Improved radial basis function artificial neural network and exact-time extended state observer based non-singular rapid terminal sliding-mode control of quadrotor system

Abstract

Purpose The purpose of this paper is to design a hybrid robust tracking controller based on an improved radial basis function artificial neural network (IRBFANN) and a novel extended-state observer for a quadrotor system with various model and parametric uncertainties and external disturbances to enhance the resiliency of the control system. Design/methodology/approach An IRBFANN is introduced as an adaptive compensator tool for model and parametric uncertainties in the control algorithm of non-singular rapid terminal sliding-mode control (NRTSMC). An exact-time extended state observer (ETESO) augmented with NRTSMC is designed to estimate the unknown exogenous disturbances and ensure fast states convergence while overcoming the singularity issue. The novelty of this work lies in the online updating of weight parameters of the RBFANN algorithm by using a new idea of incorporating an exponential sliding-mode effect, which makes a remarkable effort to make the control protocol adaptive to uncertain model parameters. A comparison of the proposed scheme with other conventional schemes shows its much better performance in the presence of parametric uncertainties and exogenous disturbances. Findings The investigated control strategy presents a robust adaptive law based on IRBFANN with a fast convergence rate and improved estimation accuracy via a novel ETESO. Practical implications To enhance the safety level and ensure stable flight operations by the quadrotor in the presence of high-order complex disturbances and uncertain environments, it is imperative to devise a robust control law. Originality/value A new idea of incorporating an exponential sliding-mode effect instead of conventional approaches in the algorithm of the RBFANN is used, which makes the control law resistant to model and parametric uncertainties. The ETESO provides rapid and accurate disturbance estimation results and updates the control law to overcome the performance degradation caused by the disturbances. Simulation results depict the effectiveness of the proposed control strategy.

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 Das et al [12], by introducing a neural network-based function approximator, a robust backstepping control was exploited to circumvent the unknown perturbations. In Ullah et al [13], a radial basis function neural network-based robust adaptive control was introduced to eliminate the performance degradation caused by the unknown system dynamics. With the aid of the above observers, excellent disturbance identification can be ensured and the robustness can be improved.…”

“…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. Notably, by filtering the available system states and introducing the invariant manifold theory, the unknown system dynamics estimator (USDE) is designed to identify the unknown disturbances.…”

“…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.…”

“…Distinguishing it from the existing sliding mode observer [5][6][7][8]-based control strategies, where chattering cannot be efficaciously surmounted by raising the deployment of the sign function, the unknown system dynamics estimator (USDE) is presented for quadrotor attitude dynamics to identify the unmeasurable perturbations by utilizing an invariant manifold principle. In addition, differing from previous function approximator-based control schemes [12][13][14], by using simple filtering calculations for the available quadrotor states, a more concise design frame and a lower calculation burden could be guaranteed.…”