Neural Network-based Robust Adaptive Certainty Equivalent Controller for Quadrotor UAV with Unknown Disturbances

Doukhi, Oualid; Lee, Deok Jin

doi:10.1007/s12555-018-0720-7

Cited by 44 publications

(26 citation statements)

References 10 publications

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“…The ideal of the control objective is to guarantee a great position tracking performance under external disturbance, model uncertainty, actuator fault, and position sensor fault. Inspired by the position sensor fault detection, the FDI and the FTC are designed to achieve the control objective for the plant (8).…”

Section: Ap J Fmentioning

confidence: 99%

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Robust Fault-Tolerant Control of an Electro-Hydraulic Actuator With a Novel Nonlinear Unknown Input Observer

Phan

Dao

et al. 2021

IEEE Access

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In this paper, a novel adaptive fault-tolerant controller is proposed for a typical electrohydraulic rotary actuator in the presence of disturbances, internal leakage fault, and sensor fault simultaneously. To construct the suggested controller, a nonlinear unknown input observer is developed to effectively identify the sensor fault, which is unaffected by not only internal leakage fault but also mismatched disturbances/uncertainties. Furthermore, a radial basis function neural network is designed to compensate for the mismatched disturbances/uncertainties caused by payload variation and unknown friction nonlinearities. Besides, an adaptive law based on the projection mapping function is applied to tackle the effect of the internal leakage fault. The integration of the above-mentioned techniques into the adaptive backstepping terminal sliding mode is investigated to obtain high tracking performance, robustness as well as fast convergence. The stability of the closed-loop system is proven by the Lyapunov theory. Finally, the capability and effectiveness of the proposed approach are validated via simulation results under various faulty scenarios.

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Section: Ap J Fmentioning

confidence: 99%

“…In the third study, the internal leakage is simultaneously considered together with the position sensor fault and mismatched disturbance to test the performances of comparative controllers. To simulate the internal leakage fault, the following slow-varying faulty coefficient is selected as ( 8) 0…”

Section: ) Case Studymentioning

confidence: 99%

Robust Fault-Tolerant Control of an Electro-Hydraulic Actuator With a Novel Nonlinear Unknown Input Observer

Phan

Dao

et al. 2021

IEEE Access

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“…There are several control techniques for the attitude and altitude of the quadcopter such as proportional-integralderivative (PID) control [1,2], adaptive control [3][4][5], neural network [6,7], LQR control [8,9], model predictive control [10,11], have been investigated in many studies. Comparison with other approaches, sliding mode control (SMC) [12,13] is exploited as a special and robust control algorithm against parametric uncertainties, external disturbances through its sliding surface.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

et al. 2021

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In this paper, a sliding mode control based on neural networks is proposed for attitude and altitude system of quadcopter under external disturbances. First, the dynamic model of the quadcopter is considered under external disturbances. Sliding mode controllers are then integrated with neural network algorithm to achieve the time-varying sliding surface; their coefficients in sliding surface are adjusted through backpropagation law. The disturbance observer is also combined with sliding mode controllers to estimate and handle the external disturbances. Finally, the Lyapunov theory is applied to validate the stability of suggested control method. The performance of proposed sliding mode control has been evaluated using a numerical simulation. The results show that the attitude and altitude controller based on suggested algorithm has a better tracking performance and disturbance rejection.

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“…For instance, in [16] an adaptive controller applied to attitude tracking is presented for an unmanned aerial vehicle to eliminate the adverse effect of measurement noises. Even in [17], a robust adaptive neural network controller is proposed, and is applied to a quadrotor UAV. This control law is not necessary for the prior information of disturbances to stabilize the quadrotor.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking with Adaptive Robust Control for Quadrotor

et al. 2021

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This work proposes three robust mechanisms based on the MIT rule and the sliding-mode techniques. These robust mechanisms have to tune the gains of an adaptive Proportional-Derivative controller to steer a quadrotor in a predefined trajectory. The adaptive structure is a model reference adaptive control (MRAC). The robust mechanisms proposed to achieve the control objective (trajectory tracking) are MIT rule, MIT rule with sliding mode (MIT-SM), MIT rule with twisting (MIT-Twisting), and MIT rule with high order sliding mode (MIT-HOSM).

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Neural Network-based Robust Adaptive Certainty Equivalent Controller for Quadrotor UAV with Unknown Disturbances

Cited by 44 publications

References 10 publications

Robust Fault-Tolerant Control of an Electro-Hydraulic Actuator With a Novel Nonlinear Unknown Input Observer

Robust Fault-Tolerant Control of an Electro-Hydraulic Actuator With a Novel Nonlinear Unknown Input Observer

Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

Trajectory Tracking with Adaptive Robust Control for Quadrotor

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