Event-triggered adaptive output-feedback control of a class of input-affine nonlinear systems using a general type-2 fuzzy neural observer: real-time experimental validation

This paper proposes an event-triggered, observer-based adaptive output-feedback control scheme for a class of nonlinear systems with partially unavailable states, formulated using a General Type-2 Takagi–Sugeno Fuzzy Neural Network (GT2-TS-FNN). The proposed framework comprises two components: an intelligent event-triggered full-state observer that estimates the unmeasurable states and a fuzzy output-feedback controller, both constructed based on the GT2-TS-FNN. To decompose a General Type-2 Fuzzy Set into several Interval Type-2 Fuzzy Sets, an efficient α-cut strategy is employed. In addition, a direct defuzzification method is adopted to reduce the computational burden associated with type reduction, thereby avoiding the iterative Karnik–Mendel algorithm. Based on Lyapunov stability theory, an adaptive updating law is derived to ensure fast convergence and guarantees the stability of the overall closed-loop system. The proposed scheme is further implemented on a microcontroller for the real-time speed control of an induction-motor drive operating over a networked communication protocol, where the event-triggered mechanism significantly reduces both the communication frequency and the computational load. Both simulation and experimental results confirm improved tracking performance under random disturbances, external load variations, and sensor-dropout conditions.