Minh Tuan Hua

• Hua, Tuan; Langås, Even Falkenberg; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2023). From rigid to hybrid/soft robots: Exploration of ethical and philosophical aspects in shifting from caged robots to human-robot teaming. In Yu, Wen (Eds.), 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE conference proceedings. ISSN 978-1-6654-3065-4. p. 1794–1799. doi: %2010.1109/SSCI52147.2023.10372032. Full text in Research Archive
• Hua, Tuan; Sanfilippo, Filippo & Hao, Nguyen Vinh (2022). A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm. Robotics. ISSN 2218-6581. 11(2). doi: 10.3390/robotics11020047. Full text in Research Archive Show summary

  Collaborative robots (or cobots) are robots that are capable of safely operating in a shared environment or interacting with humans. In recent years, cobots have become increasingly common. Compliant actuators are critical in the design of cobots. In real applications, this type of actuation system may be able to reduce the amount of damage caused by an unanticipated collision. As a result, elastic joints are expected to outperform stiff joints in complex situations. In this work, the control of a 2-DOF robot arm with elastic actuators is addressed by proposing a two-loop adaptive controller. For the outer control loop, an adaptive sliding mode controller (ASMC) is adopted to deal with uncertainties and disturbance on the load side of the robot arm. For the inner loops, model reference adaptive controllers (MRAC) are utilised to handle the uncertainties on the motor side of the robot arm. To show the effectiveness of the proposed controller, extensive simulation experiments and a comparison with the conventional sliding mode controller (SMC) are carried out. As a result, the ASMC has a 50.35% lower average RMS error than the SMC controller, and a shorter settling time (5% criterion) (0.44 s compared to 2.11 s).

• Sanfilippo, Filippo; Hua, Tuan Minh & Bos, Steven (2020). A comparison between a two feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators. In Bui, Tung X. (Eds.), Proceeding of the 53rd Hawaii International Conference on System Sciences (HICSS 2020). AIS Electronic Library University of Hawai'i at Manoa Hamilton Library 2550 McCarthy Mall Honolulu, HI 96822. ISSN 978-0-9981331-3-3. p. 881–890. doi: 10.24251/hicss.2020.110. Full text in Research Archive Show summary

  Highly-compliant elastic actuators have become progressively prominent over the last years for a variety of robotic applications. With remarkable shock tolerance, elastic actuators are appropriate for robots operating in unstructured environments. In accordance with this trend, a novel elastic actuator was recently designed by our research group for Serpens, a low-cost, open-source and highly-compliant multi-purpose modular snake robot. To control the newly designed elastic actuators of Serpens, a two-feedback loops position control algorithm was proposed. The inner controller loop is implemented as a model reference adaptive controller (MRAC), while the outer control loop adopts a fuzzy proportional-integral controller (FPIC). The performance of the presented control scheme was demonstrated through simulations. However, the efficiency of the proposed controller is dependent on the initial values of the parameters of the MRAC controller as well as on the effort required for a human to manually construct fuzzy rules. An alternative solution to the problem might consist of using methods that do not assume a priori knowledge: a solution that derives its properties from a machine learning procedure. In this way, the controller would be able to automatically learn the properties of the elastic actuator to be controlled. In this work, a novel controller for the proposed elastic actuator is presented based on the use of an artificial neural network (ANN) that is trained with reinforcement learning. The newly designed control algorithm is extensively compared with the former approach. Simulation results are presented for both methods. The authors seek to achieve a fair, non-biased, risk-aware and trustworthy comparison.

• Hua, Tuan; Sanfilippo, Filippo & Helgerud, Erlend (2019). A robust two-feedback loops position control algorithm for compliant low-cost series elastic actuators. IEEE International Conference on Systems, Man, and Cybernetics (SMC). ISSN 2163-9590. p. 2384–2390. doi: 10.1109/SMC.2019.8913845. Full text in Research Archive

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• Hua, Tuan; Sanfilippo, Filippo & Hao, Nguyen Vinh (2021). An Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm. Show summary

  Collaborative robots (or cobots) are robots that can safely operate together or interact with humans in a shared environment. They have steadily been more prevalent in recent years. The use of compliant actuators is critical in the design of cobots. In fact, this form of actuation system can potentially mitigate the damage caused by unexpected collision. As a result, when working in a complex setting, elastic joints are thought to outperform rigid joints. In this paper, a two-loop adaptive controller is proposed to deal with the control task of a 2-DOF robot arm with elastic actuators. For the outer control loop, an adaptive sliding mode controller (ASMC) is proposed to deal with uncertainties and disturbance on the load side of the robot arm. For the inner loop, model reference adaptive controllers (MRAC) are proposed to handle the uncertainties on the load side of the robot arm. Simulation experiments are conducted to verify the efficiency of the proposed two-loop controller.

• Sanfilippo, Filippo; Hua, Tuan & Bos, Steven (2020). A comparison between a two feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators.

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