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arXiv 2022年

作者： Dai, Bolun Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

control barrier functions (CBFs) are widely used in safety-critical controllers. However, constructing a valid CBF is challenging, especially under nonlinear or non-convex constraints and for high relative degree systems. Meanwhile, finding a conservative CBF that only recovers a portion of the true safe set is usually possible. In this work, starting from a "conservative" handcrafted CBF (HCBF), we develop a method to find a CBF that recovers a reasonably larger portion of the safe set. Since the learned CBF controller is not guaranteed to be safe during training iterations, we use a model predictive controller (MPC) to ensure safety during training. Using the collected trajectory data containing safe and unsafe interactions, we train a neural network to estimate the difference between the HCBF and a CBF that recovers a closer solution to the true safe set. With our proposed approach, we can generate safe controllers that are less conservative and computationally more efficient. We validate our approach on two systems: a second-order integrator and a ball-on-beam. © 2022, CC BY.

关键词： controllers

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Intermnittent Model Predictive control of an Autonomous Underwater Vehicle

Intermnittent Model Predictive Control of an Autonomous Unde...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Quan Truong Liuping Wang P. Gawthrop School of Electrical and Computer Engineering RMIT University Melbourne VIC Australia Centre for Systems and Control and Department of mechanical Engineering University of Glasgow GLASGOW Scotland

The autonomous underwater vehicles (AUVs) have been developed over the last three decades for potential uses in scientific, commercial, environmental, and military purposes. The improvement of the computer technology has allowed the expansion of control algorithms into untethered AUVs motions. The paper applies the continuous time model predictive control algorithm designed using Laguerre functions to the dynamic motions of the vehicle. The nonlinear model of the AUVs is established in six degree of freedom and converts into a state space model. The continuous time model predictive control (MPC) algorithm, using Laguerre orthogonal functions, is extended to include intermittent features for manipulation of rudder and stern angle signals. The proposed approach allows the continuous time MPC to tolerate slower computational time and longer sampling interval

关键词： Predictive models Predictive control Underwater vehicles Military computing Continuous time systems Prediction algorithms Motion control Algorithm design and analysis Vehicle dynamics State-space methods

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Backstepping control of uncertain strict-feedback system based on time-scale separation

Backstepping control of uncertain strict-feedback system bas...

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International Conference on control, Instrumentation, and Automation

作者： Navid Noroozi Alireza Khayatian Tianshi Chen School of Electrical and Computer Engineering Shiraz University Shiraz Iran Automatic Control Division Electrical Engineering Department Linköping University Linkoping Sweden

In this paper, a state-feedback strategies based on time-scale separation technique for a class of strict-feedback systems in the presence of unstructured uncertainties is proposed which recovers the state trajectories of a nominal control design (without uncertainties) in the presence of unmatched uncertain nonlinearities. The performance of the feedback control scheme is evaluated by simulating state-feedback control for an uncertain strict-feedback system.

关键词： Instruments Automation

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Perpendicularity Error Evaluation of an Axis Based on New Generation GPS

Perpendicularity Error Evaluation of an Axis Based on New Ge...

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International Conference on Genetic and Evolutionary Computing (WGEC)

作者： Juan Peng Yanru Zhong Kewei Liu Meifa Huang School of Computer & Control Guilin University of Electronic and Technology Guilin China School of Mechanical & Electrical Engineering Guilin University of Electronic and Technology Guilin China

Operation and operator technologies used in new generation geometrical product specifications (GPS) provide the necessary knowledge basis for digitization and standardization in verification process of geometrical products. This paper analyzes the operator formation for perpendicularity error evaluation of an axis, and presents mathematical models and the objective optimization functions of every association operation based on the minimum zone solution. A multi-population genetic algorithm based read-coded is then introduced to solve the optimization problem. The basic idea and steps of the algorithm are described in detail. Finally, the measurement process of the perpendicularity error evaluation of an axis based on real-coded multi-population genetic algorithm is realized through an example. The experiment indicates that the proposed method is efficient and feasible. The result of evaluation is accurate and unique.

关键词： Global Positioning System Surface morphology Genetic algorithms computer errors computer aided manufacturing Mathematical model Error correction Solid modeling Standardization Mechanical products

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Customer Attitudes Toward Circular Economy in the E-Waste Context: A Survey Assessing Sustainable Consumption Dynamics

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IEEE engineering Management Review 2023年第4期51卷 28-45页

作者： Jabbour, Charbel Jose Chiappetta Colasante, Annarita D'Adamo, Idiano Rosa, Paolo Sassanelli, Claudio NEOMA Business School Department Information Systems Supply Chain and Decision Making Mont-Saint-AignanCedex 76130 France Unitelma Sapienza-University of Rome Department of Law and Economics Rome00161 Italy Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy Politecnico di Milano Department of Management Economics and Industrial Engineering Milan20133 Italy Politecnico di Bari Department of Mechanics Mathematics and Management Bari70125 Italy

Waste from electrical and electronic equipment (WEEE) is a relevant concern in Europe. Even if relevant improvements have been made in terms of material recovery technologies and policy measures, there remains a significant gap between the amount of WEEE generated versus collected in the EU. This study is based on an online survey whose aim was to assess the attitude of students (i.e., individuals who dispose with a higher frequency electronic product) to buy/substitute three iconic product types (i.e., smartphones, notebooks, televisions), and their openness toward circular practices (i.e., recycling, reconditioning, reuse). Results have shown that: a) new products represent the greatest market share, b) reliability and quality were the most significant factors influencing the purchase decision in all product types, and c) circular premium was not recognized by students for any product type. From a social perspective, a responsible consumption pattern emerged, though a sustainability bias was evident with respect to products' residual value. From an economic perspective, these findings may inform companies about the orientation of young consumers and provide key insights for new business strategies. Results suggest that a proper waste management strategy also requires a behavioral change of people who should adopt it. © 1973-2011 IEEE.

关键词： Electronic Waste

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Adaptive behavior design based on FNN for the mobile robot

Adaptive behavior design based on FNN for the mobile robot

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IEEE International Conference on Automation and Logistics

作者： Caihong Li Ping Chen Yibin Li School of Computer Science and Technology Shandong University of Technology Zibo Shandong China School of Control Science and Engineering Shandong University Jinan Shandong China

ISBN: (纸本)9781424447947

A fuzzy neural network (FNN) has been trained off-line to memory the fuzzy control rules of the adaptive behaviors for the local optimal path planning of mobile robot. The fuzzy rules were collected automatically by reinforcement Q_Learning (QL) on-line beforehand. This method has overcome the disadvantage of traditional means which are determined by artificial experience, and are able to meet the requirement of local optimal path planning. The FNN controller is constructed by BP neural network (BPNN). After the off-line training, the rules are stored implicitly in FNN. In control applications, without looking up table, when the real-time sense information is input to FNN, the best adaptive behavior is produced in the output. The simulation results show that because all training samples are from the trained fuzzy rules, the output is almost the same as the result of the training rules.

关键词： Mobile robots Fuzzy control Fuzzy neural networks Fuzzy logic Programmable control Adaptive control Path planning Robot kinematics Neural networks Robotics and automation

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Event-driven synchronization of Lur'e system subject to cyber attack

Event-driven synchronization of Lur'e system subject to cybe...

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Chinese control Conference

作者： Liuwen Li Wenlin Zou Jie Cao Yushun Tan Shumin Fei Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing

This paper studies event-driven synchronization control problem for chaotic Lur'e system with cyber attack. Firstly, In order to save network bandwidth resources, the event-driven scheme is employed to determine whether the sampled state should be transmitted to the controller. Secondly, a synchronization error model is designed to describe the master-slave synchronization system with event-driven scheme and cyber attack. Thirdly, based on Lyapunov functional approach, sufficient conditions for stabilization are derived which can ensure synchronization of the master system and slave system;particularly, a co-designed approach of controller and the event-driven matrix is obtained using the above stability condition. Lastly, a numerical example is presented to illustrate the effectiveness of the proposed approach.

关键词： Synchronization Cyberattack Chaotic communication Master-slave Electronic mail Linear matrix inequalities control systems

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Intelligent PID controller Based on Deep Reinforcement Learning

Intelligent PID Controller Based on Deep Reinforcement Learn...

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Robotics, control and Automation (ICRCA), International Conference on

作者： Yinhe Zhai Qiang Zhao Yinghua Han Jinkuan Wang Wenying Zeng College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

PID control is still the most important and popular method in industrial control at present. PID control is easy to achieve and it can improve the steady-state performance and dynamic performance of the system. PID controller can be used for all objects, however, it has some problems with parameter adjustment and control effect. The proportional integral differential coefficient of PID control is fixed, and it can't change when disturbed, so the stability of the system will be affected. Moreover, PID control is prone to overshoot and can't be used in specific systems. Reinforcement learning (RL) algorithms have developed rapidly from discrete action to continuous action in recent years. It has aroused the high interest of researchers in the field of automatic control. RL control performs better in the degree of intelligence and dynamic performance, however, the steady-state performance is poor. The sensitive response of RL control will damage the actuator. In this paper, an adaptive PID controller based on deep reinforcement learning is proposed. By designing reward values, the desired control effect is described. In this way, an agent is trained to provide parameters to the PID controller in real time. It can improve the response speed of the system, suppress overshoot, and have a certain anti-disturbance ability by training the agent to achieve real-time PID parameter adjustment.

关键词： Training PI control Heuristic algorithms Industrial control Deep reinforcement learning Real-time systems Steady-state

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Chinese sign language recognition with 3D hand motion trajectories and depth images

Chinese sign language recognition with 3D hand motion trajec...

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World Congress on Intelligent control and Automation (WCICA)

作者： Lubo Geng Xin Ma Haibo Wang Jason Gu Yibin Li School of Control Science and Engineering Shandong University Jinan Shandong China Department of Electrical and Computer Engineering Dalhousie University Halifax Nova Scotia Canada

An important part for sign language expression is hand shape, and the 3D hand motion trajectories also contain abundant information to interpret the meaning of sign language. In this paper, a novel feature descriptor is proposed for sign language recognition, the hand shape features extracted from the depth images and spherical coordinate (SPC) feature extracted from the 3D hand motion trajectories combine to make up the final feature representation. The new representation not only incorporates both the spatial and temporal information to depict the kinematic connectivity among hand, wrist and elbow for recognition effectively but also avoids the interference of the illumination change and cluttered background compared with other methods. Meanwhile, our self-built dataset includes 320 instances to evaluate the effectiveness of our combining feature. In experiments with the dataset and different feature representation, the superior performance of Extreme Learning Machine (ELM) is tested, compared with Support Vector Machine (SVM).

关键词： Trajectory Gesture recognition Assistive technology Shape Three-dimensional displays Feature extraction Support vector machines

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Human Behavior Recognition Based on WiFi Channel State Information

Human Behavior Recognition Based on WiFi Channel State Infor...

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Chinese Automation Congress (CAC)

作者： Zhonghua Tang Aichun Zhu Zixuan Wang Keqing Jiang Yifeng Li Fangqiang Hu School of Computer Science and Technology Nanjing Tech University Nanjing China School of Information and Control Engineering China University of Mining and Technology Xuzhou China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Nowadays, with the continuous development of WiFi technology, more researchers come to realize that human behavior can be recognized by the application of WiFi Channel State Information (CSI). When human behavior has some changes, it will influence reflections of WiFi signals, which will also cause some changes to the CSI. Using the Intel WiFi Link 5300 network interface controller (NIC) and CSI-Tool, we can obtain the CSI data of corresponding behaviors. In this paper, we design a system to recognize different human behaviors based on the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU). Firstly, we use the original data collected by the CSI-Tool, then extract the CSI amplitude values of different behaviors as features and input them into neural network structures where the GRU and CNN are connected in parallel. Based on the above works, we can successfully identify different human behaviors.

关键词： Wireless fidelity Wireless communication OFDM Receiving antennas Data mining computer science Channel state information

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