Chih-Keng Chen

Prof. Dr. Chih-Keng Chen

Department of Vehicle Engineering, National Taipei University of Technology, Taipei, Taiwan
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Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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31 March 2020 in IEEE/ASME Transactions on Mechatronics
Verified
Journal Article
Closed access
Boyang LiBoyang Li
Boyang Li
JSJingxuan Sun
Jingxuan Sun
WZWeifeng Zhou
Weifeng Zhou
CWChih-Yung Wen
Chih-Yung Wen
KLKin Huat Low
Kin Huat Low
CCChih-Keng Chen
Chih-Keng Chen
This paper focuses on the transition process optimization for a vertical takeoff and landing (VTOL) tail-sitter unmanned aerial vehicle (UAV). For VTOL UAVs that can fly with either hover or cruise mode, transition refers to the intermediate phases between these two modes. This work develops a transition strategy with the trajectory optimization method. The strategy is a reference maneuver enabling the vehicle to perform transition efficiently by minimizing the cost of energy and maintaining a small change of altitude. The simplified 3-degree-of-freedom (3-DOF) longitudinal aerodynamic model is used as a dynamic constraint. The transition optimization problem is then modeled by nonlinear programming (NLP) and solved by the collocation method to obtain the reference trajectory of the pitch angle and throttle offline. Simulations with the Gazebo simulator and outdoor flight experiments are carried out with the optimized forward (hover-cruise) and backward (cruise-hover) transition solutions. The simulation and experimental results show that the optimized transition strategy enables the vehicle to finish transition with less time and change of altitude compared with that by using traditional linear transition methods.
20 August 2022 in Aerospace
Verified
Journal Article
Open access
PDF file
BJBailun JIANG
Bailun JIANG
Boyang LiBoyang Li
Boyang Li
WZWeifeng Zhou
Weifeng Zhou
LLLi-yu LO
Li-yu LO
CCChih-Keng Chen
Chih-Keng Chen
CWChih-Yung Wen
Chih-Yung Wen
A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challenging due to their irregular shape and low Reynolds number behavior. This work proposes an approach to model the full translational dynamics of a quadrotor UAV by a feedforward neural network, which is adopted as the prediction model in a model predictive controller (MPC) for precise position control. The raw flight data are collected by tracking various pre-designed trajectories with PX4 autopilot. The neural network model is trained to predict the linear accelerations from the flight log. The neural network-based model predictive controller is then implemented with the automatic control and dynamic optimization toolkit (ACADO) to achieve real-time online optimization. Software in the loop (SITL) simulation and indoor flight experiments are conducted to verify the controller performance. The results indicate that the proposed controller leads to a 40% reduction in the average trajectory tracking error compared to the traditional PID controller.
30 August 2018 in Sensors
Verified
Journal Article
Open access
PDF file
Boyang LiBoyang Li
Boyang Li
WZWeifeng Zhou
Weifeng Zhou
JSJingxuan Sun
Jingxuan Sun
CWChih-Yung Wen
Chih-Yung Wen
CCChih-Keng Chen
Chih-Keng Chen
This paper presents a model predictive controller (MPC) for position control of a vertical take-off and landing (VTOL) tail-sitter unmanned aerial vehicle (UAV) in hover flight. A ‘cross’ configuration quad-rotor tail-sitter UAV is designed with the capabilities for both hover and high efficiency level flight. The six-degree-of-freedom (DOF) nonlinear dynamic model of the UAV is built based on aerodynamic data obtained from wind tunnel experiments. The model predictive position controller is then developed with the augmented linearized state-space model. Measured and unmeasured disturbance model are introduced into the modeling and optimization process to improve disturbance rejection ability. The MPC controller is first verified and tuned in the hardware-in-loop (HIL) simulation environment and then implemented in an on-board flight computer for real-time indoor experiments. The simulation and experimental results show that the proposed MPC position controller has good trajectory tracking performance and robust position holding capability under the conditions of prevailing and gusty winds.
31 July 2019 in Journal of Low Frequency Noise, Vibration and Active Control
Verified
Journal Article
Open access
PDF file
Chih-Jer LinChih-Jer Lin
Chih-Jer Lin
WCWen-Lin Chu
Wen-Lin Chu
Cheng-Chi Josh WangCheng-Chi Josh Wang
Cheng-Chi Josh Wang
CCChih-Keng Chen
Chih-Keng Chen
ICI-Ting Chen
I-Ting Chen
Ball bearings are important parts of all modern rotating machines. Their function is to reduce friction, support rotating shafts and spindles, and bear loads. Bearing damage can result in abnormal vibrations, cause machine malfunction, and even be dangerous. In this study, analysis of four different ball-bearing conditions was carried out: normal bearings and bearings with inner ring, rolling body, and outer ring malfunction. This was based on electromechanical vibration signals produced on a fault diagnosis simulation platform. The objective was to use a series of signal processing analytical methods to build a set of identification models used to forecast malfunction. Wavelet packet transform technology was first used to process the vibration signal. The signals were pre-processed and analyzed before eigenvalue calculation was done to analyze the signal changes which allowed determination of the nature of the bearing malfunction to be made. The extracted eigenvalues and ball-bearing status categories were input to the support vector machine for model training and testing. Finally, the constructed model parameters were integrated with particle swarm optimization, and the artificial fish-swarm algorithm was used to obtain the optimal parameters for the classifier, and this improved the accuracy of malfunction classification.
30 August 2024 in Vehicles
Verified
Journal Article
Open access
PDF file
NANguyen Thi Anh
Nguyen Thi Anh
CCChih-Keng Chen
Chih-Keng Chen
XLXuhui Liu
Xuhui Liu
Regenerative braking technology is essential for reducing energy consumption in electric vehicles (EVs). This study introduces a method for optimizing the distribution of deceleration forces in front-wheel-drive electric vehicles that complies with the distribution range outlined by ECE-R13 braking regulations and aligns with an ideal braking distribution curve. In addition, using a fuzzy control strategy to manage the complex variables of the regenerative braking process, a robust and adaptable system is developed on the Simulink platform. Tested across various driving cycles are NEDC (New European Driving Cycle), WLTC (World Light Duty Vehicle Test Cycle), FTP-72 (Federal Test Procedure 72), and FTP-75 (Federal Test Procedure 75). The method significantly improves energy efficiency: 13% for WLTC, 16% for NEDC, and 30% for both FTP-72 and FTP-75. The simulation results were compared to regenerative braking control techniques A and B, showing that the proposed control method achieves a higher brake energy recovery rate. This leads to a considerable improvement in the vehicle’s energy recovery efficiency. These findings confirm the efficacy of the proposed regenerative brake control system, highlighting its potential to significantly enhance the energy efficiency of electric vehicles.
10 January 2019 in Sensors
Verified
Journal Article
Open access
PDF file
CCChih-Keng Chen
Chih-Keng Chen
TCTrung-Dung Chu
Trung-Dung Chu
XZXiao-Dong Zhang
Xiao-Dong Zhang
This study designs and controls an active stabilizing assistant system (ASAS) for a bicycle. Using the gyroscopic effect of two spinning flywheels, the ASAS generates torques that assist the rider to stabilize the bicycle in various riding modes. Riding performance and the rider’s safety are improved. To simulate the system dynamic behavior, a model of a bicycle–rider system with the ASAS on the rear seat is developed. This model has 14 degrees of freedom and is derived using Lagrange equations. In order to evaluate the efficacy of the ASAS in interacting with the rider’s control actions, simulations of the bicycle–rider system with the ASAS are conducted. The results for the same rider for the bicycle with an ASAS and on a traditional bicycle are compared for various riding conditions. In three cases of simulation for different riding conditions, the bicycle with the proposed ASAS handles better, with fewer control actions being required than for a traditional bicycle.