Online Simultaneous State and Parameter Estimation for Second-order Nonlinear Systems

Abstract

In this paper, a concurrent learning based adaptive observer is developed for a class of second-order nonlinear time-invariant systems with uncertain dynamics. The developed technique results in simultaneous online state and parameter estimation. A Lyapunov-based analysis is used to show that the state and parameter estimation errors are uniformly ultimately bounded. As opposed to persistent excitation which is required for parameter estimation in traditional adaptive control methods, the developed technique only requires excitation over a finite time interval.

arXiv admin note: text overlap with arXiv:1609.05879

References in corpus (1)

• Online Output-Feedback Parameter and State Estimation for Second Order Linear Systems

Cited by in corpus (7)

• Simultaneous state and parameter estimation: the role of sensitivity analysis
• Online Observer-Based Inverse Reinforcement Learning
• Online inverse reinforcement learning with unknown disturbances
• Inverse reinforcement learning in continuous time and space
• Output-feedback online optimal control for a class of nonlinear systems
• Online inverse reinforcement learning for nonlinear systems
• Observer-Side Parameter Estimation For Adaptive Control