Jing Zhou

• Schlanbusch, Siri Marte & Zhou, Jing (2024). Adaptive predictor-based control for a helicopter system with input delays: Design and experiments. Journal of Automation and Intelligence (JAI). ISSN 2949-8554. 3(1), p. 50–56. doi: 10.1016/j.jai.2024.02.001.
• Li, Zifei; Han, Zhen; Wang, Wei & Zhou, Jing (2023). Observer-Based Adaptive Attack Reconstruction for a Class of Uncertain Systems. In IEEE, . (Eds.), IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 979-8-3503-3182-0. doi: 10.1109/IECON51785.2023.10312718.
• Kumar, Ravi; Dale, Jørgen; Singh, Jayant & Zhou, Jing (2023). Design and Development of an Anthropomorphic Gripper for Service Robotics and Prosthetic Applications. International Conference on Control, Mechatronics and Automation. ISSN 2837-5114. p. 233–238. doi: 10.1109/ICCMA59762.2023.10374949.
• Sveen, Emil Mühlbradt & Zhou, Jing (2023). Applications of Linear Adaptive Dynamic Programming (ADP) to a Non-linear Four-bar Mechanism. International Conference on Control, Mechatronics and Automation. ISSN 2837-5114. p. 177–182. doi: 10.1109/ICCMA59762.2023.10374822.
• RIPOLL, ÁLVARO ÚBEDA; Jiang, Zhiyu; Verma, Amrit Shankar & Zhou, Jing (2023). Active damage mitigation of the blade leading edge erosion for a wind turbine during rainfall events. IOP Conference Series: Materials Science and Engineering. ISSN 1757-8981. 1294(012001). doi: 10.1088/1757-899X/1294/1/012001. Full text in Research Archive Show summary

  Leading edge erosion (LEE) of wind turbine blades is a major concern for the wind turbine industry. LEE leads to increased blade surface roughness and incurs significant repair and maintenance costs. To address this issue, the present paper proposes an active LEE mitigation method by operating wind turbines in an erosion-safe mode with reduced rotor speed during precipitation. The proposed method involves first numerically modelling the NREL 5-MW reference wind turbine in openFAST. Then, during a regular operation in the presence of rainfall, the erosion-safe mode is designed, implemented and initiated with a modified blade pitch controller in Matlab-Simulink. To evaluate the effectiveness of this mode, the long-term rainfall distribution of the De Kooy site in the Netherlands is examined. The analysis considers a comparison of power output, erosion lifetime, and levelized cost of energy (LCOE) with and without the proposed safe modes. The study shows that the erosion-safe mode can significantly extend the erosion lifetime at the cost of reduced power generation. The controller settings affect the wind turbine performance, and a reduced rotor speed of 11 revolutions per minute is recommended considering the LCOE (308.64€/MWh). While the proposed mitigation method shows promise, it requires further refinement for real-life implementation in wind turbines.

• Nama, Ajay Nagendra; Saad, Leila Ben; Beferull-Lozano, Baltasar & Zhou, Jing (2023). Neighborhood Graph Filters Based Graph Convolutional Neural Networks for Multi-Agent Deep Reinforcement Learning. In IEEE, . (Eds.), IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 979-8-3503-3182-0. doi: 10.1109/IECON51785.2023.10311938. Show summary

  Multi-agent deep reinforcement learning (MADRL), where a group of agents inside multi-agent systems cooperate to achieve a common goal, has been shown useful in many applications such as collaborative robots, autonomous driving or video games involving teams. In this paper, we propose two multi-agent deep reinforcement learning (MADRL) frameworks for value function factorization built by using Graph Convolutional Neural Networks (GCNN) based on neighborhood graph filters (NGFs). These MADRL frameworks are based on the paradigm of centralized training with decentralized execution (CTDE). In this work, we show that the superior stability of the NGFs as compared to standard graph filters leads also to superior performance for the MADRL algorithms. In the first MADRL framework, the NGF-based GCNN is used to predict the local q-value at each of the agents, while in the other one, the NGF-based GCNN is used to mix the local q-values to generate a global Q-value. We have compared the performance of NGF-based GCNNs over state-of-the-art graph neural networks for value function factorization in the MADRL framework for the StarCraft II and Coalition Structure Generation problems. The results show that the proposed MARL frameworks outperform the existing state-of-art architectures.

• Landsverk, Ronny; Zhou, Jing & Hagen, Daniel (2023). Antiswing Control and Trajectory Planning for Offshore Cranes. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/IECON51785.2023.10312101. Show summary

  Safe handling of heavy payloads in an offshore environment requires careful crane maneuvering to avoid collision with obstacles and other equipment. The residual swing from a hanging payload can ultimately lead to danger and high cost failures if not properly dealt with. This paper investigates an openloop control method for eliminating the payload swing for hanging loads on offshore knuckle-boom cranes. A trajectory tracking method is designed for payload swing suppression for open-loop control and based on the iterative learning algorithm. It is shown that the proposed anti-swing control method guarantees asymptotic convergence of the swing, the angular velocity, and the angular acceleration of the payload using Lyapunov techniques. The simulation results show the superior performance of the proposed anti-swing control method.

• Sveen, Emil Mühlbradt & Zhou, Jing (2023). Adaptive Learning Based Motor Control of an Unknown Robot Manipulator. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. Show summary

  Adaptive dynamic programming, optimal control, robot manipulator, unknown dynamics, noise Abstract—This paper focuses on motor joint control using learning based Adaptive Dynamic Programming (ADP). The dynamics of a 2-link robot manipulator is presented. The system is regarded as a linear system and the optimal control is developed using linear quadratic regulator (LQR) and ADP methods. An off-line, off-policy controller iteration approach is used to find two optimal controllers, one for each motor joint of the robot. The optimal controller is developed to control the joint position without knowing the dynamic model. Simulation results show that two independent optimal linear control policies are learned simultaneously under a common exploration strategy, and the efficiency of ADP is verified.

• Schlanbusch, Siri Marte & Zhou, Jing (2023). Adaptive Control of an Uncertain 2-DOF Helicopter System with Input Delays. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. Show summary

  In this paper, we consider a 2-degrees-of-freedom (DOF) helicopter system subject to input delays and uncertain system parameters. To address this challenge in control design, we develop an adaptive predictor-feedback control law. The control law is designed to compensate for a known delay while considering the system uncertainty. Stability of the closed-loop system is established, where tracking is achieved. We demonstrate the effectiveness of our proposed control approach through simulations of the helicopter system, where the input delays are compensated in the control-loop.

• Singh, Jayant; Zhou, Jing & Beferull-Lozano, Baltasar (2023). Enhancing Multi-Agent Reinforcement Learning: Set Function Approximation and Dynamic Policy. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. Show summary

  While Deep Learning based methods can solve complex problems by employing Neural Networks to act as powerful function approximators, they often suffer from inflexibility in terms of deployment beyond the training scenario and also include irrelevant data priors in the form of an ordered array of input values. This problem is quite evident in the field of MultiAgent Reinforcement Learning (MARL), where most research covers methods that are trained on a fixed number of agents, restricted by the fixed size of the input vector. In this paper, we argue that this is not a reasonable assumption, both in terms of the inflexible amount of environmental information and the restrictive nature of the structure of the information. We explore DeepSets and Set Transformers as two powerful set function approximators to address the problem of cardinality invariance and permutation invariance in the observation space of a reinforcement learning agent. We explain Set-Input Reinforcement Learning (SIRL) in detail and evaluate the performance of the DeepSets and Set Transformer methods through simulated experiments on a challenging multi-agent environment, that otherwise yields sub-optimal policies through traditional function approximation approaches. We demonstrate that both DeepSets and Set Transformer based encoders scale well to increasing the number of agents from training to evaluation.

• Schlanbusch, Siri Marte & Zhou, Jing (2023). Adaptive quantized control of uncertain nonlinear rigid body systems. Systems & control letters (Print). ISSN 0167-6911. 175. doi: 10.1016/j.sysconle.2023.105513. Full text in Research Archive Show summary

  This paper investigates the attitude tracking control problem for uncertain nonlinear rigid body systems, where both inputs and states are quantized. It is common in networked control systems that sensor and control signals are quantized before they are transmitted via a communication network. An adaptive backstepping control algorithm is developed for a class of uncertain multiple-input multiple-output (MIMO) systems where a class of sector bounded quantizers is considered. It is shown that all the closed-loop signals are ensured uniformly bounded and tracking is achieved. Further, the tracking errors are shown to converge towards a compact set containing the origin and the set can be made small by the choice of the quantization parameters and the control parameters. For illustration of the proposed control scheme, experiments were conducted on a 2 degrees-of-freedom (DOF) helicopter system.

• Yakkati, Rakesh Reddy; Pardhasaradhi, Bethi; Zhou, Jing & Cenkeramaddi, Linga Reddy (2022). A Machine Learning Based GNSS Signal Classification, 2022 IEEE International Symposium on Smart Electronic Systems. IEEE conference proceedings. ISSN 979-8-3503-9922-6. p. 532–535. doi: 10.1109/iSES54909.2022.00116. Show summary

  The global navigation satellite system (GNSS) provides accurate position, velocity, and time data all over the world. However, GNSS are susceptible to multipath effects in suburban, urban, and indoor environments. Furthermore, it is vulnerable to intentional interference such as jamming and spoofing, which can result in either no or false position estimates. This study focuses on classifying received GNSS signals in a multi-correlation GNSS receiver as interference-free, multipath, jamming, or spoofing. To classify the GNSS signal, the average power and distortion correlation features are extracted from the multi-correlation output. Various machine learning algorithms such as neural networks, support vector machines, nearest neighbors, kernel approxi-mation, decision trees, discriminant analysis, naive Bayes, and ensemble classifiers are investigated and quantified using test accuracy and confusion matrix.

• Singh, Jayant; Zhou, Jing; Beferull-Lozano, Baltasar & Tyapin, Ilya (2022). Learning Cooperative Multi-Agent Policies with Multi-Channel Reward Curriculum Based Q-Learning. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/IECON49645.2022.9969046.
• Schlanbusch, Siri Marte; Aamo, Ole Morten & Zhou, Jing (2022). Attitude Control of a 2-DOF Helicopter System with Input Quantization and Delay. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/IECON49645.2022.9968994. Full text in Research Archive Show summary

  In this paper the attitude tracking control problem of a 2 degrees-of-freedom helicopter system with network induced constraints is studied. A predictor feedback control law is developed to compensate a known delay in the communication, where the inputs are quantized before transmitted over the network. Stability of the closed-loop system is established, where tracking is achieved with bounded tracking errors due to the network issues. The developed predictor-based controller is experimentally tested on the helicopter system, where we demonstrate that tracking is achieved in presence of both input delay and quantization.

• Zhou, Jing & Schlanbusch, Siri Marte (2022). Adaptive Quantized Control of Offshore Underactuated Cranes with Uncertainty. IEEE International Conference on Control and Automation. ISSN 1948-3449. p. 297–302. doi: 10.1109/ICCA54724.2022.9831937. Full text in Research Archive Show summary

  The anti-swing control of offshore cranes presents much more challenges. Most existing controllers for offshore cranes are designed based on linearized dynamics and require the accurate values of the plant parameters. In this paper, an adaptive sliding mode control scheme is investigated for a nonlinear underactuated crane system with unmodeled dynamics. The proposed control method can ensure asymptotic stability and does not need linearization of the complicated nonlinear dynamic equations during controller design and stability analysis. To reduce the communication burden in a network, a uniform quantizer is introduced in the input communication channel. A quantized adaptive sliding mode control scheme is further developed for the underactuated cranes to compensate for the effects of input quantization and uncertain parameters. The proposed controller together with the quantizer ensures the asymptotic stability of the closed-loop system in the sense of signal boundedness and zero stabilization error. Numerical simulations are conducted to illustrate the effectiveness of proposed schemes.

• Borge, Jørgen; Wad, Martin Sauar; Økter, Martin Bjaadal; Ottestad, Morten; Rudolfsen, Morten H. & Zhou, Jing (2022). Design and Implementation of Mechatronics Home Lab for Undergraduate Mechatronics Teaching , 2022 IEEE 17th International Conference on Control & Automation (ICCA). IEEE conference proceedings. ISSN 978-1-6654-9572-1. p. 291–296. doi: 10.1109/ICCA54724.2022.9831876. Full text in Research Archive Show summary

  The field of mechatronics is a multidisciplinary field of engineering, where the combination of physical components and theory from several engineering fields is applied to build complex machines. Mechatronics education is an active learning process through practical laboratory exercises and problem-based learning. This paper presents the design and implementation of a mechatronics home lab to support undergraduate mechatronics teaching. The purpose is to support theoretical teaching in mechatronics with a low-cost, 3D-printable platform where the students can experiment and practice instrumentation and control theory with a practical problem-based approach. Five projects were introduced for experiment implementation of the developed home lab. Throughout these experiments, it is intended to facilitate the understanding of theories and concepts in mechatronics, and enhance the ability to design and implementation of experiments, the collection and analysis of data, and the conducting of simulation in MATLAB.

• Wang, Wei; Long, Jiang; Zhou, Jing; Huang, Jiangshuai & Wen, Changyun (2021). Adaptive backstepping based consensus tracking of uncertain nonlinear systems with event-triggered communication. Automatica. ISSN 0005-1098. 133. doi: 10.1016/j.automatica.2021.109841. Full text in Research Archive
• Schlanbusch, Siri Marte & Zhou, Jing (2021). Adaptive Backstepping Control of a 2-DOF Helicopter System in the Presence of Quantization. In Plapper, Peter (Eds.), 2021 IEEE The 9th International Conference on Control, Mechatronics and Automation. IEEE conference proceedings. ISSN 9781728137865. Full text in Research Archive
• Yu, Mengze; Wang, Wei; Zhou, Jing & Tong, Yongxin (2021). Adaptive Backstepping based Secure Control for P-normal Form of Second-Order Nonlinear Systems Against Deception Attacks. IEEE Conference on Decision and Control. Proceedings. ISSN 0743-1546. p. 1802–1807. doi: 10.1109/CDC45484.2021.9683733.
• Wang, Wei; Zhou, Jing; Wen, Changyun & Long, Jiang (2021). Adaptive Backstepping Control of Uncertain Nonlinear Systems with Input and State Quantization. IEEE Transactions on Automatic Control. ISSN 0018-9286. 67(12), p. 6754–6761. doi: 10.1109/TAC.2021.3131958. Full text in Research Archive
• Schlanbusch, Siri Marte; Zhou, Jing & Schlanbusch, Rune (2021). Adaptive Backstepping Attitude Control of a Rigid Body with State Quantization, Proceedings of 60th IEEE Conference on Decision and Control. IEEE conference proceedings. ISSN 978-1-7281-1398-2. p. 372–377. doi: 10.1109/CDC45484.2021.9683579. Full text in Research Archive Show summary

  In this paper, the attitude tracking control problem of a rigid body is investigated where the states are quantized. An adaptive backstepping based control scheme is developed and a new approach to stability analysis is developed by constructing a new compensation scheme for the effects of the vector state quantization. It is shown that all closed-loop signals are ensured uniformly bounded and the tracking errors converge to a compact set containing the origin. Experiments on a 2 degrees-of-freedom helicopter system illustrate the proposed control scheme.

• Schlanbusch, Siri Marte; Zhou, Jing & Schlanbusch, Rune (2021). Adaptive Attitude Control of a Rigid Body with Input and Output Quantization. IEEE transactions on industrial electronics (1982. Print). ISSN 0278-0046. 69(8), p. 8296–8305. doi: 10.1109/TIE.2021.3105999. Full text in Research Archive
• Huang, Jiangshuai; Wang, Wei & Zhou, Jing (2021). Adaptive Control Design for Underactuated Cranes with Guaranteed Transient Performance: Theoretical Design and Experimental Verification. IEEE transactions on industrial electronics (1982. Print). ISSN 0278-0046. 69(3), p. 2822–2832. doi: 10.1109/TIE.2021.3065835. Full text in Research Archive
• Xiangyang, Xu; Xiang, Li; Hui, Zhang & Zhou, Jing (2020). Event-Triggered Robust Control of an Integrated Motor-Gearbox Powertrain System for a Connected Vehicle under CAN and DOS-Induced Delays. SAE technical paper series. ISSN 0148-7191. 2020(February). doi: 10.4271/2020-01-5016.
• Schlanbusch, Siri Marte & Zhou, Jing (2020). Adaptive Backstepping Control of a 2-DOF Helicopter System with Uniform Quantized Inputs. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/IECON43393.2020.9254497. Full text in Research Archive
• Zhuang, Wenxiu; Zhou, Jing; Liu, Zhitao & Su, Hongye (2020). Event-Triggered Adaptive Control for a Class of Nonlinear Systems with Unknown Time-Varying Parameters. IEEE Conference on Industrial Electronics and Applications. ISSN 2158-2297. p. 1324–1329. doi: 10.1109/ICIEA48937.2020.9248250.
• Landsverk, Ronny; Zhou, Jing; Hovland, Geir & Zhang, Houxiang (2020). Modeling of Offshore Crane and Marine Craft in Wave Motion. IEEE Conference on Industrial Electronics and Applications. ISSN 2158-2297. p. 1463–1470. doi: 10.1109/ICIEA48937.2020.9248110. Full text in Research Archive
• Huang, Jiangshuai; Wang, Wei; Wen, Changyun; Zhou, Jing & Li, Guoqi (2020). Distributed adaptive leader–follower and leaderless consensus control of a class of strict-feedback nonlinear systems: a unified approach. Automatica. ISSN 0005-1098. 118. doi: 10.1016/j.automatica.2020.109021. Full text in Research Archive
• Long, Jiang; Wang, Wei; Huang, Jiangshuai; Zhou, Jing & Liu, Kexin (2019). Distributed Adaptive Control for Asymptotically Consensus Tracking of Uncertain Nonlinear Systems With Intermittent Actuator Faults and Directed Communication Topology. IEEE Transactions on Cybernetics. ISSN 2168-2267. 51(8), p. 4050–4061. doi: 10.1109/TCYB.2019.2940284. Full text in Research Archive
• Zhou, Jing (2019). Adaptive Tracking Control of Nonlinear Time-Varying Systems with Unknown Control Coefficients and Unknown Time-Varying Parameters. In et al., N.N. (Eds.), Proceedings of 58th IEEE Conference on Decision and Control (CDC 2019). IEEE Press. ISSN 978-1-7281-1397-5. p. 1025–1030. doi: 10.1109/CDC40024.2019.9029616. Full text in Research Archive
• Schlanbusch, Siri Marte & Zhou, Jing (2019). Adaptive Backstepping Control of a 2-DOF Helicopter. In Herder, J. & HosseinNia, Hassan (Ed.), Proceedings, 2019 IEEE 7th International Conference on Control, Mechatronics and Automation. IEEE Press. ISSN 9781728137865. p. 210–215. doi: 10.1109/ICCMA46720.2019.8988761. Full text in Research Archive
• Fjetland, Andreas Kvalbein; Zhou, Jing; Abeyrathna, Kuruge Darshana & Gravdal, Jan Einar (2019). Kick Detection and Influx Size Estimation during Offshore Drilling Operations using Deep Learning. IEEE Conference on Industrial Electronics and Applications. ISSN 2158-2297. doi: 10.1109/ICIEA.2019.8833850. Full text in Research Archive Show summary

  An uncontrolled or unobserved influx or kick during drilling has the potential to induce a well blowout, one of the most harmful incidences during drilling both in regards to economic and environmental cost. Since kicks during drilling are serious risks, it is important to improve kick and loss detection performance and capabilities and to develop automatic flux detection methodology. There are clear patterns during a influx incident. However, due to complex processes and sparse instrumentation it is difficult to predict the behaviour of kicks or losses based on sensor data combined with physical models alone. Emerging technologies within Deep Learning are however quite adapt at picking up on, and quantifying, subtle patterns in time series given enough data. In this paper, a new model is developed using Long Short-Term Memory (LSTM), a Recurrent Deep Neural Network, for kick detection and influx size estimation during drilling operations. The proposed detection methodology is based on simulated drilling dataand involves detecting and quantifying the influx of fluids between fractured formations and the well bore. The results show that the proposed methods are effective both to detect and estimate the influx size during drilling operations, so that corrective actions can be taken before any major problem occurs.

• Wang, Wei; Wen, Changyun; Huang, Jiangshuai & Zhou, Jing (2019). Adaptive consensus of uncertain nonlinear systems with event triggered communication and intermittent actuator faults. Automatica. ISSN 0005-1098. 111, p. 1–11. doi: 10.1016/j.automatica.2019.108667. Full text in Research Archive
• Zhuang, Wenxiu; Wen, Changyun; Zhou, Jing; Liu, Zhitao & Su, Hongye (2019). Event-triggered robust adaptive control for discrete time uncertain systems with unmodelled dynamics and disturbances. IET Control Theory & Applications. ISSN 1751-8644. 13(18), p. 3124–3131. doi: 10.1049/iet-cta.2019.0308. Full text in Research Archive
• Zhou, Jing; Wen, Chang-Yun; Wang, Wei & Yang, Fan (2019). Adaptive Backstepping Control of Nonlinear Uncertain Systems With Quantized States. IEEE Transactions on Automatic Control. ISSN 0018-9286. 64(11), p. 4756–4763. doi: 10.1109/TAC.2019.2906931. Full text in Research Archive
• Wen, Chang-Yun; Zhou, Jing; Wang, Wei; Huang, Jiang-Shuai & Fan, Hui-Jin (2018). A unified conventional approach for robust adaptive control systems and some reviews on recent developments in adaptive control. Kongzhi yu Juece/Control and Decision. ISSN 1001-0920. 33(5), p. 782–808. doi: 10.13195/j.kzyjc.2017.1459.
• Kenneth, Hatletvedt John; Kristian, Gjerstad & Zhou, Jing (2018). Modelling of a hydro-pneumatic system for heave compensation, 2018 13th IEEE Conference on Industrial Electronics and Applications. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-3758-6. p. 707–712. doi: 10.1109/ICIEA.2018.8397805. Full text in Research Archive
• Landsverk, Ronny & Zhou, Jing (2018). Modeling and simulation of an offshore crane, 2018 13th IEEE Conference on Industrial Electronics and Applications. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-3758-6. p. 713–718. doi: 10.1109/ICIEA.2018.8397806. Full text in Research Archive
• Long, Jiang; Wang, Wei; Zhou, Jing & Wu, Hao (2018). Distributed Adaptive Consensus Tracking Control of Uncertain High-order Nonlinear Systems under Directed Graph Condition, Proceeding of the 57th IEEE Conference on Decision and Control 2018. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-1395-5. p. 2211–2216. doi: 10.1109/CDC.2018.8619053. Full text in Research Archive
• Long, Jiang; Wang, Wei & Zhou, Jing (2018). Adaptive Asymptotically Tracking Control for Uncertain Strict-Feedback Nonlinear Systems with Input Quantization. In IEEE, . (Eds.), Proceeding of the 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Marina Bay Sands Expo and Convention Centre, Singapore, November 18-21, 2018. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-9581-4. p. 361–366. doi: 10.1109/ICARCV.2018.8581115. Full text in Research Archive
• Zhou, Jing (2018). Adaptive Control of Uncertain Nonlinear Systems with Time-Varying Parameters and Disturbances. International Conference on Advanced Mechatronic Systems, ICAMechS. ISSN 2325-0682. p. 36–41. doi: 10.1109/ICAMechS.2017.8316546.
• Zhou, Jing (2018). Adaptive PI Control of Bottom Hole Pressure during Oil Well Drilling. IFAC-PapersOnLine. ISSN 2405-8963. 51(4), p. 166–171. doi: 10.1016/j.ifacol.2018.06.060. Full text in Research Archive
• Huang, Jiangshuai; Wang, Wei; Wen, Changyun & Zhou, Jing (2018). Adaptive control of a class of strict-feedback time-varying nonlinear systems with unknown control coefficients. Automatica. ISSN 0005-1098. 93, p. 98–105. doi: 10.1016/j.automatica.2018.03.061.
• Zhou, Jing; Wen, Changyun & Wang, Wei (2018). Adaptive control of uncertain nonlinear systems with quantized input signal. Automatica. ISSN 0005-1098. 95, p. 152–162. doi: 10.1016/j.automatica.2018.05.014. Full text in Research Archive
• Liu, Lijuan; Zhou, Jing; Wen, Changyun & Zhao, Xudong (2017). Robust adaptive tracking control of uncertain systems with time-varying input delays. International Journal of Systems Science. ISSN 0020-7721. 48(16), p. 3440–3449. doi: 10.1080/00207721.2017.1382604.
• Zhou, Jing (2017). Decentralized Adaptive Control for Interconnected Nonlinear Systems with Input Quantization. IFAC-PapersOnLine. ISSN 2405-8963. 50(1), p. 10419–10424. doi: 10.1016/j.ifacol.2017.08.1969.
• Zhou, Jing & Wang, Wei (2017). Adaptive Control of Quantized Uncertain Nonlinear Systems. IFAC-PapersOnLine. ISSN 2405-8963. 50(1), p. 10425–10430. doi: 10.1016/j.ifacol.2017.08.1970.
• Liu, Lijuan; Sun, Ximing; Wang, Wei; Zhou, Jing & Wen, Changyun (2016). Adaptive backstepping control of uncertain systems in the presence of unmodeled dynamics and time-varying delays. In Wang, Jun (Eds.), 2016 Sixth International Conference on Information Science and Technology (ICIST). IEEE. ISSN 978-1-5090-1224-4. p. 234–240. doi: 10.1109/ICIST.2016.7483416.
• Zhou, Jing & Wang, Wei (2016). Adaptive Control of a Drilling System with Unknown Time-delay and Disturbance , Proceeding of the 14th IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV), Phuket, Thailand, 13-15 November 2016. IEEE conference proceedings. ISSN 978-1-5090-3549-6. doi: 10.1109/ICARCV.2016.7838697.
• Zhou, Jing; Gravdal, Jan Einar; Strand, Per & Hovland, Svein (2016). Automated kick control procedure for an influx in managed pressure drilling operations by utilizing PWD. MIC Journal: Modeling, Identification and Control. ISSN 0332-7353. 37(1), p. 31–40. doi: 10.4173/mic.2016.1.3.
• Zhou, Jing & Krstic, Miroslav (2016). Adaptive predictor control for stabilizing pressure in a managed pressure drilling system under time-delay. Journal of Process Control. ISSN 0959-1524. 40, p. 106–118. doi: 10.1016/j.jprocont.2016.01.004.
• Zhou, Jing (2014). Adaptive Control of Uncertain Nonlinear Systems with Time-Varying Parameters and Disturbances , 2014 International Conference on Advanced Mechatronic Systems (ICAMechS) 2014, Kumamoto, 10-12 August, 2014. IEEE conference proceedings. ISSN 978-1-4799-6382-9. doi: 10.1109/icamechs.2017.8316546.
• Zhou, Jing; Wen, Changyun & Yang, Guanghong (2014). Adaptive backstepping stabilization of nonlinear uncertain systems with quantized input signal. IEEE Transactions on Automatic Control. ISSN 0018-9286. 59(2), p. 460–464. doi: 10.1109/TAC.2013.2270870.
• Zhou, Jing (2013). Adaptive Observer Design and Parameter Estimation for a Class of Uncertain Systems. IEEE Conference on Industrial Electronics and Applications. ISSN 2158-2297. p. 148–153. doi: 10.1109/ICIEA.2013.6566356.
• Zhou, Jing & Wen, Changyun (2013). Adaptive Backstepping Control of Uncertain Nonlinear Systems with Input Quantization. Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes. ISSN 0191-2216. p. 5571–5576. doi: 10.1109/CDC.2013.6760767.
• Zhou, Jing (2012). Decentralized adaptive backstepping stabilization of interconnected systems with input time delays in dynamic interactions. International Journal of Adaptive Control and Signal Processing. ISSN 0890-6327. 26(4), p. 285–301. doi: 10.1002/acs.1281.
• Zhou, Jing; Wen, Changyun & Li, Tieshan (2012). Adaptive Output Feedback Control of Uncertain Nonlinear Systems with Hysteresis Nonlinearity. IEEE Transactions on Automatic Control. ISSN 0018-9286. 57(10), p. 2627–2633. doi: 10.1109/TAC.2012.2190208.
• Zhou, Jing & Wen, Changyun (2012). Adaptive Feedback Control of Magnetic Suspension System Preceded by Bouc-Wen Hysteresis. In Xie, L (Eds.), International Conference on Control, Automation, Robotics and Vision. IEEE conference proceedings. ISSN 978-1-4673-1871-6. p. 1244–1249. doi: 10.1109/ICARCV.2012.6485323.
• Zhou, Jing; Nygaard, G. & Vefring, Erlend (2012). Adaptive Feedback Control of Magnetic Suspension System Preceded by Bouc-Wen Hysteresis. IEEE Conference on Industrial Electronics and Applications. ISSN 2158-2297. p. 576–581. doi: 10.1109/ICIEA.2012.6360793.
• Zhou, Jing (2012). Adaptive Decentralized Control of Parallel DC-DC Converters Systems. International Journal of Systems, Control, and Communications. ISSN 1755-9340. 4(3), p. 134–149. doi: 10.1504/IJSCC.2012.048612.
• Wen, Changyun; Zhou, Jing; Liu, Zhitao & Su, Hongye (2011). Robust Adaptive Control of Uncertain Nonlinear Systems in the Presence of Input Saturation and External Disturbance. IEEE Transactions on Automatic Control. ISSN 0018-9286. 56(7), p. 1672–1678. doi: 10.1109/TAC.2011.2122730.
• Zhou, Jing (2011). Automatic model-based control scheme for stabilizing pressure during dual-gradient drilling. Journal of Process Control. ISSN 0959-1524. 21(8), p. 1138–1147. doi: 10.1016/j.jprocont.2011.06.022.
• Zhou, Jing; Stamnes, Øyvind Nistad; Aamo, Ole Morten & Kaasa, Glenn-Ole (2011). Switched Control for Pressure Regulation and Kick Attenuation in a Managed Pressure Drilling System. IEEE Transactions on Control Systems Technology. ISSN 1063-6536. 19(2), p. 337–350. doi: 10.1109/TCST.2010.2046517.
• Zhou, Jing & Nygaard, Gerhard (2011). Nonlinear Adaptive Observer for Managed Pressure Drilling System. IEEE Conference on Industrial Electronics and Applications. ISSN 2158-2297. p. 79–84. doi: 10.1109/ICIEA.2011.5975554.
• Zhou, Jing; Nygaard, Gerhard; Godhavn, John-Morten; Breyholtz, Øyvind & Vefring, Erlend (2010). Adaptive Observer for Kick Detection and Switched Control for Bottomhole Pressure Regulation and Kick Attenuation during Managed Pressure Drilling. American Control Conference (ACC). ISSN 0743-1619. p. 3765–3770.
• Carlsen, Liv Almås; Gravdal, Jan Einar; Zhou, Jing; Nygaard, Gerhard & Time, R. W. (2010). Automatic Coordination of Equipment while Circulating out a Kick and Displacing the Kill-Weight Mud, IADC Well Control Europe, Aberdeen 2010. http://www.iadc.org/conferences/Euro_WellControl/. IADC Well Control Europe.
• Wen, CY; Zhou, Jing & Wang, W (2009). Decentralized adaptive backstepping stabilization of interconnected systems with dynamic input and output interactions. Automatica. ISSN 0005-1098. 45(1), p. 55–67. doi: 10.1016/j.automatica.2008.06.018.
• Zhou, Jing; Stamnes, Øyvind Nistad; Aamo, Ole Morten & Kaasa, Glenn-Ole (2009). Pressure regulation with kick attenuation in a managed pressure drilling system. Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes. ISSN 0191-2216. p. 5586–5591.
• Stamnes, Øyvind Nistad; Zhou, Jing; Aamo, Ole Morten & Kaasa, Glenn-Ole (2009). Adaptive observer design for nonlinear systems with parametric uncertainties in unmeasured state dynamics. Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes. ISSN 0191-2216. p. 4414–4419.
• Zhou, Jing; Wen, CY & Wang, W (2009). Adaptive backstepping control of uncertain systems with unknown input time-delay. Automatica. ISSN 0005-1098. 45(6), p. 1415–1422. doi: 10.1016/j.automatica.2009.01.012.
• Wen, C; Wang, W & Zhou, Jing (2008). Decentralized adaptive backstepping stabilization of nonlinear interconnected systems. In NN, NN (Eds.), Proceedings of the 2008 Chinese Control and Decision Conference. IEEE conference proceedings. ISSN 978-1-4244-1734-6. p. 4111–4116.
• Zhou, Jing; Kaasa, Glenn-Ole & Aamo, Ole Morten (2008). Nonlinear observer-based control of unstable wells. In N, N (Eds.), Proceedings of the 3rd International Symposium on Communication, Control and Signal Processing. IEEE conference proceedings. ISSN 978-1-4244-1688-2. p. 683–688.
• Zhou, Jing (2008). Decentralized adaptive control for large-scale time-delay systems with dead-zone input. Automatica. ISSN 0005-1098. 44, p. 1790–1799. doi: 10.1016/j.automatica.2007.10.037.
• Stamnes, Øyvind Nistad; Zhou, Jing; Kaasa, Glenn-Ole & Aamo, Ole Morten (2008). Adaptive observer design for the bottomhole pressure of a managed pressure drilling system. Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes. ISSN 0191-2216. p. 2961–2966.

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• Zhou, Jing (2023). 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE conference proceedings. ISBN 979-8-3503-1568-4. 497 p.
• Zhou, Jing (2023). 2023 The 11th International Conference on Control, Mechatronics and Automation (ICCMA 2023). IEEE conference proceedings. ISBN 978-1-6654-9048-1.
• Zhou, Jing; Xing, Lantao & Wen, Changyun (2021). Adaptive Control of Dynamic Systems with Uncertainty and Quantization. CRC Press. ISBN 9781032009810. 249 p.
• Zhou, Jing (2020). 15th IEEE Conference on Industrial Electronics and Applications. Institute of Electrical and Electronics Engineers (IEEE). ISBN 978-1-7281-5168-7. 200 p.
• Wang, Wei; Wen, Changyun & Zhou, Jing (2017). Adaptive Backstepping Control of Uncertain Systems with Actuator Failures, Subsystem Interactions, and Nonsmooth Nonlinearities. CRC Press. ISBN 9781498776431. 274 p.
• Zhou, Jing & Nygaard, Olav Gerhard Haukenes (2011). Decentralized Adaptive Sabilization for Large-Scale Systems with Unknown Time-Delay. IntechOpen. ISBN 978-953-307-559-4. 16 p.
• Zhou, Jing & Wen, Changyun (2011). Backstepping Control, in Industrial Electronics Handbook: Control and Mechatronics. Tayler & Francis. ISBN 9781439802892. 22 p.

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• Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi; Zhou, Jing & Cenkeramaddi, Linga Reddy (2022). A Machine Learning Based GNSS Signal Classification.
• Zhou, Jing (2021). Next-generation Artificial Intelligence for Industrial robots. [Internet]. https://www.uia.no/en/research/priority-research.
• Zhou, Jing (2021). Adaptive control of dynamic systems with quantization.
• Zhou, Jing (2020). Adaptive Control of Systems with Quantization.
• Schlanbusch, Siri Marte & Zhou, Jing (2020). Adaptive Backstepping Control in the Presence of Quantization: Application to a 2-DOF Helicopter System.
• Hatletvedt, John Kenneth; Gjerstad, Kristian & Zhou, Jing (2018). Modelling of a Hydro-Pneumatic System for Heave Compensation.
• Robbersmyr, Kjell Gunnar; Choux, Martin Marie Hubert; Zhou, Jing & Knausgård, Kristian Muri (2018). Mekatronikk.
• Zhou, Jing; Gravdal, Jan Einar; Strand, Per & Hovland, Svein (2014). Automatic Well Control Procedure for Kick Handling in Managed Pressure Drilling Operations using PWD data.
• Zhou, Jing; Stamnes, Øyvind Nistad; Aamo, Ole Morten & Kaasa, Glenn-Ole (2009). Pressure regulation with kick attenuation in a managed pressure drilling system.
• Stamnes, Øyvind Nistad; Zhou, Jing; Aamo, Ole Morten & Kaasa, Glenn-Ole (2009). Adaptive observer design for nonlinear systems with parametric uncertainties in unmeasured state dynamics.
• Zhou, Jing; RIPOLL, ÁLVARO ÚBEDA & Jiang, Zhiyu (2023). Active control of the blade leading edge erosion for an onshore wind turbine during rainfall events. Universitetet i Agder.
• Zhou, Jing; Kumar, Ravi; Dale, Jørgen & Singh, Jayant (2023). Design and Development of a low-cost Anthropomorphic Gripper for Service Robotics and Prosthetic Applications. Universitetet i Agder.
• Schlanbusch, Siri Marte & Zhou, Jing (2023). Adaptive Control of Systems with Quantization and Time Delays. University of Agder. ISSN 978-82-8427-128-6. Full text in Research Archive Show summary

  This thesis addresses problems relating to tracking control of nonlinear systems in the presence of quantization and time delays. Motivated by the importance in areas such as networked control systems (NCSs) and digital systems, where the use of a communication network in NCS introduces several constraints to the control system, such as the occurrence of quantization and time delays. Quantization and time delays are of both practical and theoretical importance, and the study of systems where these issues arises is thus of great importance. If the system also has parameters that vary or are uncertain, this will make the control problem more complicated. Adaptive control is one tool to handle such system uncertainty. In this thesis, adaptive backstepping control schemes are proposed to handle uncertainties in the system, and to reduce the effects of quantization. Different control problems are considered where quantization is introduced in the control loop, either at the input, the state or both the input and the state. The quantization introduces difficulties in the controller design and stability analysis due to the limited information and nonlinear characteristics, such as discontinuous phenomena. In the thesis, it is analytically shown how the choice of quantization level affects the tracking performance, and how the stability of the closed-loop system equilibrium can be achieved by choosing proper design parameters. In addition, a predictor feedback control scheme is proposed to compensate for a time delay in the system, where the inputs are quantized at the same time. Experiments on a 2-degrees of freedom (DOF) helicopter system demonstrate the different developed control schemes.

• Zhou, Jing & Nilsen, Espen (2022). Robot localization. Universitetet i Agder.
• Zhou, Jing; Ims, Benjamin Årøy; Enehaug, Bjørn; Skår, Eirik Magnus & Tyapin, Ilya (2021). HUMAN-COBOT SEQUENTIAL COOPERATION. Universitetet i Agder.
• Zhou, Jing; Kumar, Ravi; Radicke, Jan-Philip; Eidhammer, Eirik & Tyapin, Ilya (2021). Multi-Robot Cell Operation using Robot Operating System. Universitetet i Agder.
• Zhou, Jing; Vange, Tom Erik & Tyapin, Ilya (2021). Model-free object grasping : Model-free object grasping with a learning-free approach. Universitetet i Agder. Show summary

  The industry standards and capability are constantly advancing and pushing forward to increase data collection, efficiency, profit, and quality as well as decrease downtime, injuries, and hazards as much as possible. In recent years, robot systems have received more attention in the context of a large number of industrial applications, such as automotive manufacturing, additive manufacturing, assembly, quality inspection, and co-packing. The collaboration between multiple robots and human operators is considered to be the most prominent strategy in Industry 4.0 and future Industry 5.0, sharing the same space and collaborating on tasks according to their complementary capabilities. With the use of robots and their abilities could efficiency, profit, safety, and quality be further increased, potentially revolutionizing the industry and production. This project was supported in part by DEEPCOBOT Project. DEEPCOBOT, Collective Efficient Deep Learning and Networked Control for Multiple Collaborative Robot Systems, are a research project funded by IKTPLUSS under Grant 306640/O70 from the Research Council of Norway. The project will investigate the design of a new generation of decentralized data-driven Deep Learning based controllers for multiple coexisting collaborative robots, which interact both between them-selves and with human operators in order to collectively learn from each other’s experiences and perform cooperatively different complex tasks in large-scale industrial environments. This is motivated by the increasing demand of automation in industry, especially the demand of a safer and more efficient collaboration between multiple robots and human operators to integrate the best of human abilities and robotic automation.

• Boginskis, Ilja; Hmdoun, Hmdoun Abker Ibrahim & Zhou, Jing (2020). Modeling, Simulation and Control for Marine Crane Operations. Universitetet i Agder.
• Zhou, Jing & Bjørnarå, Christopher (2020). Pipe joint position identification. Universitetet i Agder.

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