approach to Supervisory Contr...

引用

International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Pasitnat Sasananand David Banjerdpongchai Department of Electrical Engineering Faculty of Engineering Center of Excellence in Intelligent Control Automation for Process Systems Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

The HVAC system is one of the major power consuming equipment in buildings. Considering the effective setpoint temperature of each zone can increase efficiency in managing power usage in buildings, leading to the operating cost reduction. This paper presents the design of supervisory control (SC) for multi-zone HVAC systems that aims to minimize the total operating cost (TOC) and the thermal comfort cost (TCC). To minimize the TOC and TCC, both objectives are normalized and combined as a quadratic programming (QP) problem which can be efficiently solved. To achieve the design objective, two methods of SC are developed for multi-zone, namely, centralized SC and decentralized SC. We apply standard QP and sparse QP solvers using interior point method. The numerical results reveal that centralized SC performs better according to a tradeoff curve which TOC and TCC are lower. While both solvers give the same results, sparse solver can provide the solutions faster than interior point method. This paper demonstrates that centralized control is appropriate for implementation in multi-zones buildings and the sparse solver proves to be more suitable in the context of sparse QP problem.

关键词： HVAC Costs Buildings Supervisory control Telecommunications Quadratic programming Sparse matrices

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Recognizing and Tracking Person of Interest: A Real-Time Efficient Deep Learning based Method for Quadcopters

Recognizing and Tracking Person of Interest: A Real-Time Eff...

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International Conference on Recent Advances in Space Technologies (RAST)

作者： Hidayet Ersin Dursun Enes Can Güven Batuhan Avci Tufan Kumbasar Control and Automation Engineering Department Istanbul Technical University Istanbul Türkiye

The recognition and tracking of a person of interest is a crucial task in many applications, including search and rescue, security, and surveillance. This paper presents a distributed system architecture that leverages the asynchronous threading and communication property of ROS2 to develop and implement a real-time efficient Deep Learning (DL) based method for recognizing and tracking a person of interest. The DL model receives snapshots from the quadcopter's camera and sends back an information vector, which includes all recognized persons and their corresponding position information within the camera frame of the quadcopter. The person of interest tracking control system receives face set information about the person of interest and generates reference velocity signals to be tracked by low-level controllers embedded within the drone. Experiments conducted in a cluttered and complex environment demonstrate the efficiency of the DL-based architecture for quadcopters. The presented real- world results validate the effectiveness of the proposed approach in recognizing and tracking a person of interest. The experimental video is available at https://***/i7bYXnRy8Vc.

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Agent-Based Route Planning with Deep Q Learning 13

Agent-Based Route Planning with Deep Q Learning

引用

13th International Conference on Electrical and Electronics engineering, ELECO 2021

作者： Köroglu, Ali Osman Edem, Ali Emre Akmese, Serra Nur Elmas, Özge Tunc, Ilhan Soylemez, M. Turan Istanbul Technical University Control and Automation Engineering Department Istanbul Turkey

ISBN: (纸本)9786050114379

Solving traffic congestion is one of the most important and complex problems, as it causes chaos in metropolitans, especially during rush hours. Traditional methods that continue to be used have proven to be inadequate, and as a result, the developing technology has affected all areas as well as the solutions to the traffic control problem. Intelligent Transportation Systems have emerged with the development of artificial intelligence and communication technologies. This study aims to reduce time in traffic by using an agent-based route planning method with deep Q learning. An agent which acts as a taxi in the generated traffic flow is also used to demonstrate the efficiency of the proposed method in taxi service. It is aimed to be able to comprehend the actions to be implemented in order to complete the given task in an effective way with deep Q learning, considering criteria such as travel time and waiting time for passengers as performance criteria in different scenarios. © 2021 Chamber of Turkish Electrical Engineers.

关键词： Traffic congestion

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Plant Disease Detection Using Machine Learning

Plant Disease Detection Using Machine Learning

引用

Intelligent Technologies (CONIT), International Conference on

作者： Amit Kumar Parida Albert Alexander Stonier Department of Control and Automation School of Electrical Engineering VIT University Vellore School of Electrical Engineering Vellore Institute of Technology Vellore

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Plant diseases are a serious threat toworld agriculture, resulting in lower agricultural yields and financial losses. This study examines how machine learning methods could revolutionize the identification of plant diseases. The shortcomings of conventional approaches and suggest combining machine learning models with cutting-edge imaging and data processing technologies to improve the diagnosis’s speed and precision. This research demonstrates how well machine learning methods, such as CNN and SVM, can distinguish between healthy and unhealthy plants. Also gone through how transfer learning can be used, especially when data is scarce, to modify previously trained models for illness detection.

关键词： Support vector machines Plant diseases Transfer learning Imaging Data processing Data models Agriculture

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An Intelligent Overtaking Assistant System for Autonomous Vehicles

An Intelligent Overtaking Assistant System for Autonomous Ve...

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International Conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Armağan, Ersin Kumbasar, Tufan Control and Automation Engineering Department Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783030511555

Autonomously overtaking cars is one of the most challenging functionality when developing self-driving vehicles due to its safe crucial nature. This study presents a novel intelligent overtaking assistant based on fuzzy logic and its deployment into TORCS environment. For achieving this goal, the developed intelligent overtaking assistant decides when to overtake, and calculates the desired position and speed values to accomplish the overtaking action by using the relative speed and position information. Thus, to estimate the positions and speed of the other vehicles, we designed and deployed a multi object-tracking algorithm. Intelligent overtaking assistant consists of four fuzzy logic systems that have modular cascade structure. The fuzzy logic based overtaking assistant decides "when to start or end the overtaking action" and "whether speed adjustment is needed or not", and calculates "the desired lateral position on the road" and "how much speed increase is needed during overtaking action". The efficiency of the intelligent overtaking assistant is examined in TORCS environment and the presented results clearly demonstrate that its performance is highly efficient in various road structures. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy logic

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A data-driven disturbance estimation algorithm based on adaptive observer for MIMO linear systems

A data-driven disturbance estimation algorithm based on adap...

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第34届中国过程控制会议

作者： Xiaoyi Xu Hao Luo Xiao Zhang Department of Control Science and Engineering Harbin Institute of Technology Department of Automation Science and Electrical Engineering Beihang University

The adaptive observer is a well-developed datadriven algorithm for co-estimation of system parameters and ***,inevitable disturbances can affect the systems and lead to deterioration of the algorithm *** disturbance estimation methods require prior knowledge about model parameters and disturbance *** paper presents a robust adaptive observer method for multiple-inputmultiple-output linear systems subject to *** proposed method can perform joint estimation of unknown parameters,states and disturbances without any prior information about the model parameters and *** stability and convergence of the algorithm are rigorously proved to ensure that the estimation errors converge exponentially to *** results of a numerical example demonstrate the effectiveness of the algorithm.

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Assessment of Several PID controllers Applied to DC Motors 5

Assessment of Several PID Controllers Applied to DC Motors

引用

5h International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2021

作者： Mirac, Berke Kilic, Alperen Yildiz Technical University Department of Control and Automation Engineering Istanbul Turkey

ISBN: (纸本)9781665449304

DC motors are used in many industrial applications for precise speed and position control. However, load ripples and external effects on DC motor speed seriously affect system stability and efficiency. This study aims to keep DC motor speed in the reference value constant by determining the optimum PID controller parameters (Kp, Ki, and Kd). PID parameters were estimated using the traditional Ziegler Nichols method and meta-heuristic Genetic and Grey Wolf Optimization Algorithms. The Gear DC motor experimental setup was controlled with STM32F4 microcontroller using Waijung Blockset in MATLAB-Simulink. The results of the three algorithms were compared, and it has been seen that the Grey Wolf algorithm gave the best results in overshoot, settling time, rise time, and steady-state error. © 2021 IEEE.

关键词： DC motors

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Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation 63

Model predictive control for tracking using artificial refer...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Krupa, P. Kohler, J. Ferramosca, A. Alvarado, I. Zeilinger, M.N. Alamo, T. Limon, D. Computer Science Group L'Aquila Italy Institute for Dynamic Systems and Control Eth Zürich ZürichCH-8092 Switzerland University of Bergamo Information and Production Engineering Department of Management Via Marconi 5 BG Dalmine24044 Italy Universidad de Sevilla Department of Systems Engineering and Automation Seville Spain

ISBN: (纸本)9798350316339

This paper provides a comprehensive tutorial on a family of Model Predictive control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation. © 2024 IEEE.

关键词： Predictive control systems

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Improvement of the Predictive control Method for the High-Level controller 17

Improvement of the Predictive Control Method for the High-Le...

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer engineering, TCSET 2024

作者： Budashko, Vitalii Sandler, Albert Glazeva, Oksana Educational and Scientific Institute of Automation and Electrical Engineering National University 'Odessa Maritime Academy' NUOMA Odesa Ukraine National University 'Odessa Maritime Academy' NUOMA Department of the theory of automatic control and computer technology Odesa Ukraine National University 'Odessa Maritime Academy' NUOMA Department of electrical engineering and electronics Odesa Ukraine

ISBN: (纸本)9798331520564

automation of ship maneuvering in limited sailing conditions usually requires 100% redundancy of thrusters (THRs) of various modifications and their locations in accordance with the matrix. The hierarchy of the motion control system is branched by levels using high-level motion controllers and THRs control distribution algorithms, which allows for a modular design with software, where the high-level controller (HLC) can be designed without comprehensive information about the thruster motors, and the input signal branching and speed limits are handled by the motion controller. But, for certain THRs configurations, this branching leads to the decrease in control efficiency due to the limitation of data on the physical characteristics of the ships and the operation of the motion controller. This research examines different approaches to improving control efficiency using methods of nonlinear predictive control models as a basis for developing motion controllers with decision optimization based on relevant constraints. The implemented branched system is the result of solving two simple problems of ship's motion, which highlight the related problems. The use of different approaches to eliminate the identified shortcomings made it possible to develop the nonlinear controller that combines the motion controller with distributed control of the THRs and ensuring the certain level of system reliability. The modularity of the control system is provided by the expansion of the closed system, which made it possible to achieve an increase in efficiency during the combined mode of operation. The obtained solution of nonlinear control with time-varying constraints made it possible to increase the accuracy of the control with a decrease in the duration of the pause in response to the change of the disturbance within 10% © 2024 IEEE.

关键词： Predictive control systems

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A Reinforcement Learning Mission Supervisor with Memory for Human-multi-robot Coordination Systems 17th

A Reinforcement Learning Mission Supervisor with Memory for ...

引用

17th Chinese Intelligent Systems Conference, CISC 2021

作者： Mo, Zhibin Zhang, Zhenyi Chen, Yutao Huang, Jie School of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350108 China

ISBN: (纸本)9789811663192

In this paper, a novel reinforcement learning mission supervisor (RLMS) with memory is proposed for human-multi-robot coordination systems (HMRCS). The existing HMRCS are known to suffer from long decision waiting time and large mission error caused by repeated human intervention, restricting the autonomy of multi-robot systems. The proposed supervisor elaborately integrates deep-Q-network (DQN) and long-short-term memory (LSTM) knowledge base within the null-space based behavioral control (NSBC) framework, so as to achieve optimal adjustment strategy of the behavioral priority in the presence of mission conflicts, and to reduce the frequency of human intervention. In particular, the proposed RLMS with memory first memorize human intervention history when robot systems are not confident in decision making when encountering emergencies, and then reload the history information when encountering the same situation that have been tackled by human previously. Simulation demonstrates the effectiveness of proposed RLMS with memory. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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