2020/04/03 by Thang Nguyen, Christopher Edwards, Guido Herrmann
Engineering · Mathematics · #Adaptive Control of Nonlinear Systems #Artificial intelligence #Computer science #Context (archaeology) #Control (management) #Control system #Control theory (sociology) #Convergence (economics) #Engineering #Fault Detection and Control Systems #Mathematical optimization #Mathematics #Mode (computer interface) #Nonlinear system #Observer (physics) #Process (computing) #Sampled data systems #Sampling (signal processing) #Stability and Control of Uncertain Systems #State observer
paper · doi:10.1080/00207179.2020.1750706
openalex publication_date 2020/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/25
In this work, we consider the problem of simultaneously estimating the system states and unknown inputs in a linear sampled-data system, whose dynamics is influenced by external disturbances and uncertainties. Hardware limitations prevent an estimation scheme for a sampled-data system from achieving finite-time convergence, which is a typical property of existing sliding mode observers for dynamical continuous-time systems, because the sampling period is finite. Due to the sampling process, an approximate implementation of such an observer, designed for a continuous-time system, may not retain the desired performance in the sampled-data context. In this paper, we present an observer which takes advantage of the quasi-sliding motion concept to simultaneously estimate the state variables and the unknown input signals in a sampled-data context. A theoretical study is conducted to formally justify the convergence properties of the observer whilst simulation results are provided to show the efficiency of the proposed scheme.