Andrea L'Afflitto

By Keyvan Mohammadi and Andrea L’Afflitto

Quadrotor helicopters are drawing considerable attention both for their mobility and their potential to perform multiple tasks in complete autonomy. Moreover, the numerous limitations characterizing these aircraft, such as their underactuation, make quadrotors ideal testbeds for innovative theoretical approaches to the problem of controlling autonomous mechanical systems. In this chapter, we propose a robust model reference adaptive control architecture and design an autopilot for quadrotors, which guarantees satisfactory output tracking despite uncertainties in the vehicle’s mass, matrix of inertia, and location of the center of mass. The feasibility of our results is supported by a detailed analysis of the quadrotor’s equations of motion. Specifically, considering the vehicle’s equations of motion as a time-varying nonlinear dynamical system and avoiding the common assumption that the vehicle’s Euler angles are small at all times, we prove that the proposed autopilot guarantees satisfactory output tracking and verifies sufficient conditions for a weak form of controllability of the closed-loop system known as strong accessibility. A numerical example illustrates the applicability of the theoretical results presented and clearly shows how the proposed autopilot outperforms in strong wind conditions autopilots designed using a commonly employed proportional-derivative control law and a conventional model reference adaptive control law.

Part of the book: Adaptive Robust Control Systems

By Giri M. Kumar, Mattia Gramuglia and Andrea L’Afflitto

This chapter presents the first robust model reference adaptive control (MRAC) system for hybrid, time-varying plants affected by parametric, matched, and unmatched uncertainties as well as uncertainties in the plant’s discrete-time dynamics. This continuous-time component of this MRAC system comprises both an adaptive law and a control law that are analogous to the adaptive law and control law of classical MRAC systems. The discrete-time component of the proposed MRAC system comprises a resetting mechanism that counters the effect of resetting events in the plant dynamics. The mechanisms that guarantee robustness to unmatched uncertainties extend the well-known σ-modification and e-modification of MRAC as well as the use of continuous projection operators to a hybrid systems framework. This adaptive control framework is applied to the problem of controlling output-feedback linearized dynamical models while switching among multiple feedback-linearizing output signals according to any user-defined algorithm that is compatible with the conditions sufficient for the existence of the linearizing diffeomorphism. As an example, we solve the problem of controlling the dynamics of a quadcopter unmanned aerial vehicle (UAV) tasked with following both a user-defined trajectory and a user-defined attitude, and not just a user-defined yaw angle as it occurs in the overwhelming majority of works on this topic.

Part of the book: Latest Adaptive Control Systems [Working title]