Adaptive fuzzy control of a quadrotor using disturbance observer

Robust Approximate Optimal Trajectory Tracking Control for Quadrotors

“…The rotor speeds are related to pulse-width modulated (PWM) signals through the motors. The lift forces and torques generated by the four motors are related to the pulse width of the input signals as follows [36]:…”

A Novel Nonlinear Quadrotor Attitude Regulation with Disturbances and Preassigned Convergences

“…With the requirement of strong disturbance rejection as well as precise control performance, some attempts have been made by adding feedforward compensation based on the controller. To guarantee the improvement of feedforward perfor-mance, the disturbance observer and corresponding theory have been investigated for quadrotors in traditional techniques, such as sliding mode observers (SMO) [6][7][8][9], function approximators by neural networks (NN) [10][11][12][13], fuzzy logistic system (FLS) [14,15], and extended state observers (ESO) [16,17]. In [8], a first-order SMO equipped with high gain observer is designed to estimate unknown disturbance.…”

“…To realize the finite-time convergence, an adaptive integral sliding mode control is proposed with a novel fully connected recurrent neural networks with finite time learning process in [13]. In [14], an adaptive backstepping control is proposed with the command filtering technique for quadrotor trajectory tracking, where FLS is employed to estimate the uncertainty dynamics in the quadrotor model. By incorporating FLS into control scheme, the adaptive quantized control is considered for trajectory tracking in position loop and attitude loop in [15].…”

“…(i) Unlike the above stating SMO-based controller [6][7][8][9] and controller with function approximators [10][11][12][13][14][15], where chattering and heavy computational burden with adjusting parameters always arise, we adopt the unknown system dynamic estimator (USDE) with an invariant manifold for quadrotor attitude regulation to confirm the uncertainty in quadrotor model. In comparison with the ESObased controller [16,17], due to state in quadrotor calculated with simple filter, a perspicuous framework consisting of USDE with low computation is conducted (ii) A novel USDE-based funnel control is proposed for quadrotor attitude regulation to enable preassigned convergence with existence of estimation error.…”

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

“…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]. Em [29] são destacados vários projetos de melhoria de estabilidade para quadricópteros, que são capazes de realizar voos autônomos apenas com o uso de sensores a bordo para estimação da atitude, altitude, posição horizontal e voos trans-lacionais.…”