Dr Hai Wang
PhD

I received the B.E. degree from Hebei Polytechnic University (now known as North China University of Science and Technology), China, in 2007, the M. E. degree from Guizhou University, China, in 2010, and the Ph.D degree from Swinburne University of Technology (SUT), Australia, in 2014, respectively, all in Electrical and Electronic Engineering. From 2014 to 2015, I was the Postdoc Research Fellow in the Faculty of Science, Engineering, and Technology, at SUT, Australia. From 2015 to 2019, I was with the School of Electrical and Automation Engineering at Hefei University of Technology, China, where I served as the full Professor (Huangshan Young Scholar) and the Deputy Discipline Head of Automation. In early 2019, I joined  Murdoch University as a Senior Lecturer of Electrical Engineering (tenured) and served as Academic Chair of Instrumentation and Control Engineering (ICE), Industrial Computing Systems Engineering (ICSE) in 2020-2022, and currently serving as Academic Chair of Intelligent Industrial Control & Autonomous Systems Engineering (IICASE), in the School of Engineering and Energy, College of Science, Technology, Engineering and Mathematics.

I am also leading the Mechatronics, and Autonomous Robotic Systems (MARS) Research Laboratory at Murdoch University, located in Building 807. Please feel free to contact me if you would like to visit the MARS lab and discuss about potential research and project opportunities. 

My research interests are in sliding mode control, adaptive control, robotics & mechatronics, nonlinear systems, neural networks, artificial intelligence, autonomous vehicles and systems (drones, ground robots, and underwater vehicles), Industry 4.0 and smart agriculture, etc. I am the author of over 100 refereed journal papers including (30+ IEEE Transactions papers, 80+ Q1 journal papers).

I am a senior member of IEEE, Chair of IEEE-IES WA Chapter (2020), and also serving as Section Editor-in-Chief of Actuators, Section Editor-in-Chief of Robotics, Associate Editor of Computers and Electrical Engineering, IEEE Access, ASME-Journal of Autonomous Vehicles and Systems, IET-Energy Conversion and Economics, Lead Guest Editor of Neural Computing and Applications, Computers and Electrical Engineering, Actuators, from 2020.

I am constantly looking for highly potential research students. If you are interested in my research areas, please see the following ‘Doctoral and Masters Supervisions’ section in detail.

I am currently teaching the following units:

• ENG214: Electrical and Electronics Circuits
• ENG225: Circuits and Systems I
• ENG297: Circuits and Systems II
• ENG321: Instrument and Communication Systems
• ENG336: Engineering Finance, Management and Law
• ENG500: Engineering Finance, Management, Ethics and Law
• ENG552: Industrial Control Systems

I am currently teaching and coordinating the following units:

• ENG214: Electrical and Electronics Circuits
• ENG225: Circuits and Systems I
• ENG297: Circuits and Systems II
• ENG321: Instrument and Communication Systems
• ENG336: Engineering Finance, Management and Law
• ENG500: Engineering Finance, Management, Ethics and Law
• ENG552: Industrial Control Systems

My research interests are

• Sliding mode control and adaptive control
• Fault diagnosis
• Robotics & mechatronics
• Nonlinear systems
• Neural networks and artificial intelligence
• Autonomous vehicles and systems
• Industry 4.0 and smart agriculture

My current projects are listed as follows:

• Real-time monitoring system for glasshouse crops via intelligent autonomous robots
• Nonlinear control theory (sliding mode control, adaptive control, etc)
• Control and estimation of vehicle lateral and longitudinal dynamics (focusing on by-wire systems, such as steer-by-wire, brake-by-wire, throttle-by-wire, etc)
• Advanced control of autonomous vehicles
• Robust motion control and observation design of mechatronic systems
• Motion planning, control, navigation of autonomous robots in indoor and outdoor environments (agriculture, minding, livestock, etc)
• Multi-agent mobile robots
• AI and neural networks for pattern classifications

Grants

• 2023-2026: Matching fund of Murdoch University Strategic Partnership Scholarship $58,500, funded by Shengtaixin Culture Technology Co., Ltd., CI Dr Hai Wang, “Novel pattern recognition in guidance of mobile systems in natural environments” 2023.
• 2021-2022: Murdoch University Seed Grant, “Real-time monitoring system for glasshouse crops via intelligent autonomous robots”, Leading CI, $20,000
• 2021-2024: National Natural Science Funds of China (National Natural Science Foundation of China), ““Research on active security control for networked complex systems with hybrid information-physical faults”, CI, $140,000(equivalent)
• 2019-2020: Murdoch University College of SHEE Small Grants, “Multisource Information Fusion-based Detection for Fatigue Driving via Extreme Learning machine”, Leading CI, $15,000
• 2019-2020: Murdoch University College of SHEE Small Grants, “Developing a configurable autonomous framework for BlueROV2 underwater vehicle used for underwater species monitoring and data collection”, CI, $10,000
• 2019-2021: Murdoch University Start-up Grant, “Control and parameter estimation of mechatronic systems”, Leading CI, $20,000
• 2016-2018: Fundamental Research Funds for the Central Universities (Ministry of Education of China), “Observer designs and control implementation of industrial mechatronics systems”, Leading CI, $70,000(equivalent)
• 2015-2018: National Natural Science Funds of China (National Natural Science Foundation of China), “Steer-by-wire control and observation”, Leading CI, $95,000(equivalent)
• 2017-2018: Innovative Research Fund Scheme by Hefei local government, “Research in the development of hand-held fault diagnosis instrument for new energy vehicles”, Leading CI, $160,000(equivalent)

• Section Editor-in-Chief of Actuators (MDPI), 2020-present
• Section Editor-in-Chief of Robotics (MDPI), 2022-present
• Associate Editor of Computers and Electrical Engineering, 2022-present
• Associate Editor of ASME Journal of Autonomous Vehicles and Systems, 2021-present
• Associate Editor of IEEE Access, 2020-present
• Associate Editor of IET Energy Conversion and Economics, 2020-present
• Lead Guest Editor for a special issue of Actuators 2021, Neural Computing and Applications, Computers and Electrical Engineering, and IET Power Electronics, 2020
• Fellow of Higher Education Academy
• Senior Member of IEEE, Member of IEEE Industrial Electronics Society (IES)
• Chair of IEEE IES WA Chapter (2020)
• Regular Reviewer’s of IEEE Transactions, Elsevier, Taylor-Francis and Wiley Journals, and conferences

I am constantly seeking highly potential HDR students, in the research areas of control theory, mechatronics, artificial intelligence, and vehicle dynamics & control. The scholarships for PhD and Master candidates waive tuition fee and provide with a living allowance in the range ~ $26k-30k annually.

International PhD candidates can apply for:

• International Postgraduate Research Studentship (IPRS) (deadline 30 Sep)
• Murdoch International Postgraduate Studentship (MIPS) (deadline 30 Sep)
• Endeavour Scholarship (open in late April; deadline 30 June)
• Australia Awards Scholarship (selected countries, between Jan to April, check website)
• China Scholarship Council (for Chinese candidates, March-April, check website)

International Master (by research) candidates can apply for:

• Australia Awards Scholarship (selected countries, between Jan to April, check official website)
• China Scholarship Council (for Chinese candidates, March-April, check official website)

Domestic PhD and Master (by research) candidates can apply for:

• Australian Postgraduate Awards (for both PhD and Master studies; deadline 31 Oct)
• Murdoch University Research Studentship (MUR) (for PhD only, deadline 31 Oct)

Postdocs can apply for:

• Endeavour Scholarships & Fellowships (open in April; deadline 30 June)
• China Scholarship Council (for Chinese candidates, deadline Jan, check official website)

Details of the application process for international students can be found here, and for domestic students can be found here.

If you are interested, please feel free to contact me via email: hai.wang@murdoch.edu.au. Apologies for not being able to promptly answer your inquiry emails about the application.

The detailed publications can be found on Google Scholar page.

Referred Book Chapter:

1. H. Wang*, Y. Hu, M. Ye, J. Zhang, Z. Cao, J. Zheng, and Z. Man, “Real-time control systems with applications in mechatronics,” in Handbook of Real-time Computing (Springer), available online, 2021. doi: https://doi.org/10.1007/978-981-4585-87-3_41-1

Refereed Journal Articles:

2023

• Z. Li, L. Chen, and H. Wang, “Fixed-time Sliding Mode-based Adaptive Path Tracking Control of Maize Plant Protection Robot via Extreme Learning Machine,” IEEE Robotics and Automation Letters, accepted, 2023.
• Y. Hou, H. Wang, Y. Wei, H. Iu, and T. Fernando, “Robust adaptive finite-time tracking control for intervention-AUV with input saturation and output constraints using high-order control barrier function,” Ocean Engineering, vol. 268, 2023.
• W. Jiang, C. Song, H. Wang, M. Yu, and Y. Yan, “Obstacle detection of autonomous vehicle: an adaptive neighborhood search radius clustering approach,” Machines, vol. 11, no. 1, pp. 54, 2023.
  
• M. Tousizadeh, A. Yazdani, H. Che, H. Wang, A. Mahmoudi, and N. Rahim, “A Generalized fault tolerant control based on back EMF feedforward compensation: derivation and application on induction motors drives,” Energies, vol. 16, no. 1, pp. 51, 2023.
  
• E. Kurniawan, H. Wang, H. Septanto, H. Adinanta, S. Suryadi, H. Pratomo, and D. Hanto, “Design and analysis of higher-order repetitive sliding mode controller for uncertain linear systems with time-varying periodic disturbances,” Transactions of the Institute of Measurement and Control, accepted, 2023.
  
• Z. Huang, Y. Tu, H. Fang, H. Wang, L. Zhang, K. Shi, and S. He, “Off-policy reinforcement learning for tracking control of discrete-time markov jump linear systems with completely unknown dynamics,” Journal of the Franklin Institute, available online, 2023.
  
• Z. Li, D. Xie, L. Liu, H. Wang, and L. Chen, “Inter-row information recognition of maize in middle and late stages via LiDAR supplementary vision,” Frontiers in Plant Science, available online, 2023.
  
• E. Kurniawan, J. Prakosa, H. Wang*, S. Wijonarko, T. Maftukhah, P. Purwowibowo, H. Septanto, E. Pratiwi, and D. Rustandi, “Design of fractional order odd-harmonics repetitive controller for discrete-time linear systems with experimental validations,” Sensors, vol. 22, pp. 8873, 2022.
• M. RaofI, H. Habibi, A. Yazdani, and H. Wang*, “Robust prescribed trajectory tracking control of a robot manipulator using adaptive finite-time sliding mode and extreme learning machine method,” Robotics, vol. 11, no. 5, pp. 111, 2022.
• W. Guo, M. Ma, H. Wang, H. Wang, Q. Song, and W. Chen, “A Partition Decoupling Algorithm for Compact Thermal Model in Multi-Chip IGBT Modules,” IEEE Transactions on Power Electronics, vol. 38, no. 1, pp. 66-72, 2023.
• S. Xu, S. Huang, H. Wang*, W. Zheng, J. Wang, Y. Chai, and M. Ma, “A simultaneous diagnosis method for power switch and current sensor faults in grid-connected three-level NPC inverters,”  IEEE Transactions on Power Electronics, vol. 38, no. 1, pp. 1104-1118, 2023. doi: 10.1109/TPEL.2022.3200721

2022

• X. Wang, S. Yao, C. Qu, Y. Wang, Z. Xu, W. Huang, and H. Wang*, “Direct thrust force control of primary permanent magnetic linear motor based on improved extended state observer and model free adaptive predictive control,” Actuators, vo. 11, no. 10, pp. 270, 2022.
• D. Xie, L. Chen, L. Liu, L. Chen, and H. Wang*, “Actuators and Sensors for Application in Agricultural Robots: A Review,” Machines, vol. 10, no. 10, pp. 913, 2022.
• X. Jin, M. Gao, W. Che, and H. Wang, “Adaptive ELM-based event-triggered control for perturbed euler lagrange systems,” International Journal of Robust and Nonlinear Control, accepted, 2022.
• E. Kurniawan, H. Harno, H. Wang*, J. Prakosa, B. Sirenden, H. Septanto, H. Adinanta, and A. Rahmatillah, “Robust adaptive repetitive control for unknown linear systems with odd-harmonics periodic disturbances,” Science China Information Sciences, available online, 2022. doi: https://doi.org/10.1007/s11432-022-3561-2
• Z. Ping, Y. Jia, Y. Li, Y. Huang, H. Wang, and J. Lu, “Global position tracking control of PMSM servo system via internal model approach and experimental validations,” International Journal of Robust and Nonlinear Control, available online, 2022. doi: https://doi.org/10.1002/rnc.6317
• S. Ding, Q. Hou, and H. Wang, “Disturbance-Observer-Based Second-Order Sliding Mode Controller for Speed Control of PMSM Drives,” IEEE Transactions on Energy Conversion, available online, 2022.  doi: 10.1109/TEC.2022.3188630
• H. Xie, J. Zheng, Z. Sun, H. Wang, and R. Chai, “Finite-time tracking control for nonholonomic wheeled mobile robot using adaptive fast nonsingular terminal sliding mode,” Nonlinear Dynamics, available online, 2022. doi: https://doi.org/10.1007/s11071-022-07682-2
• X. Jin, W. Che, Z. Wu, and H. Wang, “Analog Control Circuit Designs for a Class of Continuous-time Adaptive Fault-tolerant Control Systems,” IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 4209-4220, 2022. doi: 10.1109/TCYB.2020.3024913
• K. Shao, J. Zheng, H. Wang, and Z. Man, “Terminal time regulator-based exact-time sliding mode control for uncertain nonlinear systems,” International Journal of Robust and Nonlinear Control, available online 2022. doi: https://doi.org/10.1002/rnc.6231
  
• Q. Zhu, Z. Man, Z. Cao, J. Zheng, and H. Wang, “Parameter Estimation for Robotic Manipulator Systems,” Machines, vol. 10, no. 5, pp. 392, 2022
• W. Guo, M. Ma, H. Wang, N. Xiang, H. Wang, Z. Chen, and W. Chen, “Real time average junction temperature estimation for multichip IGBT modules with low computational cost,” IEEE Transactions on Industrial Electronics, available online, 2022. doi: 10.1109/TIE.2022.3176284
• W. Ma, M. Ma, H. Wang, Z. Zhang, R. Zhang, and J. Wang, “Shading fault detection method for household photovoltaic power stations based on inherent characteristics of monthly string current data mapping,” CSEE Journal of Power and Energy Systems, accepted, 2022.
• H. Fang, Y. Tu, H. Wang, S. He, F. Liu, and Z. Ding, “Fuzzy-Based Adaptive Optimization of Unknown Discrete-Time Nonlinear Markov Jump Systems with Off-Policy Reinforcement Learning,” IEEE Transactions on Fuzzy Systems, Early Access, 2022. doi: 10.1109/TFUZZ.2022.3171844
• X. Zhang, H. Wang, J. Song, S. He, and C. Sun, “Co-Design of Adaptive Event Generator and Asynchronous Fault Detection Filter for Markov Jump Systems via Genetic Algorithm,” IEEE Transactions on Cybernetics, Early Access, 2022. doi: 10.1109/TCYB.2022.3170110
• Y. Tu, H. Fang, H. Wang, and S. He, “Reinforcement Learning-Based Adaptive Optimal Tracking Algorithm for Markov Jump Systems with Partial Unknown Dynamics,” Optimal Control, Applications and Methods, accepted, 2022.
• S. MahmoudZadeh, A. Abbasi, A. Yazdani, H. Wang, and Y. Liu, “Uninterrupted Path Planning System for Multi-USV Sampling Mission in a Cluttered Ocean Environment,” Ocean Engineering, vol. 254, pp. 111328, 2022.
• Z. Zhang, M. Ma, H. Wang, and P. Yun, “An optimal grid-connected strategy for improving the DC voltage utilization rate by increasing the number of PV modules connected in series in the string,” Solar Energy, vol. 236, vol. 687-702, 2022.
  
• H. Wang*, J. Zheng, Y. Lu, S. Ding, and H. Chaoui, “Special issue on computational intelligence-based modeling, control and estimation in modern mechatronic systems,” Neural Computing and Applications, vol. 34, no. 5011-5013, 2022. doi: https://doi.org/10.1007/s00521-021-06818-6
  
• H. Wang*, S. Ding, and K. Nam, “Guest Editorial: Introduction To The Special Section On Recent Advances and Challenges in Intelligent Sliding Mode Control for Modern Industrial Systems: Soft Computing Solutions,” Computers and Electrical Engineering, vol. 99, pp. 107782, 2022.
• X. Wang, B. Xu, Y. Cheng, H. Wang, and F. Sun, “Robust Adaptive Learning Control of Space Robot for Target Capturing Using Neural Network,” IEEE Transactions on Neural Networks and Learning Systems, Early Access, 2022. doi: 10.1109/TNNLS.2022.3144569
• L. Chen, B. Yan, H. Wang*, K. Shao, E. Kurniawan, and G. Wang, “Extreme-learning-machine-based robust Integral Terminal Sliding Mode Control of Bicycle Robot,” Control Engineering and Practice, vol. 121, pp. 105064, 2022. doi: https://doi.org/10.1016/j.conengprac.2022.105064
• H. Chen, Y. Tu, H. Wang, K. Shi, and S. He, “Fault-Tolerant Tracking Control Based on Reinforcement Learning with Application to a Steer-by-wire System,” Journal of the Franklin Institute, vol. 359, no. 3, pp. 1152-1171, 2022. doi: https://doi.org/10.1016/j.jfranklin.2021.12.012
• Z. Zhao, X. Jin, X. Wu, H. Wang, and J. Chi, “Neural Network-Based Fixed-Time Sliding Mode Control for a Class of Nonlinear Euler-Lagrange Systems,” Applied Mathematics and Computation, vol. 415, pp. 126718, 2022.
• X. Xin, Y. Tu, V. Stojanovic, H. Wang, S. He, and T. Pan, “Online Reinforcement Learning Multiplayer Non-Zero Sum Games of Continuous-Time Markov Jump Linear Systems,” Applied Mathematics and Computation, vol. 412, pp. 126537, 2022. doi: https://doi.org/10.1016/j.amc.2021.126537

2021

• C. Xiao, M. Yu, H. Wang, B. Zhang, D. Wang, “Prognosis of Electric Scooter With Intermittent Faults: Dual Degradation Processes Approach,” IEEE Transactions on Vehicular Technology, vol. 71, no. 2, pp.1411-1425, 2021. doi: 10.1109/TVT.2021.3131998
• M. Yu, C. Xiao, H. Wang*, W. Jiang, R. Zhu, “Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System Under Intermittent Fault,” Actuators, vol. 10, no. 11, 2021. doi: https://doi.org/10.3390/act10110283
• Q. Wang, Z. Bai, Z. Li, D. Xie, L. Chen, H. Wang*, “Straw/Spring teeth Interaction Analysis of Baler Picker in Smart Agriculture via a ADAMS-DEM Coupled Simulation Method,” Machines, vol. 9, no. 296, 2021. doi: https:// doi.org/10.3390/machines9110296
• P. Ghanooni, H. Habibi, A. Yazdani, H. Wang, S. MahmoudZadeh, A. Mahmoudi, “Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers,” Electronics, vol. 10, no. 21, pp. 2637, 2021. doi: https://doi.org/10.3390/electronics10212637
• X. Liu, B. Xu, Y. Cheng, H. Wang, W. Chen, “Adaptive Control of Uncertain Nonlinear Systems via Event-Triggered Communication and NN Learning,” IEEE Transactions on Neural Networks and Learning Systems, available online, 2021. doi: 10.1109/TCYB.2021.3119780
• P. Cheng, H. Wang, V. Stojanovic, S. He, K. Shi, X. Luan, and F. Liu, “Asynchronous fault detection observer for 2D Markov jump systems,” IEEE Transactions on Cybernetics, available online, 2021. doi: 10.1109/TCYB.2021.3112699
• M. Yu, H. Lu, H. Wang*, C. Xiao, D. Lan, and J. Chen, “Computational Intelligence Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process,” Actuators, vol. 10, no. 9, 2021. doi: https://doi.org/10.3390/act10090213
• Y. Hu, H. Wang*, A. Yazdani, and Z. Man, “Adaptive Full Order Sliding Mode Control for Electronic Throttle Valve System with Fixed Time Convergence Using Extreme Learning Machine,” Neural Computing and Applications, accepted, 2021. doi: https://doi.org/10.1007/s00521-021-06365-0
• E. Kurniawan, H. Wang, B. Sirenden, J. Prakosa, H. Adinanta, and S. Suryadi, “Discrete-time modified repetitive sliding mode control for uncertain linear systems,” International Journal of Adaptive Control and Signal Processing, vol. 35, pp. 2245-2258, 2021. doi: https://doi.org/10.1002/acs.3316
• Z. Zhang, M. Ma, H. Wang, H. Wang, W. Ma, and X. Zhang, “A fault diagnosis method for photovoltaic module current mismatch based on numerical analysis and statistics,” Solar Energy, vol. 225, pp. 221-236, 2021. doi: https://doi.org/10.1016/j.solener.2021.07.037
• K. Shao, J. Zheng, H. Wang*, X. Wang, and B. Liang, “Leakage-type adaptive state and disturbance observers for uncertain nonlinear systems,” Nonlinear Dynamics, vol. 105, pp. 2299-2311, 2021. doi: https://doi.org/10.1007/s11071-021-06715-6
• M. Ye, H. Wang*, A. Yazdani, S. He, Z. Ping, and W. Xu, “Discrete-time Integral Terminal Sliding Mode-based Speed Tracking Control for a Robotic Fish,” Nonlinear Dynamics, vol. 105, pp. 359-370, 2021. doi: 10.1007/s11071-021-06591-0
• X. Zhang, H. Wang, V. Stojanovic, S. He, X. Luan, and F. Liu, “Asynchronous Fault Detection for Interval Type-2 Fuzzy Nonhomogeneous Higher-level Markov Jump Systems with Uncertain Transition Probabilities,” IEEE Transactions on Fuzzy Systems, available online, 2021. doi:10.1109/TFUZZ.2021.3086224
• Z. Ping, Y. Li, Y. Song, Y. Huang, H. Wang, and J. Lu, “Nonlinear Speed Tracking Control of PMSM Servo System: A Global Robust Output Regulation Approach,” Control Engineering Practice, vol. 112, pp. 1-10, 2021. doi: https://doi.org/10.1016/j.conengprac.2021.104832
• Q. Hou, S. Ding, L. Ma, and H. Wang, “Fuzzy disturbance observer design for a class of nonlinear SISO systems,” International Journal of Fuzzy Systems, available online, 2021. doi: https://doi.org/10.1007/s40815-021-01116-8
• L. Chen, J. Liu, H. Wang*, Y. Hu, X. Zheng, M. Ye, and J. Zhang, “Robust Control of Reaction Wheel Bicycle Robot via Adaptive Integral Terminal Sliding Mode,” Nonlinear Dynamics, vol. 104, pp. 2291-2302, 2021. doi: https://doi.org/10.1007/s11071-021-06380-9
• X. Jin, S. Lv, H. Wang, and C. Deng, “Robust Adaptive Estimation and Tracking Control for Perturbed Cyber-Physical Systems against Denial of Service,” Applied Mathematics and Computation, vol. 404, pp. 1-17, 2021.
• C. Deng, X. Jin, W. Che, and H. Wang, “Learning-Based Distributed Resilient Fault-Tolerant Control Method for Heterogeneous MASs Under Unknown Leader Dynamic,” IEEE Transactions on Neural Networks and Learning Systems, available online, 2021. doi: 10.1109/TNNLS.2021.3070869
• Y. Hu, H. Wang*, S. He, J. Zheng, Z. Ping, Ke. Shao, Z. Cao, and Z. Man, “Adaptive Tracking Control of an Electronic Throttle Valve Based on Recursive Terminal Sliding Mode,” IEEE Transactions on Vehicular Technology, vol. 70, no. 1, pp, 251-262, 2021. doi: 10.1109/TVT.2020.3045778
• Z. Ping, Y. Li, Y. Huang, J. Lu, and H. Wang, “Global robust output regulation of a class of MIMO nonlinear systems by nonlinear internal model control,” International Journal and Robust and Nonlinear Control, vol. 31, pp. 4034-4051, 2021.
• K. Shao, J. Zheng, H. Wang, X. Wang, R. Lu, and Z. Man, “Tracking Control of a Linear Motor Positioner based on Barrier Function Adaptive Sliding Mode,” IEEE Transaction on Industrial Informatics, vol. 17, no. 11, pp. 7479-7488, 2021. doi:  10.1109/TII.2021.3057832
• R. Z. Ekbatani, K. Shao, J. Shannanh, H. Wang, J. Zheng, X. Chen, and M. Nikzad, “Control of an IPMC Soft Actuator Using Adaptive Full-Order Recursive Terminal Sliding Mode,” Actuators, vol. 10, no. 33, 2021. https://doi.org/10.3390/act10020033

2020

• H. Wang*, C. Mi, Z. Cao, J. Zheng, Z. Man, X. Jin, and H. Tang, “Precise Discrete-Time Steering Control for Robotic Fish Based on Data-Assisted Technique and Super-Twisting-like Algorithm,” IEEE Transactions on Industrial Electronics, pp. 1-1, 2020. doi: 10.1109/TIE.2019.2962464
• C. Xiao, M. Yu, B. Zhang, and H. Wang, “Discrete Component Prognosis for Hybrid Systems Under Intermittent Faults,” IEEE Transactions on Automation Science and Engineering, available online, 2020. doi: 10.1109/TASE.2020.3017755
• X. Jin, T. He, X. Wu, H. Wang, and J. Chi, “Robust Adaptive Neural Network-based Compensation Control of a Class of Quadrotor Aircrafts,” Journal of the Franklin Institute, vol. 357, no. 17, pp. 11241-12263, 2020. https://doi.org/10.1016/j.jfranklin.2020.09.009
• W. Guo, M. Ma, H. Wang, S. Yang, X. Zhang, X. Yan, W. Chen, and G. Cai, “A thermal estimation method for IGBT module adaptable to operating conditions,” IEEE Transactions on Power Electronics, vol, 36, no. 6, pp. 6147-6152, 2020. doi: 10.1109/TPEL.2020.3033790
• L. Chen, X. Zhi, H. Wang*, G. Wang, Z. Zhou, A. Yazdani, and X. Z4heng, “Driver Fatigue Detection via Differential Evolution Extreme Learning Machine Technique,” Electronics, vol. 9, no. 11, 2020. doi: https://doi.org/10.3390/electronics9111850
• J. Zhang, H. Wang*, M. Ma, M. Yu, A. Yazdani, and L. Chen, “Active Front Steering-based Electronic Stability Control for Steer-by-Wire Vehicles via Terminal Sliding Mode and Extreme Learning Machine,” IEEE Transactions on Vehicular Technology, vol. 69, no. 2, pp, 14713-14726, 2020. doi: 10.1109/TVT.2020.3036400
• X. Zhang, Y. Yin, H. Wang, and S. He, “Finite-time dissipative control for time-delay Markov jump systems with conic-type nonlinearities under guaranteed cost controller and quantizer,” IET Control Theory and Applications, vol. 15, no. 4, pp. 489-498, 2020. doi: 10.1049/cth2.12031
• M. Yu, H. Lu, H. Wang, C. Xiao, and D. Lan, “Compound Fault Diagnosis and Sequential Prognosis for Electric Scooter with Uncertainties,” Actuators, vol. 9, no. 4, 2020. doi:10.3390/act9040128
• J. Zhang, H. Wang*, J. Zheng, Z. Cao, Z. Man, M. Yu, and L. Chen, “Adaptive sliding mode-based lateral stability control of Steer-by-Wire vehicles with experimental validations,” IEEE Transactions on Vehicular Technology, available online, 2020. doi: 10.1109/TVT.2020.3003326
• K. Shao, J. Zheng, H. Wang, F. Xu, X. Wang, and B. Liang, “Recursive sliding mode control with adaptive disturbance observer for a linear motor positioner,” Mechanical Systems and Signal Processing, vol. 146, 2020. doi:  https://doi.org/10.1016/j.ymssp.2020.107014
• V. Moghaddam, A. Yazdani, H. Wang, D. Parlevliet, and F. Shahnia, “An Online Reinforcement Learning Approach for Dynamic Pricing of Electric Vehicle Charging Stations,” IEEE Access, accepted, 2020.
• M. Ye and H. Wang*, “Robust adaptive integral terminal sliding mode control for steer-by-wire systems based on extreme learning machine,” Computers and Electrical Engineering, vol. 86, 2020. doi: https://doi.org/10.1016/j.compeleceng.2020.106756
• L. Chen, H. Wang*, Y. Huang, Z. Ping, M. Yu, M. Ye, and Y. Hu, “Robust hierarchical terminal sliding mode control of two-wheeled self-balancing vehicle using perturbation estimation,” Mechanical Systems and Signal Processing, vol. 139, 2020. doi: https://doi.org/10.1016/j.ymssp.2019.106584
• C. Liu, Z. Ping, Y. Huang, J. Lu, and H. Wang, “Position control of spherical inverted pendulum via improved discrete-time neural network approach,” Nonlinear Dynamics, 2020. doi: https://doi.org/10.1007/s11071-019-05455-y
• Y. Hu and H. Wang*, “Robust tracking control for vehicle electronic throttle using adaptive dynamic sliding mode and extended state observer,” Mechanical Systems and Signal Processing, vol. 135, pp. 1-18, 2020.
• J. Khawwaf, J. Zheng, H. Wang, and Z. Man, “Practical model-free robust estimation and control design for an underwater soft IPMC actuator,” IET Control Theory and Applications, available online, 2020. doi: 10.1049/iet-cta.2019.1147

2019

• K. Shao, J. Zheng, K. Huang, H. Wang, Z. Man, and M. Fu, “Finite-time control of a linear motor positioner using adaptive recursive terminal sliding mode,” IEEE Transaction on Industrial Electronics, available online, 2019. doi: 10.1109/TIE.2019.2937062
• Z. Ping, T. Wang, Y. Huang, J. Lu, H. Wang, and Y. Li, “Nonlinear internal model control of PMSM position servo system: theory and experimental results,” IEEE Transaction on Industrial Informatics, vol. 16, no. 4, pp. 2202-2211, 2019.
• Y. Hu, H. Wang*, Z. Cao, J. Zheng, Z. Ping, L. Chen, and X. Jin, “Extreme-learning- machine-based FNTSM control strategy for electronic throttle,” Neural Computing and Applications, available online, 2019. doi: https://doi.org/10.1007/s00521-019-04446-9
• J. Zhang, H. Wang*, Z. Cao, J. Zheng, M. Yu, A. Yazdani, and F. Shahnia, “Fast nonsingular terminal sliding mode control for permanent magnet linear motor via ELM,” Neural Computing and Applications, available online, 2019. doi: https://doi.org/10.1007/s00521-019-04502-4
• H. Kong, J. Yan, H. Wang, and L. Fan, “Energy management strategy for electric vehicles based on deep Q-learning using Bayesian optimization,” Neural Computing and Applications, available online, 2019. doi: https://doi.org/10.1007/s00521-019-04556-4
• M. Ye and H. Wang*, “A robust adaptive chattering-free sliding mode control strategy for automotive electronic throttle system via genetic algorithm,” IEEE Access, vol. 8, 99. 68-80, 2019.
• M. Yu, D. Lan, Y. Huang, H. Wang, C. Jiang, and L. Zhao, “Event-based sequential prognosis for uncertain hybrid systems with intermittent faults,” IEEE Transaction on Industrial Informatics, vol. 15, no. 8, pp. 4455-4468, 2019.
• M. Yu, H. Li, W. Jiang, H. Wang, and C. Jiang, “Fault Diagnosis and RUL prediction of nonlinear mechatronic system via adaptive genetic algorithm-particle filter,” IEEE Access, vol. 7, pp. 11140-11151, 2019.
• X. Jin, Y. Zhao, H. Wang*, Z. Zhao, and X. Dong, “Adaptive fault-tolerant control of mobile robots with actuator faults and unknown parameters,” IET Control Theory and Applications, vol. 13, no. 11, pp. 1665-1672, 2019.
• Z. Sun, J. Zheng, Z. Man, H. Wang, K. Shao, and D. He, “Adaptive fuzzy sliding mode control design for vehicle steer-by-wire systems,” Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6601-6612, 2019.
• H. Kong, Y. Fang, L. Fan, H. Wang, X. Zhang, and J. Hu, “A novel torque distribution strategy based on deep recurrent neural network for parallel hybrid electric vehicle,” IEEE Access, vol. 7, pp. 65174-65185, 2019.
• C. Jiang, Z. Guo, X. Li, H. Wang and M. Yu, “An efficient adjoint computational method based on lifted IRK integrator and exact penalty function for optimal control problems involving continuous inequality constraints,” Discrete and Continuous Dynamical Systems Series S, online published, 2019. doi:10.3934/dcdss.2020109

2018

• M. Yu, C. Xiao, W. Jiang, S. Yang, and H. Wang, “Fault diagnosis for electromechanical system via extended analytical redundancy relations,” IEEE Transaction on Industrial Informatics, vol. 14, no. 12, pp. 5233-5244, 2018.
• H. Wang*, Z. Li, X. Jin, Y. Huang, H. Kong, M. Yu, Z. Ping, and Z. Sun, “Adaptive integral terminal sliding mode control for automobile electronic throttle via an uncertainty observer and experimental validation,” IEEE Transactions on Vehicular Technology, vol. 67, no. 9, pp. 8129-8143, 2018.
• H. Wang*, L. Shi, Z. Man, J. Zheng, S. Li, M. Yu, C. Jiang, H. Kong, and Z. Cao, “Continuous fast nonsingular terminal sliding mode control of automotive electronic throttle systems using finite-time exact observer,” IEEE Transactions on Industrial Electronics, vol. 65, no. 9, pp. 7160-7172, 2018.
• Z. Sun, J. Zheng, H. Wang, and Z. Man, “Sliding Mode-Based Active Disturbance Rejection Control for Vehicle Steer-by-Wire Systems”, IET Cyber-Physical Systems: Theory & Applications, vol. 3, no.1, pp. 1-10. 2018.
• C. Jiang, K. Xie, C. Yu, M. Yu, H. Wang, Y. He, and K. Teo, “A sequential computational approach to optimal control problems for differential-algebraic systems based on efficient implicit Runge-Kutta integration,” Applied Mathematical Modelling, vol. 58, pp. 313-330, 2018.

• H. Wang, H. Kong, Z. Man, M. T. Do, Z. Cao, and W. Shen, “Sliding mode control for Steer-by-Wire systems with AC motors in road vehicles,”  IEEE Transactions on Industrial Electronics, vol. 61, no. 3, pp. 1596-1611, 2014.
• H. Wang,  Z. Man, W. Shen, Z. Cao, J. Zheng, J. Jin and M. T. Do, “Robust control
  for Steer-by-Wire systems with partially known dynamics,” IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2003-2015, 2014.
• M. T. Do, Z. Man, C. Zhang, H. Wang, and F. Tay, “Robust sliding mode learning control for Steer-by-Wire systems,” IEEE Transactions on Vehicular Technology, vol. 63, no. 2, pp. 580-590, 2014.
• M. T. Do, Z. Man, C. Zhang, J. Jin, and H. Wang, “Robust sliding mode learning control for uncertain discrete-time MIMO systems,” IET Control Theory and Applications, vol. 8, no. 12, pp. 1045-1053, 2014.