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To overcome nonlinear, underactuated and external wind disturbances problems for the 6-DOF (degrees of freedom) quadrotor unmanned aerial vehicle (UAV) system, a backstepping sliding mode control algorithm based on high-order extended state observer (ESO) is proposed. Based on the hierarchical control principle, the quadrotor UAV dynamic system is decomposed into position subsystem and attitude subsystem to facilitate the backstepping control design. Moreover, the EXO is used to estimate the remaining unmeasurable states and the external wind disturbances online. The advantages of the controllers are that they can not only ensure good tracking performance, but also deal with uncertain external disturbances. To imitate the real situation as much as possible, the external wind disturbances are composed of four basic wind models in this paper. The tracking error and estimate error of the design methods are shown to arbitrarily small by using Lyapunov theory. Finally, the effectiveness and superiority of the proposed control algorithm are proved by the simulation.
HongBin Wang; Ning Li; Yueling Wang; Bo Su. Backstepping Sliding Mode Trajectory Tracking via Extended State Observer for Quadrotors with Wind Disturbance. International Journal of Control, Automation and Systems 2021, 1 -12.
AMA StyleHongBin Wang, Ning Li, Yueling Wang, Bo Su. Backstepping Sliding Mode Trajectory Tracking via Extended State Observer for Quadrotors with Wind Disturbance. International Journal of Control, Automation and Systems. 2021; ():1-12.
Chicago/Turabian StyleHongBin Wang; Ning Li; Yueling Wang; Bo Su. 2021. "Backstepping Sliding Mode Trajectory Tracking via Extended State Observer for Quadrotors with Wind Disturbance." International Journal of Control, Automation and Systems , no. : 1-12.
This work focuses on the fixed-time event-triggered formation control problem for multi-AUV systems with external uncertainties, which can significantly reduce energy consumption and the frequency of the controller updates. At the same time, the convergence speed of the system is improved. To tackle with the problem of explosion of differentiation terms in backstepping method, a command filter is introduced to represent the derivative of virtual variables. Moreover, the distributed control strategy is considered and the lumped uncertainties are tackled with the super-twisting sliding mode method. It is proved that under the proposed event-triggered control strategies the Zeno behavior is avoided. Further, fixed-time formation control method can be finished within a fixed settling time with arbitrary initial states of the multi-AUV. Finally, simulation is presented to show the effectiveness and validity of the fixed-time event-triggered formation protocols for the multi-AUV systems.
Bo Su; HongBin Wang; Yueling Wang; Jing Gao. Fixed-time Formation of AUVs with Disturbance via Event-triggered Control. International Journal of Control, Automation and Systems 2021, 19, 1505 -1518.
AMA StyleBo Su, HongBin Wang, Yueling Wang, Jing Gao. Fixed-time Formation of AUVs with Disturbance via Event-triggered Control. International Journal of Control, Automation and Systems. 2021; 19 (4):1505-1518.
Chicago/Turabian StyleBo Su; HongBin Wang; Yueling Wang; Jing Gao. 2021. "Fixed-time Formation of AUVs with Disturbance via Event-triggered Control." International Journal of Control, Automation and Systems 19, no. 4: 1505-1518.
Sensor fault detection of wind turbines plays an important role in improving the reliability and stable operation of turbines. The supervisory control and data acquisition (SCADA) system of a wind turbine provides promising insights into sensor fault detection due to the accessibility of the data and the abundance of sensor information. However, SCADA data are essentially multivariate time series with inherent spatio-temporal correlation characteristics, which has not been well considered in the existing wind turbine fault detection research. This paper proposes a novel classification-based fault detection method for wind turbine sensors. To better capture the spatio-temporal characteristics hidden in SCADA data, a multiscale spatio-temporal convolutional deep belief network (MSTCDBN) was developed to perform feature learning and classification to fulfill the sensor fault detection. A major superiority of the proposed method is that it can not only learn the spatial correlation information between several different variables but also capture the temporal characteristics of each variable. Furthermore, this method with multiscale learning capability can excavate interactive characteristics between variables at different scales of filters. A generic wind turbine benchmark model was used to evaluate the proposed approach. The comparative results demonstrate that the proposed method can significantly enhance the fault detection performance.
Hong Wang; HongBin Wang; Guoqian Jiang; Yueling Wang; Shuang Ren. A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine. Sensors 2020, 20, 3580 .
AMA StyleHong Wang, HongBin Wang, Guoqian Jiang, Yueling Wang, Shuang Ren. A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine. Sensors. 2020; 20 (12):3580.
Chicago/Turabian StyleHong Wang; HongBin Wang; Guoqian Jiang; Yueling Wang; Shuang Ren. 2020. "A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine." Sensors 20, no. 12: 3580.
Health monitoring and early fault detection of wind turbines have attracted considerable attention due to the benefits of improving reliability and reducing the operation and maintenance costs of the turbine. However, dynamic and constantly changing operating conditions of wind turbines still pose great challenges to effective and reliable fault detection. Most existing health monitoring approaches mainly focus on one single operating condition, so these methods cannot assess the health status of turbines accurately, leading to unsatisfactory detection performance. To this end, this paper proposes a novel general health monitoring framework for wind turbines based on supervisory control and data acquisition (SCADA) data. A key feature of the proposed framework is that it first partitions the turbine operation into multiple sub-operation conditions by the clustering approach and then builds a normal turbine behavior model for each sub-operation condition. For normal behavior modeling, an optimized deep belief network is proposed. This optimized modeling method can capture the sophisticated nonlinear correlations among different monitoring variables, which is helpful to enhance the prediction performance. A case study of main bearing fault detection using real SCADA data is used to validate the proposed approach, which demonstrates its effectiveness and advantages.
Hong Wang; HongBin Wang; Guoqian Jiang; Jimeng Li; Yueling Wang. Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling. Energies 2019, 12, 984 .
AMA StyleHong Wang, HongBin Wang, Guoqian Jiang, Jimeng Li, Yueling Wang. Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling. Energies. 2019; 12 (6):984.
Chicago/Turabian StyleHong Wang; HongBin Wang; Guoqian Jiang; Jimeng Li; Yueling Wang. 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling." Energies 12, no. 6: 984.
This paper studies the defect detection problem of adhesive layer of thermal insulation materials. A novel detection method based on an improved particle swarm optimization (PSO) algorithm of Electrical Capacitance Tomography (ECT) is presented. Firstly, a least squares support vector machine is applied for data processing of measured capacitance values. Then, the improved PSO algorithm is proposed and applied for image reconstruction. Finally, some experiments are provided to verify the effectiveness of the proposed method in defect detection for adhesive layer of thermal insulation materials. The performance comparisons demonstrate that the proposed method has higher precision by comparing with traditional ECT algorithms.
Yintang Wen; Yao Jia; Yuyan Zhang; Xiaoyuan Luo; Hongrui Wang. Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT. Sensors 2017, 17, 2440 .
AMA StyleYintang Wen, Yao Jia, Yuyan Zhang, Xiaoyuan Luo, Hongrui Wang. Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT. Sensors. 2017; 17 (11):2440.
Chicago/Turabian StyleYintang Wen; Yao Jia; Yuyan Zhang; Xiaoyuan Luo; Hongrui Wang. 2017. "Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT." Sensors 17, no. 11: 2440.
Redundant actuated parallel robot is a multi-input and multi-output (MIMO) system which usually works in an uncertain environment. In this paper, the force/position hybrid control structure of 6PUS-UPU redundant actuation parallel robot is designed, and proportional–integral (PI) and model predictive control (MPC) cascade control strategies are used in the redundant branch of 6PUS-UPU redundant actuation parallel robot. The MPC algorithm is used in the current loop of the permanent magnet synchronous motor (PMSM) to restrain the motor parameter uncertainty and external disturbances influence on motor control. The MATLAB/ADAMS joint simulation method based on virtual 6PUS-UPU redundant actuation parallel robot prototype is used to test the performance of the proposed control strategy. The performance of proposed PI-MPC control strategy is compared with the traditional PI–PI control strategy. The simulation results show that the MPC controller can improve the tracking ability of the motor torque, guarantee the system robustness under the parameter variations and load disturbance environment.
Shuhuan Wen; Guiqian Qin; Baowei Zhang; H.K. Lam; Yongsheng Zhao; HongBin Wang. The study of model predictive control algorithm based on the force/position control scheme of the 5-DOF redundant actuation parallel robot. Robotics and Autonomous Systems 2016, 79, 12 -25.
AMA StyleShuhuan Wen, Guiqian Qin, Baowei Zhang, H.K. Lam, Yongsheng Zhao, HongBin Wang. The study of model predictive control algorithm based on the force/position control scheme of the 5-DOF redundant actuation parallel robot. Robotics and Autonomous Systems. 2016; 79 ():12-25.
Chicago/Turabian StyleShuhuan Wen; Guiqian Qin; Baowei Zhang; H.K. Lam; Yongsheng Zhao; HongBin Wang. 2016. "The study of model predictive control algorithm based on the force/position control scheme of the 5-DOF redundant actuation parallel robot." Robotics and Autonomous Systems 79, no. : 12-25.
A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation (BP) neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP algorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.
Hong-Bin Wang; Mian Liu. Design of robotic visual servo control based on neural network and genetic algorithm. International Journal of Automation and Computing 2012, 9, 24 -29.
AMA StyleHong-Bin Wang, Mian Liu. Design of robotic visual servo control based on neural network and genetic algorithm. International Journal of Automation and Computing. 2012; 9 (1):24-29.
Chicago/Turabian StyleHong-Bin Wang; Mian Liu. 2012. "Design of robotic visual servo control based on neural network and genetic algorithm." International Journal of Automation and Computing 9, no. 1: 24-29.
Hong-Bin Wang; Yan Wang. Open-closed Loop ILC Corrected with Angle Relationship of Output Vectors for Tracking Control of Manipulator. Acta Automatica Sinica 2010, 36, 1758 -1765.
AMA StyleHong-Bin Wang, Yan Wang. Open-closed Loop ILC Corrected with Angle Relationship of Output Vectors for Tracking Control of Manipulator. Acta Automatica Sinica. 2010; 36 (12):1758-1765.
Chicago/Turabian StyleHong-Bin Wang; Yan Wang. 2010. "Open-closed Loop ILC Corrected with Angle Relationship of Output Vectors for Tracking Control of Manipulator." Acta Automatica Sinica 36, no. 12: 1758-1765.
In the research of iterative learning control (ILC), it is usually assumed that the initial states are consistent with the desired states or the initial states are fixed per iteration. By considering the problem that ILC law is difficult to apply to the tracking control for the manipulator under the restriction of initial states, we change the dynamic model of the manipulator system into a lower-order system by reduced-order transformations. For the transformed manipulator system, an open-closed loop ILC algorithm with angle correction term is proposed, which uses the error signal and the deviation of two adjacent error signals to adjust itself. Compared with traditional P-type algorithm, this algorithm makes better use of the saved and current information; while compared with PD-type algorithm, it overcomes the instability caused by the derivative action. Meanwhile, the angle relationship of output vectors is used as a standard to estimate the quality of the control inputs, “awarding or punishing” the changing trend of the algorithm. So, a fast convergence speed and excellent tracking effect are both realized. Improved strategies are proposed for the above algorithm when the limitation of each joint rotating angle is considered. Finally, the simulation results verify the effectiveness of the control scheme.
Hong-Bin Wang; Yan Wang. Open-closed Loop ILC Corrected with Angle Relationship of Output Vectors for Tracking Control of Manipulator. Acta Automatica Sinica 2010, 36, 1758 -1765.
AMA StyleHong-Bin Wang, Yan Wang. Open-closed Loop ILC Corrected with Angle Relationship of Output Vectors for Tracking Control of Manipulator. Acta Automatica Sinica. 2010; 36 (12):1758-1765.
Chicago/Turabian StyleHong-Bin Wang; Yan Wang. 2010. "Open-closed Loop ILC Corrected with Angle Relationship of Output Vectors for Tracking Control of Manipulator." Acta Automatica Sinica 36, no. 12: 1758-1765.