A neurobiologically-inspired intelligent trajectory tracking control for unmanned aircraft systems with uncertain system dynamics and disturbance

Jafari, Mohammad; Xu, Hao; Carrillo, Luis Rodolfo García

doi:10.1177/0142331218763007

Cited by 28 publications

(19 citation statements)

References 25 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: 61%

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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: 61%

“…In recent years, intelligent approaches have been extensively utilized for successfully solving diverse complex problems [22][23][24]. Among them, biologically-inspired intelligent approaches have received tremendous interest by many researchers because of their inherent potential to deal with computationally complex systems.…”

Section: Related Workmentioning

confidence: 99%

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

Jafari

2018

Drones

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“…In this regard, the development of control strategies with less dependency on the full knowledge of the system dynamics and with reliable online training phase is essential. In recent years, learning based approaches have been extensively utilized for successfully solving diverse complex problems [17][18][19][20][21]. From a control system point of view, neural network based approaches are effective when the system dynamics are fully or partially unknown.…”

Section: Related Workmentioning

confidence: 99%

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

Jafari

2018

Drones

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Stabilizing the Unmanned Aircraft Systems (UAS) under complex environment including system uncertainties, unknown noise and/or disturbance is so challenging. Therefore, this paper proposes an adaptive neural network based intelligent control method to overcome these challenges. Based on a class of artificial neural network, named Radial Basis Function (RBF) networks an adaptive neural network controller is designed. To handle the unknown dynamics and uncertainties in the system, firstly, we develop a neural network based identifier. Then, a neural network based controller is generated based on both the identified model of the system and the linear or nonlinear controller. To ensure the stability of the system during its online training phase, the linear or nonlinear controller is utilized. The learning capability of the proposed intelligent controller makes it a promising approach to take system uncertainties, noises and/or disturbances into account. The satisfactory performance of the proposed intelligent controller is validated based on the computer based simulation results of a benchmark UAS with system uncertainties and disturbances, such as wind gusts disturbance.

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“…Using an AI-based controller seems promising. Several works using these control techniques can be found in [25][26][27].Quaternion describing dynamics of a quadrotor instead of Euler angles is becoming very popular nowadays. A feedback signal in the form of a quaternion can be used to design linear and also non-linear attitude and position controllers [28,29].In [13], a cascade attitude controller was proposed.…”

mentioning

confidence: 99%

“…Using an AI-based controller seems promising. Several works using these control techniques can be found in [25][26][27].…”

mentioning

confidence: 99%

Control Methods Comparison for the Real Quadrotor on an Innovative Test Stand

et al. 2020

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This article is a continuation of our previously published work that presented a comparison of nine attitude quaternion-based controllers of the quadrotor in simulation environment. In this article, the best three controllers were implemented into the real quadrotor. Namely proportional derivative (PD), linear quadratic regulator (LQR) and backstepping quaternion-based control techniques were evaluated. As a suitable test stand was not available on the basis of literature analysis, the article also outlines the requirements and the development of a new innovative test stand. In order to provide a comprehensive overview, the hardware and software that was used is also presented in the article. The main contribution of this article is a performance comparison of the controllers, which was based on absolute quaternion (positioning) error and energy consumption.The quadrotor is coupled and underactuated system (i.e., six degrees of freedom are controlled only by four actuators). The translational motion of the quadrotor depends on the attitude of the quadrotor as can be seen from the equations in [9]. By this means, the quadrotor is a cascade system [10]. Therefore, a cascade structure of a controller is usually applied to control position and attitude of the quadrotor. Various controllers based on classic or modern control theory were already designed and verified. The non-linear or linear model of a quadrotor is used depending on the chosen control method [11].The linear model of a quadrotor is achieved by linearization of the non-linear model around a hovering point. Controllers using the linearized model generally perform well around this hovering point. When the quadrotor goes away from the linearized point, the performance may worsen. Furthermore, the input saturation can cause control failure when large rapid maneuvers are required [3,5,12,13]. The main advantages of linear controllers are simplicity and ease of implementation to a real platform. The disadvantage of this approach is the use of the linearized model of the quadrotor during the process of designing a controller. Commonly used linear controllers are a linear quadratic regulator (LQR) and a proportional derivative (PD) controller [14][15][16].Another group of controllers consists of a wide variety of non-linear controllers. The serious disadvantage of these controllers is the complexity that prevents the wide adoption of non-linear controllers in real applications. Among non-linear methods, the backstepping control technique based on the Lyapunov function is widely adopted due to its systematic design and an intuitive approach [17]. The proposed control law is based on the compensation of non-linear forces or torques depending on whether an attitude or position controller is being designed. Applying Lyapunov stability analysis proves that the closed-loop system is asymptotically stable. This approach was used to stabilize the quadrotor in [3,5,12,[18][19][20][21].A backstepping-based inverse optimal attitude controller (BIOAC) was derived in [3] ta...

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A neurobiologically-inspired intelligent trajectory tracking control for unmanned aircraft systems with uncertain system dynamics and disturbance

Cited by 28 publications

References 25 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

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

Control Methods Comparison for the Real Quadrotor on an Innovative Test Stand

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