Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy, and intelligence. In this article, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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UAV Swarm Intelligence: Recent Advances and Future Trends

Fuzzy logic was first developed by Zadeh in the mid 1960s for representing uncertain and imprecise knowledge. In the classical Boolean logic truth is represented by the 1 state and falsity is by the 0 state. Boolean algebra has no provision for approximate reasoning. Fuzzy logic is an extension of Boolean logic in the sense that it also provides a platform for handling uncertain and imprecise knowledge. Fuzzy logic uses fuzzy set theory, in which a variable is a member of one or more sets, with a specified degree of membership, usually denoted by the Greek letter /spl mu/. The paper provides an introduction to fuzzy systems.<<ETX>>

Introduction to fuzzy systems.

This paper reviews the current development of artificial intelligence (AI) techniques for the application area of robot communication. The research of the control and operation of multiple robots collaboratively toward a common goal is fast growing. Communication among members of a robot team and even including humans is becoming essential in many real-world applications. The survey focuses on the AI techniques for robot communication to enhance the communication capability of the multi-robot team, making more complex activities, taking an appreciated decision, taking coordinated action, and performing their tasks efficiently. We present a comprehensive review of the intelligent solutions for robot communication which have been proposed in the literature in recent years. This survey contributes to a better understanding of the AI techniques for enhancing robot communication and sheds new lights on future research direction in the subject area.

Survey on artificial intelligence based techniques for emerging robotic communication

Fuzzy swarm trajectory tracking control of unmanned aerial vehicle

A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple “if-then” relations owing the designer to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).

/pdf/neuro-fuzzy-controller-to-navigate-an-unmanned-vehicle-2py8n54qk8.pdf

Neuro-fuzzy controller to navigate an unmanned vehicle

This paper, deals with a path planning and intelligent control of an autonomous vehicle which should move safely in its road partially structured. This road, involves a number of obstacles like donkey, traffic lights and other vehicles. In this paper, the Neural Networks (NN)-based technique Artificial Neural Network (ANN) is described to solve the motion-planning problem in Unmanned Vehicle (UV) control. This is accomplished by choosing the appropriate inputs/outputs and by carefully training the ANN. The network is supplied with distances of the closest obstacles around the vehicle to imitate what a human driver would see. The output is the acceleration and steering of the vehicle. The network has been trained with a set of strategic input-output. The results show the effectiveness of the technique used, the UV drives around avoiding obstacles.

Unmanned vehicle trajectory tracking by neural networks.

Accurate and precise trajectory tracking is crucial for unmanned aerial vehicle (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) controller and Improved Particle Swarm Optimization algorithm (IPSO) model based on functional inertia weight. The controller is implemented in a three degrees of freedom (3 DOF) quadrotor symbolized with its non-linear dynamical mathematical model. To achieve Cartesian position trajectory tracking capability, the construction of the controller can be divided into two stages: a regular ANFIS controller to guarantee fast convergence rapidity and IPSO aims to facilitate convergence to the ANFIS’s optimal parameters to accurately reproduce a desired reference trajectory. Simulation results are given to confirm the advantages of the proposed intelligent control, compared with ANFIS and PID control methods.

Optimal trajectory tracking control of unmanned aerial vehicle using ANFIS-IPSO system

Development of unmanned aerial vehicles (UAVs) has become the most important research areas in the field of autonomous aeronautical control. This paper proposes a robust and intelligent controller based on adaptive-network-based fuzzy inference system (ANFIS) and improved ant colony optimization (IACO) to govern the behavior of a three degree of freedom quadrotor UAV. The quadrotor was chosen due to its simple mechanical structure; nevertheless, these types of aircraft are highly nonlinear. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the desired trajectory of the quadrotor in 2D Vertical plane and the IACO algorithm aims is to facilitate convergence to the ANFIS’s optimal parameters in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed IACO tuned ANFIS controller, a comparison between the proposed ANFIS-IACO controller and other controller’s performance such us ANFIS only and proportional–integral–derivative controllers is illustrated using the same system. As expected, the hybrid ANFIS-IACO controller gives very satisfactory results than the others methods already developed in the same study.

https://link.springer.com/content/pdf/10.1007/s42452-020-2236-z.pdf

Boumediene Selma

Papers

Fuzzy swarm trajectory tracking control of unmanned aerial vehicle

Neuro-fuzzy controller to navigate an unmanned vehicle

Unmanned vehicle trajectory tracking by neural networks.

Optimal trajectory tracking control of unmanned aerial vehicle using ANFIS-IPSO system

Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task