Robustification of Learning Observers to Uncertainty Identification via Time-varying Learning Intensity

This brief studies the simultaneous estimation of states and uncertainties in general continuous-time systems. In particular, we present a novel time-varying learning intensity (TLI) learning observer (LO). It has the advantage of inheriting the valuable properties of conventional LOs with a simple structure, i.e., the uncertainty estimation is achieved using simply one algebraic equation with low computational costs. The foremost difference in comparison with conventional LOs is the utilization of the TLI approach, which attenuates the overshooting response in the case of large estimation errors and obtains decent performance improvement. Simulations for constant and time-varying signals demonstrate a notable performance boost of TLI-LO.

[1]  Choon Ki Ahn,et al.  On Attitude Tracking Control With Communication-Saving: An Integrated Quantized and Event-Based Scheme , 2021, IEEE Transactions on Circuits and Systems II: Express Briefs.

[2]  Wen Chen,et al.  Simultaneous identification of time-varying parameters and estimation of system states using iterative learning observers , 2007, Int. J. Syst. Sci..

[3]  Choon Ki Ahn,et al.  Learning Observer and Performance Tuning-Based Robust Consensus Policy for Multiagent Systems , 2022, IEEE Systems Journal.

[4]  Bing Xiao,et al.  On low-complexity control design to spacecraft attitude stabilization: An online-learning approach , 2021 .

[5]  Xiaodong Shao,et al.  Event-Based Prescribed Performance Control for Dynamic Positioning Vessels , 2021, IEEE Transactions on Circuits and Systems II: Express Briefs.

[6]  Youmin Zhang,et al.  Engineering Notes Observer-Based Attitude Control for Satellite Under Actuator Fault , 2015 .

[7]  Changyin Sun,et al.  A compensation method for the packet loss deviation in system identification with event-triggered binary-valued observations , 2020, Science China Information Sciences.

[8]  Guanghui Wen,et al.  Event-Triggered Master–Slave Synchronization With Sampled-Data Communication , 2016, IEEE Transactions on Circuits and Systems II: Express Briefs.

[9]  Xiao Chen,et al.  Observer-Based Finite-Time Attitude Containment Control of Multiple Spacecraft Systems , 2021, IEEE Transactions on Circuits and Systems II: Express Briefs.

[10]  Yuandan Lin,et al.  A Smooth Converse Lyapunov Theorem for Robust Stability , 1996 .

[11]  Qiang Liu,et al.  Data-driven multimodal operation monitoring and fault diagnosis of high-speed train bearings , 2020, SCIENTIA SINICA Informationis.

[12]  Yingchun Zhang,et al.  Integrated design of fault reconstruction and fault-tolerant control against actuator faults using learning observers , 2016, Int. J. Syst. Sci..

[13]  Hui Yi,et al.  Data-driven fault diagnosis for dynamic traction systems in high-speed trains , 2020 .

[14]  Hamid Reza Karimi,et al.  An Adaptive Event-Triggered Synchronization Approach for Chaotic Lur’e Systems Subject to Aperiodic Sampled Data , 2019, IEEE Transactions on Circuits and Systems II: Express Briefs.

[15]  Xueqin Chen,et al.  Robust fault reconstruction via learning observers in linear parameter-varying systems subject to loss of actuator effectiveness , 2014 .

[16]  Hongjing Liang,et al.  Event-Triggered Fault Detection and Isolation of Discrete-Time Systems Based on Geometric Technique , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[17]  Huayi Li,et al.  Fault Reconstruction for Continuous‐Time Systems Via Learning Observers , 2016 .

[18]  Wen Chen,et al.  Fault Reconstruction and Fault-Tolerant Control via Learning Observers in Takagi–Sugeno Fuzzy Descriptor Systems With Time Delays , 2015, IEEE Transactions on Industrial Electronics.