scholarly journals Improving Sustainable Safe Transport via Automated Vehicle Control with Closed-Loop Matching

2021 ◽  
Vol 13 (20) ◽  
pp. 11264
Author(s):  
Tamás Hegedűs ◽  
Dániel Fényes ◽  
Balázs Németh ◽  
Péter Gáspár
Keyword(s):  
Neural Network ◽  
Vehicle Dynamics ◽  
Closed Loop ◽  
Path Following ◽  
Evaluation Process ◽  
Vehicle Control ◽  
Transport Systems ◽  
Control Input ◽  
The Neural Network ◽  
Vehicle Automation

The concept of vehicle automation is a promising approach to achieve sustainable transport systems, especially in an urban context. Automation requires the integration of learning-based approaches and methods in control theory. Through the integration, a high amount of information in automation can be incorporated. Thus, a sustainable operation, i.e., energy-efficient and safe motion with automated vehicles, can be achieved. Despite the advantages of integration with learning-based approaches, enhanced vehicle automation poses crucial safety challenges. In this paper, a novel closed-loop matching method for control-oriented purposes in the context of vehicle control systems is presented. The goal of the method is to match the nonlinear vehicle dynamics to the dynamics of a linear system in a predefined structure; thus, a control-oriented model is obtained. The matching is achieved by an additional control input from a neural network, which is designed based on the input–output signals of the nonlinear vehicle system. In this paper, the process of closed-loop matching, i.e., the dataset generation, the training, and the evaluation of the neural network, is proposed. The evaluation process of the neural network through data-driven reachability analysis and statistical performance analysis methods is carried out. The proposed method is applied to achieve the path following functionality, in which the nonlinearities of the lateral vehicle dynamics are handled. The effectiveness of the closed-loop matching and the designed control functionality through high fidelity CarMaker simulations is illustrated.

Neural Network Controlled High-Step Up Based Single-Phase PV Inverter System with Improved Dynamic Response

2017 ◽  
Vol 14 (1) ◽  
pp. 421-429
Author(s):  
M. L Bharathi ◽  
D Kirubakaran
Keyword(s):  
Neural Network ◽  
Dynamic Response ◽  
Single Phase ◽  
Closed Loop ◽  
Simulation Studies ◽  
Loop Control ◽  
Pv Inverter ◽  
Simulink Model ◽  
Neural Network Controller ◽  
The Neural Network

In AC utility closed loop high step up based single phase PV inverter system is preferred because of fast dynamic response. This paper deals with comparison of dynamic response of the PI and the neural network controlled single phase PV inverter systems. The DC output from the solar cell is stepped up using a high step up converter and the output is converted into AC using an inverter. A change in the insolation is considered in the present investigation. Simulation studies are conducted using MATLAB. The closed loop control systems using the PI controller and the neural network controller are investigated and their results are compared. The Simulink model for the above system are developed and they are successfully used for simulation studies. The hardware is fabricated, tested and the practical results matches with the simulation results.

Study on Intelligent Self-Adaptive Control System

2014 ◽  
Vol 539 ◽  
pp. 620-624
Author(s):  
Ze Min Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control System ◽  
Pid Controller ◽  
Closed Loop ◽  
Rapid Development ◽  
Adaptive Control System ◽  
Positive Role ◽  
The Neural Network ◽  
Control Of Parameters

With the rapid development of China's industry, the use of the control system has become more and more extensive. However, with the complicating of the production system, the traditional control system has been unable to meet the needs of the current industry. Effectively bring the genetic algorithm of the neural network into the control system can solve this problem. Here, it firstly describes the neural network, genetic algorithm principle, operation procedures and the characteristics; secondly, analyzes the principle and lack of conventional PID controller; finally, effectively combines genetic algorithm and controller together, forming a closed loop, strengthening the control of parameters, and giving a code description of the genetic algorithm. This paper plays a certain positive role for industrial engineers and programmers.

scholarly journals Neural-Network-Based Collaborative Control for Continuous Unknown Nonlinear Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Siyu Gao ◽  
Xin Wang
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Cooperative Learning ◽  
Cooperative Control ◽  
Closed Loop ◽  
Collaborative Control ◽  
The Neural Network ◽  
Control Scheme ◽  
Optimal Value ◽  
Trained Neural Network

This paper proposes an NN-based cooperative control scheme for a type of continuous nonlinear system. The model studied in this paper is designed as an interconnection topology, and the main consideration is the connection mode of the undirected graph. In order to ensure the online sharing of learning knowledge, this paper proposes a novel weight update scheme. In the proposed update scheme, the weights of the neural network are discrete, and these discrete weights can gradually approach the optimal value through cooperative learning, thereby realizing the control of the unknown nonlinear system. Through the trained neural network, it is proved if the interconnection topology is undirected and connected, the state of the unknown nonlinear system can converge to the target trajectory after a finite time, and the error of the system can converge to a small neighbourhood around the origin. It is also guaranteed that all closed-loop signals in the system are bounded. A simulation example is provided to more intuitively prove the effectiveness of the proposed distributed cooperative learning control scheme at the end of the article.

scholarly journals Neural Network Controller Application on a Visual based Object Tracking and Following Robot

2019 ◽  
Vol 8 (1) ◽  
pp. 31-40
Author(s):  
Pola Risma ◽  
Tresna Dewi ◽  
Yurni Oktarina ◽  
Yudi Wijanarko
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Object Tracking ◽  
Effective Control ◽  
Computational Time ◽  
Control Input ◽  
Distance Information ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

Navigation is the main issue for autonomous mobile robot due to its mobility in an unstructured environment. The autonomous object tracking and following robot has been applied in many places such as transport robot in industry and hospital, and as an entertainment robot. This kind of image processing based navigation requires more resources for computational time, however microcontroller currently applied to a robot has limited memory. Therefore, effective image processing from a vision sensor and obstacle avoidances from distance sensors need to be processed efficiently. The application of neural network can be an alternative to get a faster trajectory generation. This paper proposes a simple image processing and combines image processing result with distance information to the obstacles from distance sensors. The combination is conducted by the neural network to get the effective control input for robot motion in navigating through its assigned environment. The robot is deployed in three different environmental setting to show the effectiveness of the proposed method. The experimental results show that the robot can navigate itself effectively within reasonable time periods.

A Neural Network-Based Closed Loop Identification of a Magnetic Bearings System

2004 ◽  
Author(s):  
Jose´ Medina ◽  
Mo´nica Parada ◽  
Victor Guzma´n ◽  
Luis Medina ◽  
Sergio Di´az
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
Magnetic Bearing ◽  
External Input ◽  
Open Loop ◽  
Active Magnetic Bearing ◽  
The Neural Network ◽  
Closed Loop Identification ◽  
Artificial Neural Network Ann ◽  
Amb System

This paper deals with the identification of a radial-type active magnetic bearing (AMB) system using Artificial Neural Network (ANN). Identification and validation experiments are performed on a laboratory magnetic bearing system. Since the electromechanical configuration is inherently unstable, the identification data is gathered while the AMB is operating in closed loop with a controller in the loop. From this data, the identification procedure generates an open-loop plant model. A NNARX (Neural network autoregressive external input model) structure is proposed and evaluated for emulating the system’s dynamic. The model is implemented by a Neural network, constructed using a multilayer perceptron (MLP) topology, and trained using as inputs the rotor’s displacements and excitation currents. Validation tests are performed under perturbation conditions (impact applied on the rotor). Results show that the neural network based model presented here is a powerful tool for dynamic plant’s identification, and that it could be also suitable for robust control application.

scholarly journals Direct adaptive control using feedforward neural networks

2003 ◽  
Vol 14 (4) ◽  
pp. 348-358 ◽  
Author(s):  
Daniel Oliveira Cajueiro ◽  
Elder Moreira Hemerly
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Feedforward Neural Networks ◽  
Control Input ◽  
Neural Adaptive Control ◽  
Direct Adaptive Control ◽  
Network Training ◽  
The Neural Network ◽  
Lyapunov’S Second Method ◽  
Kalman Filter Algorithm

This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the convergence of the identification error is investigated by Lyapunov's second method. The performance of the proposed scheme is evaluated via simulations and a real time application.

scholarly journals Optimal Discrete-Time Sliding-Mode Control Based on Recurrent Neural Network: A Singular Value Approach

2021 ◽  
Author(s):  
Hamid Toshani ◽  
Mohammad Farrokhi
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Discrete Time ◽  
Closed Loop ◽  
Sliding Mode ◽  
Singular Value ◽  
Control Signal ◽  
Reaching Law ◽  
Mode Control ◽  
The Neural Network

Abstract In this paper, a strategy involving the combination of optimal discrete-time sliding-mode control and recurrent neural networks is proposed for a class of uncertain discrete-time linear systems. First, a performance index based on the reaching law and the control signal is defined. Then, the constrained quadratic programming problem is formulated considering the limitations on the control signal as the static constraint. The dynamic and algebraic model of the neural network is derived based on the optimization conditions of the quadratic problem and their relationship with the projection theory. The proposed method prevents the chattering by selecting proper parameters of the twisting reaching law. The convergence of the neural network is analysed using the Lyapunov stability theory. A singular-value-based analysis is employed for robustness of the proposed method. The stability conditions of the discrete-time closed-loop system is analysed by studying eigenvalues of the closed-loop matrix using the singular value approach. The performance of the proposed algorithm is assessed in simulated example in terms of chattering elimination, solution feasibility, and encountering uncertainties and is compared with the recently proposed DSMC methods in the literature.

Fault-tolerant control of rotor/magnetic bearing systems using reconfigurable control with built-in fault detection

2000 ◽  
Vol 214 (12) ◽  
pp. 1445-1465 ◽  
Author(s):  
M O T Cole ◽  
P S Keogh ◽  
C R Burrows
Keyword(s):  
Neural Network ◽  
Control Systems ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Industrial Applications ◽  
Magnetic Bearing ◽  
Control Input ◽  
Sensor Failure ◽  
The Neural Network ◽  
And Performance

Magnetic bearings now exist in a variety of industrial applications. However, there are still concerns over the control integrity of rotor/magnetic bearing systems and the ability of control systems to cope with possible faults that can occur during operation. Unless control systems can be developed that have the ability to maintain safe operation when the system is in a degraded or faulty state, then many, otherwise viable, magnetic bearing applications will remain unfulfilled. In this paper, a method is proposed for the design of a fault-tolerant control system that can detect and identify both incipient and sudden faults as and when they occur. A multivariable H∞ controller is reconfigured on occurrence of a fault so that stability and performance is maintained. A neural network is trained to identify faults associated with the system position transducer measurements so that the output from the neural network can be used as the decision tool for reconfiguring control. In this way, satisfactory control of the system can be maintained during failure of a control input. The method requires no knowledge of the system dynamics or system disturbances, and the network can be trained on-line. The validity of this method is demonstrated experimentally for various modes of sensor failure.

A High Precision Positioning Control Method Based on Learning Algorithm with Reference Model

2011 ◽  
Vol 211-212 ◽  
pp. 671-675
Author(s):  
Kenji Nakajima ◽  
Hiroyuki Saitou ◽  
Seiji Hashimoto
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Control Method ◽  
Reference Model ◽  
Learning Algorithm ◽  
Input Saturation ◽  
Control Input ◽  
Precision Positioning ◽  
Positioning Control ◽  
The Neural Network

In this paper, a high precision positioning control method based on the learning algorithm with the reference model is proposed. The reference model is composed of a plant model and a feedback controller. In the proposed control method, disturbance, modeling error and nonlinear characteristic can be effectively compensated by the neural network-based controller, which learns the reference model. Moreover, the control-input saturation problem due to the over-learning for the neural network can be avoided. The effectiveness of the proposed control method is experimentally verified using the precision positioning equipment with nonlinear friction characteristics.

scholarly journals FlexRay Vehicle Network Predictive Control Based on Neural Network

2018 ◽  
Vol 232 ◽  
pp. 01042 ◽  
Author(s):  
Li Huan ◽  
Li Chao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Network Model ◽  
Predictive Control ◽  
Network Control ◽  
Learning Ability ◽  
Control Input ◽  
Network Control System ◽  
The Neural Network ◽  
Vehicle Network

We propose a design method of FlexRay vehicle network forecasting control based on the neural network to solve the security and reliability of FlexRay network control system, where the control performance and stability of the system are reduced when transmiting data under heavy load, by sampling the working state of the vehicle network at the present time to predict the next-time network state, and adapting to the dynamic load in the vehicular network system by on-line adaptive workload adjustment. The method used the nonlinear neural network model to predict the performance of the future model. The controller calculated the control input and optimized the performance of the next-time network model. The simulation results from the Matlab/Simulink showed that the neural network predictive control had good learning ability and adaptability. It could improve the performance of FlexRay vehicle network control system.

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