Table of contents

Control theory and engineering have witnessed dramatic achievements, which have made possible space travel and communication satellites, have assisted in the design of safe and efficient aircraft, ships, trains, and cars, have helped in developing cleaner chemical processes while addressing environmental concerns.

The International Conference on Control Theory and Applications 2021(ICoCTA 2021), organized by the Hong Kong Society of Robotics and Automation (HKSRA), was scheduled to be held in Chengdu, China, but was held virtually on November 4-6, 2021 due to the Covid-19 pandemic. ICoCTA 2021 aims to bring together researchers, engineers, scientists and industry professionals in a unique platform and present their stimulating research and knowledge transfer ideas in Control Theory and Applications.

The conference included two full days of technical sessions consisting of keynote lectures, oral presentations, and poster presentations. We were delighted and honored to invited Prof. Romeo Ortega (IEEE Fellow, Full Professor at ITAM, Mexico), Prof. Petros Ioannou (A.V. 'BAL' Chair Professor, University of Southern California) and Prof. YangQuan Chen (University of California, Merced) to share their insightful ideas and mind-blowing research as keynote speakers.

After a couple of months well preparation and hard work, the proceeding of the International Conference on Control Theory and Applications 2021 (ICoCTA 2021) is smoothly published.

On behalf of the conference organizing committee, we'd like to show our congratulation and sincere gratitude to all authors, peer reviewers, speakers, oral and poster presenters and listeners. We deeply appreciate their endeavor in making the conference an impressive success.

List of ICoCTA Committee Members are available in the pdf

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

• Type of peer review: Double Anonymous

• Conference submission management system: Morressier

• Number of submissions received: 47

• Number of submissions sent for review: 43

• Number of submissions accepted: 20

• Acceptance Rate (Submissions Accepted / Submissions Received × 100):

42.6

• Average number of reviews per paper: 2

• Total number of reviewers involved: 30

• Contact person for queries:

Name: Jennie Z. J. Lee

Email: editor@hksra.org

Affiliation: The Hong Kong Society of Robotics and Automation

Smoking in public places not only brings about some safety hazards, but also does harm to people's lives, property and living environment. A smoking behavior detection model based on deep learning is trained for the concern of environment and safety. First, a vertical rotation data enhancement method is adopted in the preprocessing stage to extend the dataset and increase the objects of detection. Then, the channel attention module is introduced in backbone network to calibrate the feature response. Finally, added a small target detection layer to the YOLOv5 algorithm. This paper analyzes the network structure of the YOLOv5s, and the model is trained and tested by utilizing the YOLOv5s network. Experimental results show that the mAP value of the algorithm is improved by 5.3% over the original algorithm.

In order to keep the angular velocity output of unmanned aerial vehicle (UAV) swarm consistent, a solution is proposed in this paper. Firstly, a reasonable simplified modeling is carried out for UAV, and its linear part is selected to set the output consistency controller. Then, based on graph theory and lyapunov stability theorem, the asymptotic stability conditions of the designed controller are given. Finally, the effectiveness of the proposed method is verified by simulation. The results show that the designed controller can quickly keep the output angular velocity of UAV swarm consistent.

In this paper, the traditional DSW conditions was presented. The nonlinear incomplete differential conditions were converted into common differential conditions through travelling wave transformation, and all the definite voyaging wave arrangements for the traditional DSW conditions can characterized by use a complete separation framework for polynomials. The specific voyaging wave arrangements existed as periodic solutions of geometrical functions and sane numbers, lone wave arrangements, doubly occasional arrangements of elliptic functions.

Smart product-service system (Smart PSS), as an emerging digital paradigm, should meet user expectations and optimize their experience with a higher degree of sustainability. As a complex solution bundle with digital technology, its innovative design method differs from the existing method for traditional products. For a reason that massive user-generated data can be collected in the usage stage, which can be utilized to obtain new user requirements and calculate the user satisfaction degree. According to the real-time user preference information, some modules can be upgraded or adjusted in order to extend the lifecycle of the sustainable Smart PSS. Nevertheless, few studies focus on conducting innovative design by concerning massive information collected in the usage stage in a sustainable Smart PSS environment, let alone a novel approach to support the information re-use in product development of other fields. Aiming to fill this gap, a new innovative design methodology is proposed by combining Function-Behavior-Structure (FBS) and Kano model to guide Smart PSS development. User requirements and behaviors are predicted and analysed by forecasting and collecting information from the perspective of information. As an illustrative case study, a self-service medicine vending system is described to explain the proposed approach. This research provides guidance for Smart PSS in the medical field.

The variability and randomness of the load bring certain difficulties to the control accuracy of the electromagnet. A novel human-simulation logical control research on liftingelectromagnet is studied in this paper. Based on the pan-Boolean theory, the logical control model for electromagnetic force control system is based on control practice and is consisted by a series of control rules. The dynamic control model of electromagnetic force control system is established in Matlab/Simu1ink, the simulation experimental results show that if adding the human-simulation logical controller, the overshoot of the system is smaller, the response speed is faster, and the steady-state accuracy is higher. The controller can better meet the working requirements of the lifting electromagnet.

In recent years, people's awareness of wildlife protection has been increasing, and the demand for wildlife observation and recording has increased. However, the existing sound source localization system needs to continuously collect sound signals and convert the time-domain signals of the sound into frequency-domain signals. These shortcomings will increase the power consumption of the device, adversely affect the battery life, and the sound source localization result is easily interfered by the echo of environmental noise and target sound waves. In this study, the algorithm and hardware are improved on the basis of the existing sound source positioning system, which can improve the positioning accuracy and reduce the power consumption and cost of the whole machine. The adaptive circuit is used to filter and adjust the sound signal in real time to eliminate the influence of the difference between the individual microphones in the microphone array, which makes the sound source localization system more robust to the environmental noise and the echo of the target sound wave, and more suitable for the forest scene of wildlife protection. Through the research of this sound source localization system, we hope to provide some new ideas for producing economical and practical sound source localization equipment and lay a foundation for the intelligent development of wildlife protection in the future.

Quantization factor is one of the main external parameters of a fuzzy PID controller, which has an obvious impact on the performance of the controller. The traditional setting methods do not reveal its inherent laws. In this paper, we take a specific fuzzy PID control system as the research object. Secondly, we study the influence of quantization factor on the performance of the controller in detail. We propose a new method of calculating quantization factor with probability coverage. Experiments are conducted to prove its rationality and effectiveness

For the classification problem of power event monitoring data, manual rules are mainly used to recognize the known classes of events, and new classes of events cannot be extracted by using existing manual rules. But new classes may emerge with the upgrading of power equipment and traditional representation of this time series data is not proper for the task. To solve this issue, we introduce an effective approach that includes three parts: i) embed the raw object of power event in semantic space ii) learn the representation of power event with multi-level. iii) detect new classes, classify old classes, and update models to classify both new classes and old classes. Experiments on real power event monitoring dataset demonstrate that the proposed method outperforms the state-of-the-art methods.

Aiming at the problem of a large amount of CAN communication message data of automobile controllers and low efficiency of traditional software development, this paper proposes a method of CAN message unpack and pack based on Simulink automatic code generation technology. S-Function is used to design CAN message sending and receiving model that can import DBC files, and use RTW (real-time workshop) to realize Simulink model to automatically generate C code. This method effectively solves the unpack and pack of CAN messages, simplifies the code development process, and improves the efficiency of software development, especially when there are DBC files, which simplifies the work of sending and receiving and testing.

In order to solve the problem that it is difficult to extract fault information in all directions in the process of dimensionality reduction of high-dimensional and nonlinear fault data sets, this research designs a rotor fault data set that combines Kernel principal component analysis, KPCA and Kernel Neighborhood Preserving Embedding, and KNPE Dimensionality reduction method. This method first uses the KPCA algorithm to effectively reduce the redundant attributes of the data, and retains the global nonlinear information of the original data; then uses the KNPE algorithm to mine the local manifold structure information of the data and extract the low-dimensional essential features. The above method can avoid the distortion of the local subspace structure through the simultaneous orthogonalization process, and can visually display the low-dimensional results. Use low-dimensional feature subsets to input K-nearest neighbor, KNN recognition rate and cluster analysis, etc. The distance G_B and the intra-class distance G_W are used as indicators to measure the effect of dimensionality reduction. Experiments show that this method can fully extract global and local discriminant information, make the fault classification clearer, and the recognition accuracy rate has been significantly improved. The research can solve the visualization and classification of high-dimensional and nonlinear mechanical fault data sets, and provide theoretical reference.

The state of charge of battery is an important index of battery management system. The Lithium-ion batteries are widely used in various industries, so they are full of uncertainty, which makes them difficult to estimate the state of charge of Lithium-ion batteries. To solve the problem of low accuracy and large variability in real-time estimation of Lithium-ion batteries, taking Lithium-ion batteries as the research object, the Thevenin model is used to simulate the working characteristics. On the basis of the extended Kalman filtering algorithm, through the influence of the covariance matrix and noise, a smoothing factor is introduced to increase the Kalman gain and improve flexibility. Experiments have proved that the smoothing factor-extended Kalman algorithm improves the flexibility of the algorithm, and at the same time reduces the non-linear error caused by the rapid charging and discharging changes of Lithium-ion batteries. In the Hybrid Pulse Power Characterization test, the maximum estimation error is 0.14%, and the average estimation error is 0.1%. It provides a new method for estimating the state of charge of Lithium-ion batteries.

The problems of regulating power quality are more prevalent. In general, Active power filter(APF) is one of the best solution. But the influence of load impedance and grid impedance were widely ignored by a great number of papers, supposed the load voltage is constant and modeling in APF singly, which can't describe the system accurately. Firstly,there are build an accurate model, including LC APF, load and grid impedance. Secondly, applying feedback linearization and proportional resonant differentiation (FL-PRD) control in inner current loop. Then analyze the influence of load and grid impedance. Once more,checked the accuracy of builded accurate model by Simulink and experiment. Eventually, compare mentioned algorithm in the paper with classical proportional resonant (PR) controller. The results show that it is rather important to set up an accurate model and the proposed algorithm gets a perfect capacity.

This paper addresses the problem of the robust stabilization control for a quadrotor slung-load system using sliding mode control. The hybrid model of the slung-load system is presented as two separate systems, one fully actuated, and another under actuated. To achieve the robust stabilization control with the unexpected external disturbance, a sliding mode controller is proposed to enforce the slung-load system tracking of the desired trajectory with a slight swing of the suspended load. The Lyapunov function is defined as the system energy function that is always dissipated over time until nullity, when the system reaches the point of equilibrium. The stability of the controller is analyzed by the Lyapunov function and the stability criteria are given. Finally, the simulations are performed to evaluate the effectiveness of the proposed control scheme.

Smart product-service system (Smart PSS), as an emerging digital paradigm, offers users solution bundles to enhance the digitalization capability of the system by utilizing user-generated data. Massive information can be collected to investigate the dynamic changes of user requirements and assists the developers in designing personalized products and services. Meanwhile, key technologies (i.e. big data analysis and cloud computing) have enabled upgrading the rehabilitation equipment from the traditional physical training device to the personalized smart rehabilitation product-service system. However, few studies focus on gathering and utilizing user-generated physiological data to guide the innovative design of rehabilitation solutions in the Smart PSS environment. To fill this gap, an innovative design method is proposed to guide the Smart PSS development in the personalized rehabilitation field. The presented approach can be divided into three phases: the research phase, the development phase, and the usage phase. A personalized lower limb rehabilitation smart product-service system was described to explain the proposed approach.

Estimating and understanding the surroundings of the vehicle precisely forms the basic and crucial step for the autonomous vehicle. The perception system plays a significant role in providing an accurate interpretation of a vehicle's environment in real-time. Generally, the perception system involves various subsystems such as localization, obstacle (static and dynamic) detection, and avoidance, mapping systems, and others. For perceiving the environment, these vehicles will be equipped with various exteroceptive (both passive and active) sensors in particular cameras, Radars, LiDARs, and others. These systems are equipped with deep learning techniques that transform the huge amount of data from the sensors into semantic information on which the object detection and localization tasks are performed. For numerous driving tasks, to provide accurate results, the location and depth information of a particular object is necessary. 3D object detection methods, by utilizing the additional pose data from the sensors such as LiDARs, stereo cameras, provides information on the size and location of the object. Based on recent research, 3D object detection frameworks performing object detection and localization on LiDAR data and sensor fusion techniques show significant improvement in their performance. In this work, a comparative study of the effect of using LiDAR data for object detection frameworks and the performance improvement seen by using sensor fusion techniques are performed. Along with discussing various state-of-the-art methods in both the cases, performing experimental analysis, and providing future research directions.

This paper, A model reference adaptive control method based Lyapunov stability theory is proposed, because it is difficult for the control of inclined channel of missile, due to large disturbance and the dramatic change of missile parameter during the flight. The core strategy is that the real system can be adjusted automatically and in real time to be consistent with the reference model as far as possible, when the output of the real system is different from that of the reference model, so that the real system can run in the best state, this control method can well adapt to the changes of control parameters and external environment. The rationality and validity of the method has been verified by mathematical simulation.

To quickly and accurately identify motor faults, this paper proposes the gray wolf algorithm based on teaching and learning improvement to adaptively optimize the parameters of the multi-class support vector machine. Finally, based on the improved algorithm, the seven types of fault data such as the air gap eccentricity of the motor, the broken bar of the rotor, the bearing seat damage, and the bearing wear were carried out fault diagnosis experiments, and a comprehensive comparison and analysis were carried out with the widely used algorithm. The results show that the motor fault diagnosis accuracy rate of this method is 97.88%, which is better than other methods in classification accuracy.

Automated Valet Parking (AVP) can relieve drivers from having to park. It enables vehicles to drive, park and charge fully automatically. This paper focuses on the first working step of the motion planning module, the global path planner. Therefore, a global path planner will be developed and evaluated based on actual research methods. A route will be computed by using an A^* algorithm based on the occupancy grid map. Subsequently, a smoothing operation will follow to obtain a drivable path. By implementing a single polar polynomial function each vertex is smoothened. The resulting global path serves as an input for path tracking. For a reasonable simulation, a vehicle controller with a lock-ahead distance and a node-check will be designed in order to keep the vehicle on the desired path. A simulation of the designed components in CarSim will be carried out to analyze the path tracking and vehicle behavior in detail.

This work presents the development and implementation of an armband for EMG-data acquisition of the upper arm. The developed prototype serves for investigations on EMG-signal processing with the final objective of reliably controlling a prosthesis for a transhumeral amputee. To make the product available for a greatest possible number of people one of the main characteristics will be a design of very low cost. A focus is put on the electric circuit design. In order to manufacture the prototype without difficulties, it is exclusively designed with components, which are available on the Ecuadorian market. The development is based on previous investigations and approaches of other researchers. With help of electric circuit simulations, the design was adapted and optimized step by step and a reliable low-noise circuit was established. All components are arranged on printed circuit boards in a way to keep the device as small as possible. To optimally avoid noise the length of the connection from the electrodes to the amplifier is minimized. Compact 3D-printed housings cover all the electric components. All housings consist of only two parts and are intuitive to assemble. Holes in the bottom offer space to fix electrodes via a snap-fastener connection. 3D-printed elastic straps are designed to connect the subsystems and hold the device in place. The armband including two sensors weighs 92 g and is capable of measuring two muscles with a bipolar EMG-setting each, sharing one reference electrode, which is aligned on the side of the upper arm between the biceps and triceps muscles. The amplification of the sensors is adjustable individually by potentiometers, facilitating a gain factor range of 211 to 2016 V/V. The data is recorded by a microcontroller board and send to a computer for processing via wire or Bluetooth. For wireless operation, rechargeable batteries are integrated. Test measurements on an able-bodied human prove the functionality of the device.

Due to the limitation of volume and weight, the missile body parameters of the micro tailless air-to-ground missile have a large range during flight, and the characteristics of the rolling channel are greatly different from those of conventional missiles, the stability control of the roll channel is greatly affected by the error of the actuator. Under the classic PID control, the HWIL simulation results show that the jitter of roll angle is obvious, and the anti-disturbance ability of the system is poor. Based on the classic PID, fuzzy control is introduced to optimize the design of rolling channel by using fuzzy online self-correcting PID control. The results of the hardware-in-the-loop simulation show that the jitter of roll angle is suppressed, which improves the characteristics of the roll channel and the control efficiency.