Observer-Based Decentralized Event-Triggered Neuro-Adaptive Controller for Complex Uncertain Affine Nonlinear Systems
Abdul Rehman, A.G. Gondal · 2021
In this paper, an observer-based decentralized event-triggered neuro-adaptive controller (DETNAC) is presented for complex uncertain nonlinear systems. Novelty of the study lies in the construction of the scheme and observer-based control design which uses artificial neural network (ANN), machine learning (polynomial regression), and nominal system dynamics along with Event-triggering. Event-triggering is based on actual system's performance parameter (tracking error) and although multiple uncertainties are considered but online estimation using advance techniques makes controller much robust and efficient. Proposed method not only provide estimation and tracking performance, but its two tunable gains provide filtering effect which helps to avoid transient high frequency oscillations. Lyapunov analysis is used for stability analysis and to develop event-triggering condition. Efficacy of the controller is demonstrated using a nonlinear numerical example of a chaotic complex system.