Intelligent Control for Unmanned Aerial Systems with System Uncertainties and Disturbances Using Artificial Neural Network

Jafari, Mohammad; Xu, Hao

doi:10.3390/drones2030030

Cited by 37 publications

(18 citation statements)

References 19 publications

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“…Since our analysis is focused on developing an intelligent distributed controller for flocking of networked multi-UAS, in this paper, the double integrator dynamics is adopted. Considering the fact that the double integrator dynamics is a very reduced dynamics of the quad rotorcrafts, one can extend our results by employing model-free inner-loop controllers in [23,30], etc.…”

Section: Remarkmentioning

confidence: 60%

Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

Jafari

2018

Drones

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In this paper, a biologically-inspired distributed intelligent control methodology is proposed to overcome the challenges, i.e., networked imperfections and uncertainty from the environment and system, in networked multi-Unmanned Aircraft Systems (UAS) flocking. The proposed method is adopted based on the emotional learning phenomenon in the mammalian limbic system, considering the limited computational ability in the practical onboard controller. The learning capability and low computational complexity of the proposed technique make it a propitious tool for implementing in real-time networked multi-UAS flocking considering the network imperfection and uncertainty from environment and system. Computer-aid numerical results of the implementation of the proposed methodology demonstrate the effectiveness of this algorithm for distributed intelligent flocking control of networked multi-UAS.

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Section: Remarkmentioning

confidence: 60%

Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

Jafari

2018

Drones

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“…Fortunately, neural networks (NN) have been used in controller design for their excellent ability to approximate arbitrary unknown nonlinearities [21], including the unmodeled dynamics and random disturbances. Therefore, NN controllers have been designed for high nonlinear pneumatic systems to obtain better model compensation [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

2019

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In this paper, an adaptive robust neural network controller (ARNNC) is synthesized for a single-rod pneumatic actuator to achieve high tracking accuracy without knowing the bounds of the parameters and disturbances. The ARNNC control framework integrates adaptive control, robust control, and neural network control intelligently. Adaptive control improves the precision of dynamic compensation with parametric estimation, and robust control attenuates the effect of unmodeled dynamics and unknown disturbances. In reality, the unmodeled dynamics of the complicated pneumatic systems and unpredictable disturbances in working conditions affect the tracking precision. However, these cannot be expressed as an exact formula. Therefore, the real-time learning radial basis function (RBF) neural network component is considered for better compensation of unmodeled dynamics, random disturbances, and estimation errors of the adaptive control. Although the bounds of the parameters and disturbances for the pneumatic systems are unknown, the prescribed transient performance and final tracking accuracy of the proposed method can be still achieved with fictitious bounds. Asymptotic tracking performance can be acquired under the provided circumstance. The comparative experiments with a pneumatic cylinder driven by proportional direction valve illustrate the effectiveness of the proposed ARNNC as shown by a high tracking accuracy is achieved.

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“…16 Ren et al 17 presented in their study a command filter-based back-stepping controller for robust height and attitude tracking for small unmanned helicopter with flapping dynamics under uncertainties and external disturbances. Jafari and Xu 18 proposed a research that consists of the design of an adaptive intelligent control strategy, which is effective for the real-time autonomous flight of a UAV, even under the system uncertainties and disturbances. Finally, Kim et al 19 presented a robust optimization approach to find the optimal flight schedule in the flight network considering uncertain battery duration.…”

Section: Introductionmentioning

confidence: 99%

Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty

Аl-Мashhadani

2019

Measurement and Control

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In the past decade, many approaches that attempted to solve the problem of optimal control and parameter estimation of an unmanned aerial vehicle with a priori uncertain parameters simply implied two ways to solve such problem. First, by the formation of optimal control based on a refined mathematical model of the unmanned aerial vehicle, and second, by using the estimation and identification methods of the model parameter of the unmanned aerial vehicle based on measured data from flight tests. However, the identification of the dynamic parameters of the unmanned aerial vehicle is a complicated task because of a number of factors such as random vibration noise, disturbance, and uncertainty of the sensor measurements. Due to the influence of random vibration noise, the problem of correlated vibration noises and uncertainty has encountered inevitably, and the accuracy of the state estimation for unmanned aerial vehicle is degraded. This study concentrates on the optimal control and state estimation for the unmanned aerial vehicle under the combination of both random vibration noise and uncertainty collected by the sensors. The effects of random vibrations at various stages of a large-scale flight that are a priori uncertain require the inclusion of identification algorithms in the optimal control loop. The results showed that the method used in the analysis had been able to provide accurate estimations.

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Intelligent Control for Unmanned Aerial Systems with System Uncertainties and Disturbances Using Artificial Neural Network

Cited by 37 publications

References 19 publications

Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty

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