Quantized-Feedback-Based Adaptive Event-Triggered Control of a Class of Uncertain Nonlinear Systemsopen access
- Authors
- Choi, Yun Ho; Yoo, Sung Jin
- Issue Date
- Sep-2020
- Publisher
- MDPI
- Keywords
- quantized feedback control; event-triggered; adaptive control; neural networks; unmatched nonlinear uncertainties
- Citation
- MATHEMATICS, v.8, no.9
- Journal Title
- MATHEMATICS
- Volume
- 8
- Number
- 9
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44099
- DOI
- 10.3390/math8091603
- ISSN
- 2227-7390
2227-7390
- Abstract
- A quantized-feedback-based adaptive event-triggered tracking problem is investigated for strict-feedback nonlinear systems with unknown nonlinearities and external disturbances. All state variables are quantized through a uniform quantizer and the quantized states are only measurable for the control design. An approximation-based adaptive event-triggered control strategy using quantized states is presented. Compared with the existing recursive quantized feedback control results, the primary contributions of the proposed strategy are (1) to derive a quantized-states-based function approximation mechanism for compensating for unknown and unmatched nonlinearities and (2) to design a quantized-states-based event triggering law for the intermittent update of the control signal. A Lyapunov-based stability analysis is provided to conclude that closed-loop signals are uniformly ultimately bounded and there exists a minimum inter-event time for excluding Zeno behavior. In simulation results, it is shown that the proposed quantized-feedback-based event-triggered control law can be implemented with less than 10% of the total sample data of the existing quantized-feedback continuous control law.
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