Adaptive sliding mode-based active disturbance rejection control for a quadcopter

Abstract

This paper presents an adaptive sliding mode-based active disturbance rejection control (ASM-ADRC) strategy for the altitude and attitude control of an unmanned quadcopter with disturbances. The quadcopter is an underactuated system subject to parametric perturbations, nonlinearity, unmodeled dynamics, strong coupling, and external disturbances. The proposed algorithm is based on the quadcopter’s dynamic model, where the effects of noise and wind are considered additive disturbances. The central concept is to combine the advantages of adaptive sliding mode control (SMC) to accurately track the reference trajectory with the ability of ADRC to reject the parameter uncertainties and external disturbances. The proposed control scheme is verified in simulation studies with sensor noise and external disturbances. The simulation results show that the proposed algorithm can significantly reduce the chattering phenomenon, owing to the estimation capability of the extended state observer (ESO). The proposed method also improves the robustness against modeling errors and disturbances and smoothly tracks the reference trajectory.

Intelligent Quadcopter Control Using Artificial Neural Networks

“…The PID controller is used to control the attitude of the quadcopter while the reinforcement learning-based adaptive controller is used to control the altitude in (Barzegar et al 2022). Adaptive sliding mode control is used to actively reject disturbances in the attitude and altitude control of quadcopters by (Suhail et al 2022). El Gmili et al (2022 used an optimal PD controller to control the orientation and position of quadcopters in their study.…”

Unknown System Dynamics Estimator-Based Anti-Disturbance Attitude Funnel Control for Quadrotors with Experimental Verifications

“…In order to recover the unknown perturbations and acquire an enhanced robustness, a great deal of advanced disturbance observers have been exploited for quadrotors by compensating for the negative impact in a feedforward manner, i.e., sliding mode observers (SMO) [5][6][7][8], extended state observers (ESO) [9][10][11], and function approximators [12][13][14]. To reinforce the tracking performance for quadrotors, a fixed-time-based SMO control rule was suggested in Zhou et al [5].…”

“…In Cui et al [9], with the aid of the estimated results of ESO, a super-twisting-based adaptive control method was designed for quadrotors to govern the trajectory tracking behaviors. In Suhail et al [10], an ESObased adaptive sliding mode control was exploited to realize the robustness of the attitude tracking response. In Das et al [12], by introducing a neural network-based function approximator, a robust backstepping control was exploited to circumvent the unknown perturbations.…”

“…However, certain defects including the unexpected chattering phenomenon in [5][6][7][8] and complex argument selections impose restrictions on engineering practices to a certain extent. In addition, it is generally difficult to make a compromise between the peak phenomenon and the convergence speed of estimation errors by using ESO [9][10][11]. On the other hand, the tedious iteration calculations in [12][13][14] caused by weight updating inevitably increase the computational burden and deteriorate control response.…”

“…In Na et al [16], an USDE-based motion control was proposed for robotic systems to address the internal unknown dynamics and environmental disturbances. Compared to the aforementioned disturbance observers [5][6][7][8][9][10][11][12][13][14], the USDE possesses a convenient parameter tuning rule and a satisfied estimation accuracy; however, the existing USDE-based outcomes are oriented towards simple integral-chain systems, which cannot be directly applied to the quadrator attitude with obvious coupling and multiple variables, yielding to a more complicated controller development and stability analysis. Additionally, the key metrics, i.e., overshoot, convergence time and steady-state errors, are not actively considered during the above control developments.…”