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

Intelligent Control ◽

Research Work ◽

Active Suspension ◽

Suspension System ◽

International Organization ◽

International Organization For Standardization ◽

Neural Network Controller ◽

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.

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

Drones ◽

10.3390/drones2030030 ◽

2018 ◽

Vol 2 (3) ◽

pp. 30 ◽

Cited By ~ 10

Author(s):

Mohammad Jafari ◽

Hao Xu

Keyword(s):

Neural Network ◽

Intelligent Control ◽

Unmanned Aircraft ◽

Nonlinear Controller ◽

Adaptive Neural Network ◽

Intelligent Controller ◽

Neural Network Controller ◽

System Uncertainties ◽

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.

Adaptive Artificial Neural Network Surrogate Model of Nonlinear Hydraulic Adjustable Damper for Automotive Semi-Active Suspension System

IEEE Access ◽

10.1109/access.2020.3004886 ◽

2020 ◽

Vol 8 ◽

pp. 118673-118686 ◽

Cited By ~ 1

Author(s):

Jingliang Lin ◽

Haiyan Li ◽

Yunbao Huang ◽

Zeying Huang ◽

Zhiqian Luo

Keyword(s):

Neural Network ◽

Surrogate Model ◽

Active Suspension ◽

Suspension System ◽

Application and Study of artificial neural network intelligent control

Journal of Physics Conference Series ◽

10.1088/1742-6596/1948/1/012003 ◽

2021 ◽

Vol 1948 (1) ◽

pp. 012003

Author(s):

Jing Zhao ◽

Bin Zhang

Keyword(s):

Neural Network ◽

Intelligent Control ◽

Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

Measurement and Control ◽

10.1177/0020294020964826 ◽

2020 ◽

pp. 002029402096482

Author(s):

Sulaiman Khan ◽

Abdul Hafeez ◽

Hazrat Ali ◽

Shah Nazir ◽

Anwar Hussain

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Research Work ◽

Recognition Rate ◽

Recognition Task ◽

Convolution Neural Network ◽

Medium Size ◽

Support Vector ◽

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.