Interval-valued fuzzy estimation and its application to adaptive control of quadrotor

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 contains unknown elements, including structure and parameters. Additionally, the system may be susceptible to external environmental disturbances. Initially, the nonlinear unknown components of the system are estimated using a fuzzy model. We introduce a novel approach for constructing a Takagi–Sugeno (TS) interval-valued fuzzy model (IVFM) based on input–output data obtained from the identified system. Following the construction of the fuzzy model that estimates the unknown aspects of the quadrotor system, we implement a control strategy with online adjustments for the fuzzy-modeled dynamics. In this phase, the system’s model is estimated in an adaptive manner, allowing the dynamic equations to be utilized in sliding mode control. Finally, we apply the proposed technique to a quadrotor, presenting simulation results to demonstrate the effectiveness of this approach in controlling a system characterized by unknown nonlinear dynamics.