Projects

2022.5-2023.4 Postgraduate Research & Practice Innovation Program of Jiangsu Province -Industrial Practice Training Mechanism of Graduate Students based on the School-Enterprise Joint Innovation Center:

Role: PI, Total Funding: 10,000 CNY

This topic will be guided by the demand for high-quality compound talent training during the construction of my country's "14th Five-Year Plan" period, and break through bottlenecks such as the disconnect between professional skills and industrial practice in the current postgraduate training process. This topic clarifies the two-way promotion mechanism of theory combined with practice in the process of postgraduate training, and then completes the development of postgraduate practice job matching algorithm based on artificial intelligence big data, and proposes an effective evaluation and improvement plan for the core competitiveness of postgraduate employment in the process of industrial practice. This project responds to the call of the Ministry of Education to deepen the collaborative education of industry-university-research institutes, explores the industrial practice training mechanism for postgraduates during the construction of the "14th Five-Year Plan" period, and integrates the professional skills mastered by postgraduates into relevant industrial practice issues to realize the organic integration of theory and practice. The combination will eventually cultivate a group of outstanding talents with rich practical experience, which is of great significance to the batch training of high-quality compound talents in the new era of our country.

2022.1-2024.12 Lanxi Wheel Top Car Material Co., Ltd.-Low-carbon Integrated Intelligent Equipment and Process Industry Technology Innovation Center:

Role: PI, Total Funding: 1,000,000 CNY

In order to better serve the local economy, promote the cooperation of "production, learning and research", fully integrate the advantages of colleges and universities in basic research and technology development and the conditions of enterprises in production, operation and industrialization, and promote the cooperation between schools and enterprises. Development, speed up the pace of transforming scientific research results into productive forces, and improve the level of industrial technological innovation. After friendly negotiation, the two sides decided to carry out close and long-term cooperation on the basis of mutual benefit and complementary advantages, and jointly build the "Soochow University-Lanxi Lunfeng Vehicle" Materials Co., Ltd. low-carbon integrated intelligent equipment and process industry technology innovation center "

2021.04-2024.03 Doctor of Entrepreneurship and Innovation in Jiangsu Province:

Role: PI, Total Funding: 150,000 CNY, Funding Number: JSSCBS20210641

The applicant will be engaged in theoretical innovation, starting from the high-precision and low-consumption requirements of path following problems under assembly line operations, and intends to break through technical bottlenecks such as insufficient control algorithm accuracy and lack of energy management mechanisms under China's current extensive development model. The applicant will clarify the operation mechanism of each process of the pipeline including variable following rate and hybrid system constraints, and then complete the modeling of multi-objective coupling path following problems including control, optimization and constraint inclusion control, optimization and constraint inclusion control, optimization and constraint , Path following problem modeling, path following problem modeling, design multi-objective iterative learning control algorithm. In response to the State Council’s call for green energy-saving manufacturing, the applicant explored high-precision and low-consumption pipeline path-following strategies, and integrated iterative learning control technology into the high-precision and low-consumption path following problems to achieve a combination of theory and practice, and ultimately achieve high-quality and low-consumption products. The dual goal of low cost is of great significance to improving assembly line production accuracy and energy consumption management.

2021.07-2024.06 Key Industrial Technology Forward-looking Fundamental Application Research Project of Suzhou -Research on Path Following Strategy of High Precision and Low Consumption Assembly Linw based on Generalized Iterative Learning Control:

Role: PI, Total Funding: 100,000 CNY, Funding Number: SYG202138

(1) Research on the operation mechanism of various processes in the path following problem of high-precision and low-consumption pipeline.

(2) Research on iterative learning control algorithm based on multi-object coupling path following problem model.

(3) Research on generalized iterative learning control theory under constraint conditions and multiple variables.

(4) The actual performance verification research of the algorithm under the simulation pipeline experiment platform

2022.01-2024.12 National Natural Science Foundation of China -Research on Multi-objective Iterative Learning Control for Assembly Line Path Following Strategy with High Accuracy and Low Energy Consumption:

Role: PI, Total Funding: 300,000 CNY, Funding Number: 62103293

Assembly line is lifeline of modern industry, and there exist low accuracy and high energy consumption problems in our domestic assembly line. It is a common depending problem that how to guarantee the machine devices perform path following tasks with high accuracy and low energy consumption within assembly line production. The project focuses on the practical needs on high accuracy and low energy consumption to propose a path following strategy using multi-objective iterative learning control, which unlocks the chock points on manufacturing accuracy and energy management to achieve the dual design objectives. This project has four research topics as follows: ① explicitly explain the operating mechanism of assembly line containing varying speed profile as well as mixed system constraints, and formulate a theoretical model for multi-objective coupled path following problem; ② design multi-objective iterative learning control algorithm using coordinate descent method, successive projection method and convex optimization method; ③ prove the robustness, convergence and optimization of the algorithm to solve the control problems on constrained conditions and multi-variables; ④ Validate the algorithm performance on an experimental test platform replicating an assembly line working environment. This project aims at exploring a path following strategy to achieve high accuracy and low energy consumption, which has essential meanings for improving the manufacturing accuracy and energy management of assembly line.

2021.09-2022.08 Huanjiu Information Technology (Suzhou) Co., Ltd. -Research on Non-destructive Survey System for Smart Construction Site:

Role: PI, Total Funding: 15,000 CNY

1. Algorithms for 3D point cloud modeling of indoor spaces in smart construction sites. 2. Debugging and prototype construction of related hardware equipment. The team used the LIDAR RPLIDAR A3 M1 lidar scanner as a means to conduct surveys. The data provided by lidar is a distance and its corresponding angle, then 3D modeling can be implemented.

2021.07-2024.06 Natural Science Foundation of Jiangsu Province -High Precision and High Efficiency Path Following Strategy based on Iterative Learning Control :

Role: PI, Total Funding: 200,000 CNY, Funding Number: BK20210709

This project will be guided by the high-precision and high-efficiency requirements of the pipeline path following problem, and break through the technical bottlenecks such as insufficient control algorithm accuracy and lack of efficiency management mechanism under the extensive development mode. This project clarifies the operation mechanism of each process in the pipeline including variable following rate and hybrid system constraints, and then completes the modeling of path following problems including multi-objective coupling of control, optimization and constraints, and designs multi-objective iterative learning control algorithms. This project responds to the call of the State Council for intelligent manufacturing, explores high-precision and efficient pipeline path following strategies, integrates iterative learning control technology into the high-precision and efficient path following problem to achieve the combination of theory and practice, and finally achieves the double quality of products and high efficiency The goal is of great significance for improving the production accuracy and efficiency management of the assembly line.

2021.04-2024.03 Suzhou Jingsheng Intelligent Technology Co., Ltd. -Carbon neutral integrated industrial intelligent diagnosis and decision-making system:

Role: PI, Total Funding: 1,500,000 CNY

The project team intends to develop a carbon-neutral integrated industrial intelligent diagnosis and auxiliary decision-making system for the environmental protection treatment process of solid and hazardous waste. 1. Artificial intelligence technology for diagnosing the working conditions of various production links in the environmental protection industry. 2. Digital twin technology for numerical simulation of the entire environmental protection industry line. 3. Energy-saving and emission-reduction assistant decision-making technology based on working condition diagnosis and numerical simulation.

2021.01-2022.12 Soochow University -Research on Robust Iterative Learning Control Based on Variable Initial Conditions and Mixed Constraints:

Role: PI, Total Funding: 50,000 CNY

Compared with traditional control methods, iterative learning control can effectively improve the product performance and accuracy of large-scale repetitive industrial production tasks. Since the control method requires low computing resources and the actual execution method is relatively simple, it has the feasibility of being applied to industrial production such as laser engraving assembly lines and produces important economic benefits. This project will conform to the development trend of iterative learning control research field in recent years, remove the task assumption that the initial state of the system is unchanged, and fully consider the various system constraints in real applications, and expand the application scope of this method to a broad sense The trajectory tracking problem of industrial robots. This project took another approach in the process of problem modeling, fully considering bounded variable initial conditions and mixed constraint processing, and reducing errors through continuous iterations while ensuring the feasibility of the robot trajectory following the task. This project plans to add variable initial states and various practical constraints to the algorithm design to verify the robust convergence of the algorithm at the theoretical level, and to further test the performance of the algorithm on the experimental platform to verify their practical application Reliability and validity in.

2021.01-2022.12 Civil Aviation University of China -Task Planning and Allocating Method of Integrated Avionics System Based on Artificial Intelligence Assistant Algorithm:

Role: PI, Total Funding: 80,000 CNY, Funding Number: SH2020112703

The core content of this project is to solve the problems of high dynamics, high heterogeneity, and high computing load in the task planning and distribution of the integrated avionics system by introducing artificial intelligence algorithms. The main research contents include: mathematical modeling of integrated avionics system operation and digital twin simple system construction, verifying the pros and cons of various task allocation schemes at the numerical simulation level; on the basis of this system, design a new set of artificial intelligence Auxiliary algorithms are used in various complex environments to screen out the safest and best-performing solutions through deep neural networks; based on the above two research results, this project will build an artificial intelligence-assisted decision-making planning system for integrated avionics system tasks.

2020.06-2023.05 Excellent Young Scholar Program of Soochow University:

Role: PI, Total Funding: 400,000 CNY

This funding is the startup package of Excellent Young Scholar provided by Soochow University.

2018.09-2020.05 Future SCOOT: Next Generation Smart Urban Traffic Control Algorithm

Role: Participator

Work with the teams from Siemens and Traffic for London to model the urban traffic system, and develop intelligent control algorithms based on artificial intelligence to reduce the congestion and delay of the traffic in the city of London.

2013.10-2018.09 Iterative Learning Control for Spatial Path Following:

Role: PHD Project

Develop control algorithms to improve the path following accuracy of the high performance production line such as laser scenery, 3D printer and robotic manipulator.

2012.10-2013.06 Mean Field Game:

Role: MEng Final Year Project

Build a mathematical model of an economic market with multiply participators. Use the control techniques to help the market regulator to optimize some desired performance index to achieve certain practical benefits.

2011.10-2012.06 Outreach for Prospective Student:

Role: Participator

Due to the tuition fee increase of the UK home student, the admission competition between the universities in UK becomes much severe. To help with admission, an Android device application is developed to introduce the information of Imperial College London to the prospective students and their parents.

2009.10-2010.05 Intelligent Unmanned Vehicle (EEbug):

Role: Participator

Use the relevant professional knowledge and skills in Electrical and Electronic Engineering to design and build a mini unmanned vehicle, which can change the direction when it reaches some marked white lines.