Ayad Al-Mahturi

UNSW Sydney | UNSW

The development of Unmanned Aerial Vehicles (UAVs) has become one of the most fruitful research areas in the field of autonomous flight control. Quadcopters are chosen due to their simple mechanical structure which is able to hover in a stationary manner, vertical takeoff and landing. Nevertheless, these types of aircraft are highly nonlinear and u...

Autonomous Underwater Vehicles (AUVs) have attracted a lot of interest in recent years as a tool to perform various underwater tasks in both civilian and military sectors. As AUVs' dynamics are highly nonlinear, complex, and time-varying, several studies have been conducted to develop an adaptive control based on intelligent control techniques. Thi...

Recently, Type-2 fuzzy systems have become increasingly prominent as they have been applied to various nonlinear control applications. This article presents an adaptive fuzzy controller based on the sliding-mode control theory. The proposed self-adaptive interval Type-2 fuzzy controller (SAF2C) is based on the Takagi-Sugeno (TS) fuzzy model and it...

This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure...

This paper aims to design an enhanced self-adaptive interval type-2 fuzzy control system (ESAF2C) for stabilization of a quadcopter drone under external disturbances. Due to the ability to accommodate the footprint-of-uncertainty (FoU), an interval type-2 Takagi-Sugeno fuzzy scheme is employed to directly address the uncertainties in the nonlinear...

This chapter presents the applications of an interval Type-2 (IT2) Takagi-Sugeno (T-S) fuzzy system for modeling and control the dynamics of a quad-copter unmanned aerial vehicle (UAV). In addition to being complex and non-linear, the dynamics of a quadcopter are under-actuated and uncertain, making the modeling and control tasks across its full fl...

This work was accepted to be presented at the SSCI conference in 2020.

This paper presents an novel online system identification technique based on a recursive interval type-2 Takagi-Sugeno fuzzy C-means clustering technique (IT2-TS-FC) for modeling nonlinear uncertain dynamics of autonomous systems. The construction of the fuzzy antecedent parameters is based on the type-2 fuzzy C-means clustering (IT2FCM) technique,...

Presentation slides for the EAIS conference 2010.

This paper proposes a self-evolving Takagi-Sugeno fuzzy controller for nonlinear systems with uncertainties. The self-evolving framework is used to add and prune fuzzy rules in an online manner. Our proposed nonlinear controller is model-free and does not depend on the plant dynamics. All adjustable fuzzy parameters are tuned using a sliding surfac...

Presentation slides for the SSCI conference 2019.

This paper presents a sequential learning machine based on the Takagi-Sugeno (TS) fuzzy inference system to model the dynamics of a MIMO nonlinear quadcopter using experimental data. Unlike conventional TS-fuzzy systems, all the antecedent and consequent parameters of our proposed TSfuzzy model are updated using the gradient descent-based backpropa...

Presentation slides for the IAICT conference 2019.

Presentation slides for the FUZZ-IEEE conference 2019.

Presentation slides for the SSCI conference 2018.

Conference slides.

Book chapter cover