Adaptive Position/Attitude Tracking Control of Aerial Robot With Unknown Inertial Matrix Based on a New Robust Neural Identifier

Lai, Guanyu; Liu, Zhi; Zhang, Yun; Chen, C. L. Philip

doi:10.1109/tnnls.2015.2406812

Cited by 57 publications

(19 citation statements)

References 55 publications

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“…This section highlights discusses, compares, summarizes and critiques more than eighty research articles on artificial neural network model's application to the diverse area of the economy. The comparison was made based on (i) author(s)/year of publication (ii) ANN modeling (iii) ANN area of application (iii) studied contribution to human challenges (with references to Supplementary Table 2) [ 11 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , …”

Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,160

785

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This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

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Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,160

785

View full text Add to dashboard Cite

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“…See Appendix A for detailed definitions and properties of projection operator. Combining (20) with (21), an auxiliary system is established as [35]:…”

Section: Nominal Neural Network Designmentioning

confidence: 99%

“…In [19], NNs are introduced to learn the uncertainties online and control a small quadrotor. In [20], a NNs based robust adaptive controller is proposed to identify the inertia matrix of a small helicopter. In [21], NNs are introduced to enhance the performance of a dynamic inversion control scheme.…”

Section: Introductionmentioning

confidence: 99%

Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability

Cheng

Pei

2020

IEEE Access

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In this paper, we focus on the transition control of a ducted fan vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV). To achieve a steady transition from hover to high-speed flight, a neural-networks-based controller is proposed to learn the system dynamics and compensate for the tracking error between the aircraft dynamics and the desired dynamic performance. In prior, we derive the nonlinear system model of the aircraft full-envelope dynamics. Then, we propose a novel neural-networks-based control scheme and apply it on the underactuated aircraft system. Key features of the proposed controller consist of projection operator, state predictor and dynamic-formed adaptive input. It is proved and guaranteed that the tracking errors of both state predictor and neural-networks weights are upper bounded during the whole neural-networks learning procedure. The very adaptive input is formed into a dynamic structure that helps achieve a reliable fast convergence performance of the proposed controller, especially in highfrequency disturbance conditions. Consequently, the closed-loop system of the aircraft is able to track a certain trajectory with desired dynamic performance. Satisfactory results are obtained from both simulations and practical flight test in accomplishing the designed flight course. INDEX TERMS Ducted fan, fast convergence, high-speed flight, neural networks, transition control, unmanned aerial vehicle (UAV).

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“…By combining the definition of Dx i =x i À x i with Property 3 and equation (20), the last term in equation (28) can be rewritten as…”

Section: Stability Analysismentioning

confidence: 99%

Adaptive tracking control of a nonlinear teleoperation system with uncertainties in kinematics and dynamics

Yang

Chen

Zhao

2019

Advances in Mechanical Engineering

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A new adaptive tracking control problem of a nonlinear teleoperation system is considered in this article, which differs from the previous ones in that both kinematics and dynamics of manipulators are uncertain and perturbation of the actuator parameters exists due to overheating of the actuator. To overcome the first which is also the key problem, we propose a new adaptive tracking control framework which additionally contains a neural network mechanism and a robust adaptive mechanism. Based on the framework, an adaptive controller is further established to compensate for the perturbation of the actuator parameters online. By employing the neural networks and adaption method in control design, an extended teleoperation control system is effectively accommodated. With the Lyapunov theory, the tracking error is shown to converge to a residual around zero as the time goes to infinity. The effectiveness of the proposed scheme has been verified in the physical platform.

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Adaptive Position/Attitude Tracking Control of Aerial Robot With Unknown Inertial Matrix Based on a New Robust Neural Identifier

Cited by 57 publications

References 55 publications

State-of-the-art in artificial neural network applications: A survey

State-of-the-art in artificial neural network applications: A survey

Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability

Adaptive tracking control of a nonlinear teleoperation system with uncertainties in kinematics and dynamics

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