Application and Study of artificial neural network intelligent control

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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Heavy trucks with intelligent control of active suspension based on artificial neural networks

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820958516 ◽

2020 ◽

pp. 095965182095851

Author(s):

Anis Hamza ◽

Noureddine Ben Yahia

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Intelligent Control ◽

Research Work ◽

Active Suspension ◽

Suspension System ◽

International Organization ◽

International Organization For Standardization ◽

Neural Network Controller ◽

Artificial Neural

The active control of a suspension system is meant to provide an isolated behavior of the system spring-mass (for example, increased comfort and performance). During this article, we are going to explain the importance of developing an intelligent control approach for active truck suspensions based on the artificial neural network. From where the main objective of this article is to obtain a mathematical model for active suspension systems then build a hydraulic model for active suspension control for trucks using an artificial neural network. In this article, a corresponding artificial neural network nonlinear active suspension controller has been designed and optimized for approximate road profiles, using simulation according to International Organization for Standardization 2631-5 and International Organization for Standardization 8608 standardizations. The model developed with MATLAB Toolbox, estimated and validated from data collected during tests carried out with a truck in other research work. To model the system, the laws of physics are used to describe the system and experimental data or information supplied about the system to determine the parameters of the system. The statement of the problem of this research is to develop a robust artificial neural network controller for the nonlinear active suspension system of the heavy truck that can improve the performances and its verifications using graphical and simulation output. The results of the simulation show that the methodology offers excellent performance. In addition, the robustness of the artificial neural network hydraulic controller is demonstrated for a variety of road profiles that increase the capabilities of the proposed methodology and prove its effectiveness.

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Intelligent Control of Agricultural Irrigation through Water Demand Prediction Based on Artificial Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/7414949 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Qiuyu Bo ◽

Wuqun Cheng

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Intelligent Control ◽

Water Demand ◽

Accurate Estimation ◽

Agricultural Irrigation ◽

Demand Prediction ◽

Ecological Water Demand ◽

Water Demand Prediction ◽

Artificial Neural

In irrigated areas, the intelligent management and scientific decision-making of agricultural irrigation are premised on the accurate estimation of the ecological water demand for different crops under different spatiotemporal conditions. However, the existing estimation methods are blind, slow, or inaccurate, compared with the index values of the water demand collected in real time from irrigated areas. To solve the problem, this paper innovatively introduces the spatiotemporal features of ecological water demand to the forecast of future water demand by integrating an artificial neural network (ANN) for water demand prediction with the prediction indices of water demand. Firstly, the ecological water demand for agricultural irrigation of crops was calculated, and a radial basis function neural network (RBFNN) was constructed for predicting the water demand of agricultural irrigation. On this basis, an intelligent control strategy was presented for agricultural irrigation based on water demand prediction. The structure of the intelligent control system was fully clarified, and the main program was designed in detail. The proposed model was proved effective through experiments.

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