Shyh-Leh Chen was born on October 25, 1964, in Keelung, Taiwan. He received B.S and M.S. degrees from National Tsing-Hua University, Hsin-Chu, Taiwan, in 1987 and 1989, respectively, both in power mechanical engineering. He received a Ph.D. degree in mechanical engineering from Michigan State University, East Lansing, Michigan, USA, in 1996.

Since 1996, he has been with National Chung Cheng University, Chiayi, Taiwan, where he is currently Dean of the Office of research and Development, and Distinguished Professor in the Department of Mechanical Engineering. He served as Deputy Director of Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI) from 2011 to 2017. He also served as the Director of Advanced Machine Tools Research Center from 2010 to 2013. His research interests include nonlinear dynamics and control, wavelet analysis, with application to motion control of multi-axis systems, active magnetic bearings, and ship stabilization. 

Prof. Chen has served as the program chairs/invited session chairs of several international conferences, including 9th World Congress on Intelligent Control and Automation (WCICA 2011), 2013 CACS International Automatic Control Conference (CACS 2013), the 5th International Conference on Advanced Manufacturing (ICAM 2014), and the tenth annual IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014), and the 35th Annual Conference of Chinese Society of Mechanical Engineers (CSME 2018), the 22nd Annual Meeting of Chinese Society of Mechanism and Machine Theory (CSMMT 2019), the 2021 CACS International Automatic Control Conference (CACS 2021). He received the Automatic Control Award for Young Scholars and Outstanding Automatic Control Award from Chinese Automatic Control Society in 2003 and 2015, respectively, Outstanding Teaching Award from CCU in 2006, Delta Award from IEEE Tainan section in 2017, and Distinguished Research Award from CCU in 2018. He was the recipient of the Best Paper Award of several conferences, including: The 2011 IAENG International Conference on Control and Automation, 2013 Conference on Precision Machinery and Manufacturing Technology (PMMT 2013), 2015 CACS International Automatic Control Conference (CACS 2015), 2017 CACS International Automatic Control Conference (CACS 2017), 2021 International Automatic Control Conference(CACS 2021), and MDPI Electronics Special Awards, the 9th International conference on Advanced Robotics and Intelligent Systems (ARIS 2021). He is a Fellow of Chinese Automatic Control Society. He is an associated editor for Mechatronics (since 2021) and IEEE Transactions on Automation Science and Engineering (since 2022).

The lab's main research area is dynamic analysis and control, with applications to mechatronic systems. In recent years, the research topics have focused on two areas: the development and application of multi-axis motion systems for machine tools and robots, and magnetic levitation systems. The lab is committed to developing control theory methods and related practical technologies that are helpful for practical applications. Major research results in recent years include:

1. Integrate equivalent contour error and iterative learning control (ILC) theory and apply it to the intelligence of machine tools. In recent years, we have developed several ILC technologies and applied them to multi-axis motion systems (including computer numerical control (CNC) machine tools and industrial robots), and has published the results in international journals and patents. Representative results include
 Shyh-Leh Chen, Chih-Chao Wen and Ro-Lan Chao, “Performance Enhancement of Rigid Tapping by Iterative Learning Control,” Asian Journal of Control, Vol. 20, Issue 4, pp. 1413-1426, July, 2018.
 Shyh-Leh Chen and Sheng-Min Hsieh, “Iterative Learning Contouring Control: Theory and Application to Biaxial Systems,” Mechatronics, Vol. 89, 102932, Dec., 2022.
 Thanh-Quan Ta and Shyh-Leh Chen, “Iterative Learning Control for Integrated System of Robot and Machine tool,” Asian Journal of Control, Vol. 25, Issue 2, pp. 807-823, March, 2023.
 Shyh-Leh Chen, Sheng-Min Hsieh, and Thanh-Quan Ta, “Iterative Learning Contouring Control for Five-axis Machine Tools and Industrial Robots,” Mechatronics, Vol. 94, 103030, October, 2023.
 Chen Shile, Li Huixuan, Chen Pengsheng, "Learning system and method for controlling dual-axis machine tool by equivalent contour error", ROC Invention Patent No. I 679507, December 11, 2019 to October 22, 2038.
These ILC technologies are mainly aimed at tracking control, that is, they focus on reducing contour errors. Several characteristics of tracking control make it difficult to directly apply traditional ILC, including complex contour error models and the inconsistency between the control target and the number of input commands. Some studies combine ILC with cross-coupled control (CCC), called CCILC, but it has several problems. First, it requires an accurate contour error model, which is usually difficult to obtain and is usually approximated. Other accurate contour error estimation methods involve complex optimization calculations and are not efficient. Secondly, the contour error estimated by CCILC is decomposed into components of each axis and compensated through ILC. In other words, what is reduced through ILC is still the tracking error (the part that contributes to the contour error). Its control target (tracking error) is consistent with the number of input commands, but it does not directly reduce the contour error, so the performance will be compromised. Finally, the CCILC method is difficult to generalize to systems with more than three axes. It is based on a contour error model established in the task space. For systems with more than 3 axes, the contour error model is difficult to construct because in addition to the position contour error, it also involves the tool azimuth error and forward and reverse kinematic transformations. The ILCC technology we proposed can solve the problems of existing ILC applied to tracking control of multi-axis motion systems (such as CCILC). Its main advantages include: (1) it can directly reduce contour errors; (2) it does not require precise contour error models or approximations, so it is more efficient and accurate. In other words, there is no need for complex optimization algorithms to estimate contour errors; (3) the inconsistency between the number of control targets and input commands can be handled, and the learning gain of ILCC can be systematically designed; (4) it can be applied to systems with more than three axes, such as five-axis machine tools and robots. Our ILCC algorithm is applied to the mechanical axis coordinates, which can directly match the system dynamics of five-axis machine tools and robots to reduce the equivalent contour error. Reducing the equivalent contour error can simultaneously reduce the position contour error and the tool azimuth error. This is the only ILC method in the literature that can effectively deal with the tracking control problem of five-axis machine tools.
2. Proposed the innovative dual-robot processing system "Luban". Under the general trend of intelligent automation, machine tool and robot integration systems have become very common. However, this type of integrated system still requires individual machine tools and robot systems. The machine tools are responsible for processing, while the role of the robot is positioned as production automation, so the most common function is to pick and place workpieces. In order to further integrate the functions of robotic arms and machine tools, the project leader implemented two three-year projects of the Ministry of Science and Technology (closed) to design and develop a dual-robotic arm processing system and named it "Lu Ban", hoping that it will have superb processing technology like Lu Ban, the father of craftsmen. The main body of Luban is composed of two sets of robotic arms with five degrees of freedom each, equipped with spindle motors and tools fixed on the same plane as the robotic arms, to perform highly complex surface cutting processing. When clamping workpieces for processing, the dual-arm robot is a five-degree-of-freedom parallel mechanism with five-axis processing capabilities. When processing, the two arms can each perform other automated functions, such as picking and placing workpieces, assembly, etc. Therefore, compared to the common machine tool and robot integration systems today, Luban has both serial and parallel type architectures, which can be flexibly applied. During processing, it works in double-arm mode and clamps the workpiece at the same time, which can be regarded as a parallel structure with better rigidity. When performing other automated functions, it works in single-arm mode and can perform two different automated functions at the same time, improving the efficiency of time and space utilization. In addition, it has many advantages: complex functions, multi-functional automation, cost reduction, etc. In the past five years, papers published in this area include
 Goragod Junplod, Woraphrut Kornmaneesang, Shyh-Leh Chen, and Sarawan Wongsa, “Modeling of an External Force Estimator for an End-effector of a Robot by Neural Networks,” Journal of the Chinese Institute of Engineers, Vol. 46, Issue 8, pp. 895-904, Oct., 2023.
 Woraphrut Kornmaneesang, Shyh-Leh Chen and Sudchai Boonto, “Contouring Control of an Innovative Manufacturing System Based on Dual-arm Robot,” IEEE Transactions on Automation Science and Engineering, Vol. 19, Issue 3, pp. 2042-2053, July, 2022.
 Woraphrut Kornmaneesang and Shyh-Leh Chen, “Time-optimal Feedrate Scheduling with Actuator Constraints for 5-axis Machining,” International Journal of Advanced Manufacturing Technology, Vol. 119, pp. 6789-6807, 2022.
 Woraphrut Kornmaneesang and Shyh-Leh Chen, “MPC-based Robust Contouring Control for a Robotic Machining System,” Asian Journal of Control, Vol. 23, Issue 3, pp. 1212-1224, May, 2021.
 Woraphrut Kornmaneesang, Thanana Nuchkrua and Shyh-Leh Chen, “Contouring Control of a 5-DOF Robot Arm for Machining,” iROBOTICS, Vol. 1, No. 1, pp. 35-40, April, 2018.
3. Propose an innovative tracking controller design method and apply it to various types of complex motion control systems. The applicant proposed the concept of equivalent contour error, converting the tracking control problem into a stabilization problem, and combined with nonlinear control theory to design a tracking controller that integrates feedback, feedforward, and cross-coupling effects. It is applicable to general curves and easy to calculate. The advantage of using the equivalent contour error model as the control target of tracking motion over the traditional contour error model is that it can directly control the contour error without the need for linear approximation. Thanks to the support of the Ministry of Science and Technology's past projects, we have established a number of tracking control theories based on equivalent contour errors, including three types of multi-axis motion systems: general, position-constrained, and non-integrable velocity-constrained. We also explored the tracking control of smooth paths and non-smooth paths. At the same time, we also apply it to many important multi-axis motion systems: two-axis platform system, five-axis machine tool, parallel mechanism, robot system, and two-wheel inverted pendulum vehicle system. More than 10 papers have been published in this area in previous years, and the papers published in the past five years are
 Shyh-Leh Chen, Mun-Hooi Khong and Sheng-Min Hsieh, “Contouring Control of a Five-axis Machine Tool with Equivalent Errors,” Electronics, Vol. 11, No. 16, p. 2521, August, 2022.
4. Propose S-type acceleration and deceleration optimization technology. Currently, acceleration and deceleration planning are all considered from the path. However, such a design is not only too conservative, but may also be too conservative in actual processing (such as speed limit for linear processing), and there is also a risk of exceeding the motion limit of the drive axis (such as acceleration limit for arc or curve processing). Therefore, in addition to the motion restrictions on the path, we also need to consider the motion restrictions of each axis. Under these constraints, we hope to find the acceleration and deceleration parameters that minimize the processing time. Therefore, this can be regarded as an optimization problem with multiple constraints, and an evolutionary algorithm can be used to find the best solution, such as the particle swarm algorithm (PSO). Since evolutionary algorithms evaluate the quality of solutions through objective functions, if constraints are added to the objective function, solutions that do not meet the constraints will obtain lower objective function values ​​and will be eliminated in the evolution. When the evolutionary algorithm is used to determine whether the designed acceleration and deceleration meets the axis restriction conditions, the simplified assumptions previously made to generate analytical solutions can be relaxed. For example, the restrictions of each axis can be directly considered without the need to design the acceleration and deceleration curve based on approximate path restrictions, and the acceleration and deceleration curve is no longer limited to a symmetrical form. This will be more conducive to finding the optimal acceleration and deceleration curve. In the past five years, papers published in this area include
 Bhumesh Patle, Shyh-Leh Chen, Anil Singh, and Sunil Kumar Kashyap, “Optimal Trajectory Planning of the Industrial Robot Using Hybrid S-Curve-PSO Approach,” Robotic Intelligence and Automation, Vol. 43, No. 2, pp. 153-174, May, 2023.
 Tzyy-Chyang Lu and Shyh-Leh Chen, “Novel Feedrate Optimization Method for NURBS Tool Paths Under Various Constraints,” IEEE Access, Vol. 10, pp. 3192-3205, 2022.
 Tzyy-Chyang Lu and Shyh-Leh Chen, “Real-time local optimal Bezier corner smoothing for CNC machine tools,” IEEE Access, vol. 9, pp. 152718-152727, 2021.
 Tzyy-Chyang Lu, Shyh-Leh Chen and Eileen Chih-Ying Yang, “Time-optimal S-curve Velocity Planning for Multiple Line Segments under Axis Constraints,” IEEE Transactions on Industrial Electronics, Vol. 65, Issue 12, pp. 9582-9592, December 2018.
5. Committed to the design and control technology development of magnetic bearings and applied to flywheel batteries. Generally, magnetic bearings have eight magnetic poles, which has the advantage that the magnetic circuit is less coupled and therefore easier to control. The applicant proposed a three-pole type and found that not only its copper loss and iron loss are lower than the eight-pole type, but also it only requires two sets of drive circuits and power amplifiers (the eight-pole type requires four sets), thereby reducing costs and greatly improving the practicality of the magnetic bearing. As for the serious nonlinear problem caused by magnetic coupling, it is solved by using the newly developed nonlinear control method. We have developed technologies for optimization design, modeling, current control, voltage control and smoothing control, and have actually manufactured several sets of magnetic levitation rotor systems for testing and verification, with fruitful results. In recent years, we have applied the magnetic bearing technology accumulated over the years to the development of flywheel batteries. The magnetic bearing is the most critical component that distinguishes advanced flywheel batteries from traditional flywheel batteries. It uses magnetic force to suspend the rotor in the air, thereby eliminating the energy loss and vibration caused by friction between the rotor and the bearing, allowing the flywheel to break through the speed limit of traditional bearings. We have published many papers in internationally renowned journals in this area. In addition, the applicant has also obtained patents in China, the United States, Switzerland and other countries. More than 10 papers have been published in this area in previous years, and papers published in the past five years include
 Shyh-Leh Chen, Yi-Tsung Li, Chin-Hsiang Lin, and Chao-Yun Chen, “Effects of Imperfect Assembly and Magnetic Properties on the Three-pole AMB System,” Applied Sciences, Vol. 13, No. 1, pp. 347, Dec., 2022.
 Shyh-Leh Chen, Chow Shing Toh, and Shyu-Yu Lin, “Adaptive Unbalance Compensation for a Three-pole AMB System,” IEEE Transactions on Industrial Electronics, Vol. 67, Issue 3, pp. 2097-2106, March, 2020.