Interval Valued Fuzzy Modeling and Indirect Adaptive Control of Quadrotor

TL;DR

This paper presents a novel interval-valued fuzzy modeling approach combined with adaptive sliding mode control to manage the complex, uncertain dynamics of a quadrotor, demonstrating effective control through simulation results.

Contribution

It introduces a new Takagi-Sugeno interval-valued fuzzy model for estimating unknown quadrotor dynamics and integrates it with adaptive control for improved handling of uncertainties.

Findings

Effective control of quadrotor with unknown nonlinear dynamics

Successful simulation validation of the proposed method

Enhanced robustness against environmental disturbances

Abstract

In this paper, a combination of fuzzy clustering estimation and sliding mode control is used to control a quadrotor system, whose mathematical model is complex and has unknown elements, including structure, parameters, and so on. In addition, they may be affected by external environmental disturbances. At first, the nonlinear unknown part of the system is estimated by a fuzzy model, A new method is presented for constructing a Takagi-Sugeno (TS) interval-valued fuzzy model (IVFM) based on inputoutput data of the identified system. Following the construction of the fuzzy model that estimates the unknown part of the quadrotor system, a control and on-line adjusting of the fuzzy modeled part of dynamics is used. In this step, the system model will be estimated in adaptive form so that the dynamic equations can be used in sliding mode control. Finally, the proposed technique is applied, and the simulation results are presented to show the effectiveness of this approach in controlling the quadrotor with unknown nonlinear dynamics.