Performance Examinations of Quadrotor with Sliding Mode Control-Neural Network on Various Trajectory and Conditions

Abstract

In this article, the performance of the sliding mode control (SMC) that is combined with the backpropagation neural network (NN) as the main control of quadrotor’s dynamic systems was examined on various trajectories and conditions, through numerical simulation. The simulation is conducted with three different trajectories in the absence and presence of the time-varying external disturbances that were adopted from previous studies. The time-varying external disturbances are implemented for the roll, pitch, yaw, and altitude movement simultaneously with the gain set up at the value of 0.8. The simulation results show that the SMC-NN scheme was able to control the quadrotor either in the absence or in the presence of time-varying external disturbances, for each trajectory without any chattering or vibration issues in the quadrotor’s dynamic system. It can be concluded that the SMC-NN is one of the control strategies that are appropriate for the mission with various conditions and circumstances.

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

“…Zhao and Jin [30] proposed a SMC algorithm based on Radial Basis Function Neural Network (RBFNN) to eliminate the effects of model uncertainties and exogenous disturbances on the path tracking controller of agricultural quadcopter with variable payload, and verified the efficacy and more accurate path tracking performance of the proposed controller by comparing it experimentally with other classic SMC methods. Darwito and Wahyuadnyana [31] combined SMC with Back Propagation Neural Network (BPNN) for trajectory tracking of quadcopters and simulated its effect, their results proved that the proposed scheme could control the quadcopter in absence and presence of time-varying external disturbances. Zare et al [32] integrated SMC with fuzzy logic based on Lyapunov function and optimized it using an intelligent fuzzy-genetic algorithm and applied it to quadcopter slung load position and attitude control, the proposed method exhibited good stability, robustness, and tracking performance in case of transient and steady states with external disturbances, and effectively reduced the chattering phenomenon.…”

“…(3) The proposed ANFIS-SMC scheme of quadcopter controller was compared with the SMC method proposed in [31] and its effectiveness and superiority were verified via simulation results.…”

“…The control scheme is illustrated in Figure 2. The control strategy adopted in this paper was a SMC method based on the backstepping approach [31,39]. Let the 𝑥 𝑑 , 𝑦 𝑑 , 𝑧 𝑑 , and 𝜙 𝑑 , 𝜃 𝑑 , 𝜓 𝑑 respectively represent the desired position of quadcopter in 𝑥, 𝑦, 𝑧 directions and the desired attitude of quadcopter in 𝜙, 𝜃, 𝜓, directions; then the tracking error 𝑒 𝑖 , 𝑖 (𝑖 = 𝑥, 𝑦, 𝑧, 𝜙, 𝜃, 𝜓), can be defined as:…”

Optimized Adaptive PID Controller Design for Trajectory Tracking of a Quadcopter

“…To control a quadcopter a new controller based on emotional learning was proposed a novel bidirectional algorithm for emotional learning is used in conjunction with an easier fuzzy neural network within the confines of this feature technique, the outcomes showed that the suggested approach performed admirably, as shown by the findings [10]. While in reference [11], sliding mode control using neural network backpropagation using three alternative paths and numerous circumstances with and without external disturbances was examined. Simulated results using MATLAB prove the SMC-NN control system's tracking performance and capacity to reach the target trajectory in the face of external disturbances.…”

Fractional Order Sliding Mode Controller for HBV Epidemic System

“…The robustness is an attractive property of the SMC method. However, as noted in the literature [35][36][37], the fundamental problem of the SMC approach is that it causes chattering, or high frequency in the control input signal. To overcome the disadvantage, fractional order sliding mode control (FOSMC) is proposed as one of the enhanced versions of the SMC method.…”