Attitude Control of a Quadcopter Using Adaptive Control Technique

Abstract

This chapter presents an adaptive control technique to stabilize the attitude dynamics of unmanned aerial vehicle (UAV) type quadrotor in the presence of disturbances and/or uncertainties in the parameters due to changes in the payload, nonlinear actuators, and change in environmental conditions. To address the above problem, MRAC (model reference adaptive control) strategy is used. In this schema, a cost function is defined as a function of the error between the output of the system and a desired response from a reference model. Based on this error, the controller parameters are updated. To guarantee the global asymptotic stability of the system, Lyapunov's theory is applied. Simulation results using MATLAB-Simulink platform are presented to demonstrate the convergence of the controller parameters.

A Review of Quadrotor Unmanned Aerial Vehicles: Applications, Architectural Design and Control Algorithms

“…Adaptive control [30,92,[113][114][115]151] -Able to deal with systems that have unpredictable parameter variation and disturbances. -Able to deal with un-modelled dynamics.…”

“…Common control laws which have received a large interest from researchers are the Backstepping [16][17][18], Gain- scheduling [110][111][112], adaptive control [92,[113][114][115], H ∞ Control [116,117], Fuzzy Logic [118][119][120], Linear Quadratic Regulators (LQR) [8][9][10][11], Proportional Integral Derivative (PID) [5][6][7]98] and Sliding mode controllers (SMC) [7,12,13]. Since these are found to be the popular control techniques for attitude stabilisation and position control, this review will focus on providing the reader with an overview of the controller functionality as well as other researchers' comments on the performance.…”

“…Thus, the adaptive control method can be applied such that these uncertainties are identified [115]. The authors in [113] have investigated a model reference adaptive control (MRAC) which is a strategy that adjusts the controller's parameters such that the plant dynamics can track the reference path successfully. While the system states are fed back to the Fig.…”

“…16 Tracking trajectory using the gain scheduled controller [110] Fig. 17 Model reference adaptive control (MRAC) applied on to the Quadrotor model [113] approach, the state-dependant bounds of internal and external uncertainties has been successfully approximated and estimated to build a successful control scheme.…”

Optimal Control and Adaptive Learning for Stabilization of a Quadrotor-type Unmanned Aerial Vehicle via Approximate Dynamic Programming

“…Segundo [9], grande parte das publicações científicas acerca de quadricópteros tem se concentrado na solução de algoritmos de controle. Dentre as técnicas de controle aplicadas, pode-se destacar as seguintes: a abordagem de Lyapunov [12], a qual garante, sob certas condições, a estabilidade assintótica do quadricóptero; a estrutura de realimentação PD, Proporcional-Derivativo [13,14], com propriedade de convergência exponencial devido à compensação dos termos Coriolis e giroscópicos, e a estrutura PID, Proporcional-Integral-Derivativo [7,15], a qual não requer o conhecimento de parâmetros específicos do modelo e a lei de controle é muito mais fácil de implementar, porém com robustez limitada contra perturbações; o controle RLQ (Regulador Linear Quadrático) [16], e controle H ∞ [4], cuja vantagem é que exibem boas propriedades de robustez: margem de ganho infinitamente crescente, margem de fase entre ±60 • e boa tolerância à nãolinearidades; controle adaptativo [17,18,19], que fornecem bom desempenho com parâmetros incertos e dinâmicas não modeladas. Existem outros algoritmos de controle para melhorias do desempenho de sistemas quadrirrotores, tais como técnicas fuzzy [1,20], redes neurais [21], controle backstepping [22,23], controle baseado em realimentação visual [24,25], e controle baseado em aprendizagem por reforço [26,27,28].…”