Fault Tolerant Control

Robust Safe Control and Fault Identification for Robotic Systems

Introduction: Safety is always a critical issue for robotic systems especially in the context of human-robot interaction or collaboration.  The uncertainties brought by the human cooperators greatly increase the challenges in the design of the man-in-the-loop systems. On the one hand, the task targets of the systems should be achieved with human uncertainties, which renders a robustness issue. On the other hand, the human-robot collaboration system should always guarantee the hard compliance of the required constraints to ensure the safety of humans. However, the current technical standard in the manufacturing industry is far from satisfying these demands. Therefore, the design of a safe human-robot collaboration system is still an open problem. 

Among the diverse research fields in safe human-robot interaction,  control, observation, and fault identification are important topics to guarantee the safe performance of robotic systems. A safe controller is necessary to ensure that the robotic systems achieve the desired tasks without violating the safety rules or causing injuries to human operators. Meanwhile, fault identification is an important technology to quantify the influence of disturbance or system faults on the accomplishment of the desired tasks. Given that the conventional methods of robust control and fault identification focus too much on the steady-state performance of the systems, namely tracking control errors and identification errors,  they cannot ensure sufficient bandwidth of the system responses. For robot control, this issue leads to the system being sensitive to high-bandwidth disturbances, such as fast-changing signals or hard safety constraints. For fault identification, this issue means the difficulty of the system in reconstructing high-bandwidth disturbances, such as square signals or triangle signals. In this research, we are dedicated to finding novel control and learning approaches that can ensure fast response of the systems in robust control and precise fault identification. To be more specific, we are interested in using second-order sliding mode control, time-delay estimation, supervised learning, and Bayesian inference to achieve a better balance between the steady performance and the bandwidth of the closed-loop systems. All these approaches form a novel safety framework for the design of robotic systems in human-robot collaboration contexts with human uncertainties and hard safety requirements. This framework is promising in inspiring using both control-theory-based and machine-learning-based methods to build safe collaborative robots. The scope of the framework is mainly described in the following four aspects.

1. A high-precision online identification method for the external forces of robotic systems that are able to swiftly perceive human-robot collisions without extrinsic force sensors. The method is based on the sliding mode observation theory which ensures both high-bandwidth and robust precision;

2. A fast and accurate fault diagnosis scheme based on the supervised learning methods, which is able to identify accidental collisions from intentional interactions;

3. A sliding-mode-based robust robot motion control scheme that ensures fast and precise tracking of the desired trajectory with compliance of the predefined safety constraints;

4. A novel human-motion actuation mechanism based on sliding mode control, which can generate human trajectories with robustness to the inherent randomness and uncertainties.

The investigated safety framework involves various research fields including the control and observation theory, machine learning, fault detection and isolation, and stochastic-model-based neuro-dynamics. By proposing such a framework, we expect to take a step forward in the state of the art for a faster, more accurate, and more reliable human-robot collaboration system that benefits the current manufacturing industry. 

Projects:

• hybrid Human-Robot RECYcling plant for electrical and eLEctRonic equipment (HR-recyler) [Details]

• Human-Robot Collaboration in Industrial Production [Details]

Thesis:

• Z., Zhang. Towards Safe Human-Robot Collaboration Oriented to Collision Handling. Dissertation. Technische Universität München, 2021. [MediaTUM]

Publications:

• Z. Zhang*, D. Wollherr, "Disturbance Estimation for High-Degree-of-Freedom Euler-Lagrangian Systems Using Sliding Mode Observer without Matching Conditions". [arXiv]

• Z. Zhang, D. Wollherr, and H. Najjaran*,  "Disturbance estimation for robotic systems using continuous integral sliding mode observer", in International Journal of Robust and Nonlinear Control, Vol. 32, no. 14, pp. 7946-7966, Sept. 2022, doi: 10.1002/rnc.6252. [Wiley]

• Y. Wang, Z. Zhang*, C. Li, and M. Buss, Adaptive incremental sliding mode control for a robot manipulator, in Mechatronics, vol. 82, no. 2022, pp. 102717, 2022, doi: 10.1016/j.mechatronics.2021.102717. [ScienceDirect]

• Z. Zhang, Y. Wang*, and Dirk Wollherr, "Safe Tracking Control of Euler-Lagrangian Systems Based on A Novel Adaptive Super-twisting Algorithm", in IFAC-PapersOnLine, vol. 53, no. 2, pp. 9974-9979, July. 2020, doi: 10.1016/j.ifacol.2020.12.2714. [ScienceDirect]

• Z. Zhang, K. Qian*, B. W. Schuller, and D. Wollherr, "An Online Robot Collision Detection and Identification Scheme by Supervised Learning and Bayesian Decision Theory," in IEEE Transactions on Automation Science and Engineering, vol. 18, no. 3, pp. 1144-1156, July. 2021, doi: 10.1109/TASE.2020.2997094. [IEEEXplore]

• Z. Zhang*, M. Leibold, and D. Wollherr, "Integral Sliding-Mode Observer-Based Disturbance Estimation for Euler–Lagrangian Systems," in IEEE Transactions on Control Systems Technology, vol. 28, no. 6, pp. 2377-2389, Nov. 2020, doi: 10.1109/TCST.2019.2945904. [IEEEXplore]

• Y. Sun*, Z. Zhang, M. Leibold, R. Hayat, D. Wollherr, and M. Buss. "Protective Control For Robot Manipulator By Sliding Mode Based Disturbance Reconstruction Approach." 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). Munich, Germany, 03-07 July 2017.

Datasets:

• The Robot Joint Torque Measurements for Accidental Collisions and Intentional Contacts [Zenodo]