Shuai Li’s homepage



Dr. Shuai Li (李帥)

Research Assistant Professor

Department of Computing

The Hong Kong Polytechnic University

Hung Hom, Kowloon, Hong Kong

Tel:  (852)27667286



Opening     Education     Research   Students   Publications     Teaching     Service


Looking for highly motivated PhD students with strong background in robotics, control theory or convex optimization (one of the above mentioned fields, not all of them) or with research/working experience in programming and embedded system development. If you have interest, please feel free to contact me.




Shuai Li received the B.E. degree in precision mechanical engineering from Hefei University of Technology, China in 2005, the M.E. degree in automatic control engineering from University of Science and Technology of China, China in 2008, and the Ph.D. in electrical and computer engineering from Stevens Institute of Technology, USA in 2014.



My research interests include:

l  Robotics (Multi-robot coordination and cooperation, Kinematic and dynamic analysis of robotic systems, Path and motion planning, SLAM)

l  Control of Networked Systems (Multiple Manipulator Coordination, Multiple Mobile Robots, Laser Array, Large-scale Battery Array, etc.)

l  Recurrent Neural Networks (Optimal neural network design, Convergence analysis, Convergence speed-up, applications to WSNs, robotics, winner-take-all problem)

l  Active/Robotic Sensing Networks (Multi-robot source seeking, Multi-robot Plume front tracking)

l  Distributed Estimation, Optimization and Control (Consensus, Consensus filter)

l  Dynamic Systems and control

l  Development of robotic systems


Previous research experience:

l  Multi-robot Plume Front Tracking

The recent deep water horizon oil spill has posed great challenges to both robotics and ocean engineering communities. It took months to estimate the extent of the underwater plume, and the accuracy of these estimates will likely be debated for years to come. The challenges motivate us to consider utilizing advanced robotic techniques to monitor and track the propagation of oil plumes. We propose a model-based method to track the dynamic plume front using multi-robot platforms.  Different from existing work purely relying on gradient measurement, the transport model of pollution source is explicitly considered in tracking control design. We first study the problem using a single robot and solve the problem in an estimation and control framework. We then extend it to the multi-robot case in a nearest-neighbor communication structure, and have the robots take formation along the plume front. The distributed control is scalable to a large number of robots.

The snapshots of the robot movements for a typical simulation run with 30 robots for cooperative plume front tracking, where the pseudo-color indicates the concentration distribution at each time step with the scale shown in the color-bar aside, the black curve is the contour of the plume front, the hollow square in blue and the hollow circle in red represent the position of the follower robots and the true position of the boundary robots, respectively, the positions marked with S1 and S2 are the two sources starting propagating chemicals in the flow field at time t = 0.

l  Multi-robot Source Seeking

In this work, we consider such a problem: the spoilage of chemical sources propagates with the flow in the space or water and forms a time-varying concentration field. We want to design algorithms to drive a group of robots to track the plume front. Potential applications include the monitoring of hazardous waste propagation, the coverage of polluted areas, the surrounding of gas leakages, and chemical plume tracing in underwater environments. In addition, the design of efficient plume tracking algorithms for robotic system may also be helpful to the understanding of many natural phenomenon, such as the pheromone plume tracing demonstrated by male moth for mating, and the navigation by exploiting the odor plume dynamics. In our formulation, each robot maintains a gradient estimation, moves to the source by tracing the gradient, and all together keeps a pre-defined formation in movement. We present two control algorithms with all-to-all and limited communications, respectively. The estimation error is taken into account to derive robust control algorithms. Comparing to existing methods, the proposed algorithm with limited communications is fully distributed. Theoretical analysis validates the effectiveness of our methods. Experimental results on the E-puck robot platform demonstrate satisfactory performances in a light source seeking application.

Light source seeking experiment with the limited communication on three E-puck robots


l  Winner-take-all

The winner-take-all (WTA) competition is widely observed in both inanimate and biological media and society. Many mathematical models are proposed to describe the phenomena discovered in different fields. These models are capable of demonstrating the WTA competition. However, they are often very complicated due to the compromise with experimental realities in the particular fields; it is often difficult to explain the underlying mechanism of such a competition from the perspective of feedback based on those sophisticate models. In this work, we make steps in that direction and present a simple model, which produces the WTA competition by taking advantage of selective positive–negative feedback through the interaction of neurons. Compared to existing models, this model has an explicit explanation of the competition mechanism. The ultimate convergence behavior of this model is proven analytically. Both theoretical and numerical results validate the effectiveness of the dynamic equation in describing the nonlinear phenomena of WTA competition. The proposed model is as simple as follows in a compact form, 

where u is the input vector, x is the state vector, c0 and c1 are two positive constants.


l  Simultaneous Localization and Mapping (SLAM)

Data association is a fundamental problem in multi-sensor fusion, tracking, and localization. The joint compatibility test is commonly regarded as the true solution to the problem. However, traditional joint compatibility tests are computationally expensive, are sensitive to linearization errors, and require the knowledge of the full covariance matrix of state variables. This work proposes a posterior-based joint compatibility test scheme to conquer the three problems mentioned above.  This work also shows how to apply the proposed method to various simultaneous localization and mapping (SLAM) algorithms. Theoretical analysis and experiments on both simulated data and popular datasets show the proposed method outperforms some classical algorithms, including sequential compatibility nearest neighbor (SCNN), random sample consensus (RANSAC), and joint compatibility branch and bound (JCBB), on precision, efficiency, and robustness.

Experiment on the Intel Research Center dataset. (a) shows the map constructed by overlapping raw observations according to the optimized poses, and in the figure, green points are raw laser observations; blue triangles are robot poses; and randomly colored blobs are extracted features. (b) shows the generated graph, and in the figure, green lines indicate links of associations; yellow stars denote landmarks; and blue triangles are robot poses. The convergence of the map and the links in the graph show that the proposed method has high detection rate and low false positive rate.


Current Students

PhD Students

Aquil Mirza Mohammed

Muhammad Usman Rafique

Yinyan Zhang

Postdoctoral Fellow

Long Jin


Journal papers

S. Li, M. Zhou, X. Luo and Z. You, Distributed Winner-take-all in Dynamic Networks,  Regular paper, IEEE Transactions on Automatic Control, 2016.

S. Li, Y. Zhang and L. Jin, Kinematic Control of Redundant Manipulators Using Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, 2016.

S. Li, J. He, U. Rafique, and Y. Li, Distributed Recurrent Neural Networks for Cooperative Control of Manipulators: A Game-Theoretic Perspective, IEEE Transactions on Neural Networks and Learning Systems, 2016.

L. Jin, Y. Zhang, S. Li and Y. Zhang, Noise-Tolerant ZNN Models for Solving Time-Varying Zero-Finding Problems: A Control-Theoretic Approach, Brief paper, IEEE Transactions on Automatic Control, 2016.

X. Luo, M. Zhou, M. Shang, S. Li and Y. Xia, A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices, IEEE Access, 2016.

L. Jin, Y. Zhang, S. Li, Integration-Enhanced Zhang Neural Network for Real-Time Varying Matrix Inversion in the Presence of Various Kinds of Noises, IEEE Transactions on Neural Networks and Learning Systems, 2016.

M. Mao, J. Li, L. Jin, S. Li and Y. Zhang, Enhanced discrete-time Zhang neural network for time-variant matrix inversion in the presence of bias noises, Neurocomputing, 2016.

Z. You, M. Zhou, X. Luo, S. Li, Highly Efficient Framework for Predicting Interactions Between Proteins, IEEE Transactions on Cybernetics, 2016.

M. Khan, S. Li, Q. Wang and Z. Shao, Distributed Multi-robot Formation and Tracking Control in Cluttered Environment, ACM Transactions on Autonomous and Adaptive Systems, 2016.

H. Wang, X. Liu, P. Liu and S. Li, Robust adaptive fuzzy fault-tolerant control for a class of non-lower-triangular nonlinear systems with actuator failures, Information Sciences, 60-74,2016

Y. Wang, Z. Qin, R. Chen, Z. Shao, Q. Wang, S. Li and L. Yang, A Real-Time Flash Translation Layer for NAND Flash Memory Storage Systems, IEEE Transactions on Multi-Scale Computing Systems, 2016.

M. Khan, S. Li, Q. Wang and Z. Shao, CPS Oriented Control Design for Networked Surveillance Robots with Multiple Physical Constraints, IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems,2016.

M. Khan, S. Li, Q. Wang and Z. Shao, Formation Control and Tracking for Co-operative Robots with Non-holonomic Constraints, Journal of Intelligent and Robotic Systems, 82(1): 163-174, 2016.

H. Wang, H. Yang, X. Liu, L. Liu, S. Li, Direct adaptive neural control of nonlinear strict-feedback systems with un-modeled dynamics using small-gain approach, International Journal of Adaptive Control and Signal Processing, 2016.

X. Luo, M. Zhou, H. Leung, Y. Xia, Q. Zhu, Z. You, S. Li, An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering, IEEE Transactions on Automation Science and Engineering, 13(1), 333-343, 2016.

X. Luo, M. Zhou, S. Li, Z. You, Y. Xia, and Q. Zhu. A Non-negative Latent Factor Model for Large-scale Sparse Matrices in Recommender Systems via Alternating Direction Method. IEEE Transactions on Neural Networks and Learning Systems, 27(3), pp 579 - 592, 2016.

L. Wong, Z. You, S. Li, Y. Huang, and G. Liu. Detection of protein-protein interactions from amino acid sequences using a rotation forest model with a novel PR-LPQ descriptor, Advanced Intelligent Computing Theories and Applications, pp. 713-720, 2016.

X. Luo, M. Zhou, S. Li, Z. You, Y. Xia, Q. Zhu, and H. Leung. An Efficient Second-order Approach to Factorizing Sparse Matrices in Recommender Systems. IEEE Transactions on Industrial Informatics, 2015, 11(4): 946-956.

X. Luo, Z. Ming, Z. You, S. Li, Y. Xia, and H. Leung. Improving Network Topology-based Protein Interactome Mapping via Collaborative Filtering, Knowledge-Based Systems, 90: 23-32, 2015.

Y. Huang, Z. You, X. Luo, S. Li, L. Huang. Improved Protein-Protein Interactions Prediction via Weighted Sparse Representation Model combining Continuous Wavelet Descriptor and PseAA Composition, International Journal of Genomics, 2015.

S. Li, Z. You, H. Guo, X. Luo, Z. Zhao, Inverse-free Extreme Learning Machine with Optimal Information Updating, IEEE Transactions on Cybernetics, 2015.

Z. Wang, X. Liu, K. Liu, S. Li, H. Wang, Backstepping-based Lyapunov Function Construction using Approximate Dynamic Programming and Sum of Square Techniques, IEEE Transactions on Cybernetics, 2016.

A. Muhammad, S. Li, Dynamic Neural Networks for Kinematic Redundancy Resolution of Parallel Stewart Platforms, IEEE Transactions on Cybernetics, 2015.

X. Luo, M. Zhou, S. Li, Z. You, Y. Xia, Q. Zhu and H. Leung, A Hessian-free Optimization-based Approach to Factorizing Incomplete Matrices in Recommender Systems, IEEE Transactions on Industrial Informatics, 2015..

X. Luo, Z. You, M. Zhou, S. Li, H. Leung, Y. Xia, Q. Zhu, A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework, Scientific reports, Nature, 2015.

Z. You, S. Li, X. Gao, X. Luo, Z. Ji, Large-scale Protein-protein Interactions Detection by Integrating Big Biosensing Data with Computational Model, BioMed research international, 2014.

L. Cheng, S. Yang, S. Li, Deployment and Tracking in Distributed Sensor Networks, International Journal of Distributed Sensor Networks, 2014.

S. Li, C. Pham, A. Jaekel, M. Matin, A. Amin, Y. Li Editorial: Perception, Reaction, and Cognition in Wireless Sensor Networks, International Journal of Distributed Sensor Networks, 2013.

S. Li, R. Kong, and Y. Guo, Cooperative Distributed Source Seeking by Multiple Robots: Algorithms and experiments, IEEE/ASME Transactions on Mechatronics, 2014

S. Li and Y. Guo, Distributed Consensus Filtering on Directed Switching Graphs, International Journal of Robust and Nonlinear Control, 2014.

S. Li, Y. Lou and B. Liu, Bluetooth Aided Mobile Phone Localization: a Nonlinear Neural Circuit Approach, ACM Transactions on Embedded Computing Systems, 2014, 13

S. Li and Y. Li, Nonlinearly Activated Neural Network for Solving Time-varying Complex Sylvester Equation, IEEE Transactions on Cybernetics, 2013.

S. Li, B. Liu and Y. Li, Selective Positive-negative Feedback Produces the Winner-take-all Competition in Recurrent Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(2), 301-309

Y. Li, S. Li, M. Meng, Q. Song, Fast and Robust Data Association Using Posterior Based Approximate Joint Compatibility Test, IEEE Transaction on Industrial Informatics, 2013.

S. Li, Y. Li and Z. Wang, A Class of Finite-time Dual Neural Networks for Solving Quadratic Programming Problems and Its k-winners-take-all Application, Neural Networks, 2013, 39: 27-39

S. Li and Y. Guo, Dynamic Consensus Estimation of Weighted Average on Directed Graphs, International Journal of Systems Science, 2013.

S. Li, F. Qin, A Dynamic Neural Network Approach for Solving Nonlinear Inequalities Defined on A Graph and Its Application to Distributed, Routing-free, Range-free Localization of WSNs, Neurocomputing, 2013, 117: 72–80

S. Li, Y. Guo, J. Fang, and H. Li, Average consensus with weighting matrix design for quantized communication on directed switching graphs, International Journal of Adaptive Control and Signal Processing, 2013, 27: 519-540.

S. Li, Y. Li, B. Liu, et al, Model-free Control of Lorenz Chaos Using an Approximate Optimal Control Strategy, Communications in Nonlinear Science and Numerical Simulation, 2012, 17: 4891-4900.

S. Li, Y. Wang, J. Yu, et al. A Nonlinear Model to Generate the Winner-take-all Competition, Communications in Nonlinear Science and Numerical Simulation, 2013, 18: 435–442.

S. Li, H. Cui, Y. Li, et al. Decentralized Control of Collaborative Redundant Manipulators with Partial Command Coverage via Locally Connected Recurrent Neural Networks, Neural Computing and Applications. 2013, 23:1051-1060

S. Li, Z. Wang, Y. Li, Using Laplacian Eigenmap as Heuristic Information to Solve Nonlinear Constraints Defined on A Graph and Its Application in Distributed Range-free Localization of Wireless Sensor Networks, Neural Processing Letters, 2013, 37:411–424.

S. Li, S. Chen and B. Liu, Accelerating a Recurrent Neural Network to finite-time Convergence for Solving Time-varying Sylvester Equation by Using a Sign-Bi-Power Activation Function, Neural Processing Letters, 2013, 37:189–205

S. Li, J. Yu, M. Pan, et al, Winner-take-all based on discrete-time dynamic feedback, Applied mathematics and Computation, 2012, 219: 1569-1575

S. Li, S. Chen, B. Liu, et al, Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks, Neurocomputing, 2012, 91:1-10

S. Li, B. Liu, B. Chen, et al. Neural Network Based Mobile Phone Localization Using Bluetooth Connectivity, Neural Computing and Applications, 2013, 23: 667-675.

Y. Li, S. LiA Biologically Inspired Solution to Simultaneous Localization and Consistent Mapping in Dynamic Environments, Neurocomputing, 2013, 104:170-179.

S. Chen, S. Li, B. Liu, et al. Self-learning Variable Structure Control for a Class of Sensor-actuator Systems. Sensors. 2012, 12: 6117-6128.

L. Sun, M. MengS. Li, et al: A Novel CPG with Proprioception and its Application on the Locomotion Control of Quadruped Robot. International Journal on Information Acquisition, 2009, 6: 33-46.



Conference Papers

S. Steinhorst, Z. Shao, S. Chakraborty, M. Kauer, S. Li, M. Lukasiewycz, S. Narayanaswamy, M. Rafique, Q. Wang: Distributed reconfigurable Battery System Management Architectures, 22nd Asia and South Pacific Design Automation Conference, ASP-DAC 429-434, 2016.

Z. He, S. Li, Z. Shen, M. Khan, Z. Shao and Q. Wang, WiP Abstract: A Quadcopter Swarm for Active Monitoring of Smog Propagation, International Conference on Cyber-Physical Systems, 2015.

Q. Huang, Z. You, S. Li, Z. Zhu, Using Chou’s amphiphilic Pseudo-Amino Acid Composition and Extreme Learning Machine for prediction of Protein-protein interactions. International Joint Conference on Neural Networks (IJCNN), 2952-2956, 2014.

S. Li, Y. Guo, and B. Bingham, Multi-Robot Cooperative Control for Monitoring and Tracking Dynamic Plumes, IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, June 2014.

S. Li and Y. Guo, Discrete-time Consensus Filters on Directed Switching Graphs, IEEE International Conference on Control and Automation (ICCA), Taiwan, June 18-20, 2014.

S. Li, Y. Guo and Y. Braiman, Synchronizing Coupled Semiconductor Lasers under General Coupling Topologies, American Control Conference (ACC), Washington DC, June 17 - July 19, 2013

S. Li and Y. Guo, Distributed Consensus Filter on Directed Graphs with Switching Topologies, American Control Conference (ACC), Washington DC, June 17 - July 19, 2013.

S. Li and Y. Guo, Distributed Source Seeking by Cooperative Robots: All-to-All and Limited Communications, IEEE International Conference on Robotics and Automation (ICRA), Minneapolis, USA, May 14-18, 2012

S. Li, Y. Guo, J. Fang and H. Li, Robust H_infinity Consensus on Directed Networks with Quantized Communication, The Seventh IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Hoboken, NJ, June 17-20, 2012

Y. Lu, S. Li and Y. Guo, Synchronization of Coupled Laser Arrays with All-to-all and Limited Coupling Topology, ASME Dynamic Systems and Control Conference, FL, Oct. 17-19, 2012

S. Li, S. Chen, Y. Lou, et al. A Recurrent Neural Network for Inter-localization of Mobile Phones. International Joint Conference on Neural Networks (IJCNN), Brisbane, Austrilia, June, 2012

S. Li and Y. Guo, Neural-Network Based AUV Path Planning in Estuary Environments, The 10th World Congress on Intelligent Control and Automation(WCICA2012), Beijing, China, 2012. 

S. Li, M.Q.H. Meng, W. Chen, et al. SP-NN: A Novel Neural Network Approach for Path Planning, International Conference on Robotics and Biomimetics (ROBIO). Sanya, China. 2007

S. Li, Z. Zhang, Y. Ma. A Sensor Networks Based Method for Detecting Mine Methane, International Conference on Information Acquisition (ICIA), Jeju City, Korea, 2007:403-407

S. Li, M.Q. H. Meng, H. Liang, et al. A Localization Error Estimation Method Based on Maximum Likelihood for Wireless Sensor Networks, IEEE International Conference on Mechatronics and Automation(ICMA), Harbin, China, 2007:348-353



Book Chapters

S. Li, Y. Wang and C. Long, Securing the Control of Euler–Lagrange Systems in networked environments with Model-Free Sliding Mode Control, in the book Security for multi-hop wireless networks, Publisher: CRC Press, 2014.

S. Li and Y. Li, Chapter 12: Distributed Range-free Localization of Wireless Sensor Networks via Nonlinear Dynamics, in the book wireless sensor networks, edited by M. Matin, Publisher: InTech, 2012.

S. Zhang, S. Li and Y. Guo, Chapter 7: Cooperative Control Design for Nanorobots in Drug Delivery. In Book: Selected Topics in Micro/nano-robotics for Biomedical Applications, edited by Y. Guo, Springer, New York, 2012.




Y. Li, S. Li, Y. Sun, et al, A Method and Means to Generate 3D Point Clouds from Coloured Lidar, Chinese Patent CN103308925A, May, 2013. 

H. Liang, S. Li, W. Chen, et al, A Mapping System and Its Corresponding Method for Mobile Robots Navigation, Chinese Patent 200710019784.2, 16, Feb. 2007




COMP436: Distributed Objects and Middleware (Fall 2014, Fall 2015, Fall 2016)

COMP5228: Embedded Software Engineering (Spring 2015, Spring 2016)

COMP2222: Introduction to Human Computer Interaction (Spring 2016)



Distributed Winner-Take-All for Competitive Control of Networked Systems, Sponsor: Hong Kong Research Grants Council (RGC) Early Career Scheme (ECS), PI. Project No. 25214015, Duration: 2015-2018.

Limited Communication Constrained Multi-robot Cooperation based on Distributed Dynamic Neural Networks, Sponsor: National Science Foundation of China (NSFC), PI. Project No. 61401385, Duration: 2015-2018.

Software Defined Battery: A Novel Architecture for Large Scale Battery. Sponsor: Hong Kong Polytechnic University Departmental General Research Fund (DGRF), PI. Project No. G-UA7L, Duration: 2015-2018.



Reviewers for the following journals and conferences:

IEEE Transactions on Signal Processing (TSP)

IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

IEEE/ASME Transactions on Mechatronics (TMECH)

Transactions of the Institute of Measurement and Control (TIMC)

Signal Processing Letter (SPL)

Neural Processing Letters (NEPL)

International Journal of Communication Systems (IJCS)

International Journal of Computer and Information Technology (IJCIT)

Journal of Communication and Computer (JCC)

IEEE International Conference on Robotics and Automation (ICRA)

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

American Control Conference (ACC)

European Control Conference (ECC)

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