Improvement of Genetic Algorithm and Its Application in Optimization of Fuzzy Traffic Control Algorithm

2005 ◽  
pp. 132-141
Author(s):  
Jian Qiao ◽  
Huiyu Xuan ◽  
Jinhu Jiang
Keyword(s):  
Genetic Algorithm ◽  
Traffic Control ◽  
Control Algorithm

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

The Walking Robot Control System that is Adaptive to Changes in the Kinematics

2021 ◽  
Vol 22 (11) ◽  
pp. 601-609
Author(s):  
A. S. Samoylova ◽  
S. A. Vorotnikov
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Robot Control ◽  
Control Algorithm ◽  
Human Life ◽  
Support Surface ◽  
Machine Modeling ◽  
Cargo Delivery ◽  
Formal Classification

The walking mobile robots (WMR) have recently become widely popular in robotics. They are especially useful in the extreme cases: search and rescue operations; cargo delivery over highly rough terrain; building a map. These robots also serve to explore and describe a partially or completely non-deterministic workspace, as well as to explore areas that are dangerous to human life. One of the main requirements for these WMR is the robustness of its control system. It allows WMR to maintain the operability when the characteristics of the support surface change as well as under more severe conditions, in particular, loss of controllability or damage of the supporting limb (SL). We propose to use the principles of genetic programming to create a WMR control system that allows a robot to adapt to possible changes in its kinematics, as well as to the characteristics of the support surface on which it moves. This approach does not require strong computational power or a strict formal classification of possible damage to the WMR. This article discusses two main WMR control modes: standard, which accord to a serviceable kinematics, and emergency, in which one or more SL drives are damaged or lost controllability. As an example, the structure of the control system of the WMP is proposed, the kinematics of which is partially destroyed in the process of movement. We developed a method for controlling such robot, which is based on the use of a genetic algorithm in conjunction with the Mealy machine. Modeling of modes of movement of WMR with six SL was carried out in the V-REP program for two cases of injury: absent and not functioning limb. We present the results of simulation of emergency gaits for these configurations of WMP and the effectiveness of the proposed method in the case of damage to the kinematic scheme. We also compared the performance of the genetic algorithm for the damaged WMR with the standard control algorithm.

scholarly journals Optimization of the Parameters of RISE Feedback Controller Using Genetic Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Fayiz Abu Khadra ◽  
Jaber Abu Qudeiri ◽  
Mohammed Alkahtani
Keyword(s):  
Genetic Algorithm ◽  
Numerical Simulations ◽  
Control Algorithm ◽  
Chaotic Systems ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Algorithm Optimization ◽  
External Disturbances ◽  
Van Der Pol ◽  
Control Methodology

A control methodology based on a nonlinear control algorithm and optimization technique is presented in this paper. A controller called “the robust integral of the sign of the error” (in short, RISE) is applied to control chaotic systems. The optimum RISE controller parameters are obtained via genetic algorithm optimization techniques. RISE control methodology is implemented on two chaotic systems, namely, the Duffing-Holms and Van der Pol systems. Numerical simulations showed the good performance of the optimized RISE controller in tracking task and its ability to ensure robustness with respect to bounded external disturbances.

Analysis and comparison of the two sectoring approaches in elevator traffic systems

2019 ◽  
Vol 40 (5) ◽  
pp. 611-626
Author(s):  
Lutfi Al-Sharif ◽  
Ahmad Hammoudeh ◽  
Jannat Al-Saidi
Keyword(s):  
Control Systems ◽  
Traffic Control ◽  
Control Algorithm ◽  
Dynamic Allocation ◽  
Practical Application ◽  
Round Trip ◽  
Allocation Method ◽  
Group Control ◽  
Static Allocation ◽  
Traffic Systems

Sectoring is a group control algorithm that is used in elevator traffic control systems by grouping passengers that have common destinations or common origins into elevator cars that serve these floors. The building is split into sectors usually comprising contiguous floors. Two different alternative algorithms for sectoring are discussed in this paper. The first approach is based on dynamic allocation with equal sector allocation. The second approach is based on static allocation with unequal sector sizes. Under static allocation, the same elevator car is allocated to the same sector in every round trip. Under dynamic allocation, each elevator car is allocated to a different sector in each round trip. Under the dynamic allocation scheme suggested in this paper, the elevator cars are sequenced to the various sectors in the buildings in a round-robin fashion. It is important to note that under both schemes, the provided (relative) handling capacity of different sectors is equalised. Five different buildings have been analysed using the two suggested sectoring algorithms. The building is first designed by finding the required number and speed of elevators assuming conventional control. Each building is then analysed using one of the two suggested sectoring algorithms. In order to compare the performance of the two sectoring algorithms, the provided (relative) handling capacity is calculated. The provided (relative) handling capacity of the two suggested algorithms is then compared. Very little difference was found between the two algorithms. The dynamic sectoring with equal sector sizes offers the convenience of having equal sector sizes. The static sectoring with unequal sector sizes is more convenient for passengers that are familiar with the building. Practical application: This paper analyses two different options for sectoring the control system of an elevator system in a building. Each of the two sectoring methods is suitable for different situations. The dynamic allocation method is more suitable for destination group control systems and offers the group controller more flexibility. The static allocation method with unequal sector sizes ensures that the passengers remain familiar with the elevators that they use to get to their floors. The programmer of the elevator group controller can programme both methods in the controller and allow it to switch to the most suitable algorithm depending on the prevailing conditions.

Optimal Placement of Reservation-Based Intersections in Urban Networks

2019 ◽  
Vol 2673 (10) ◽  
pp. 781-792
Author(s):  
Rahul Patel ◽  
Prashanth Venkatraman ◽  
Stephen D. Boyles
Keyword(s):  
Genetic Algorithm ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Large City ◽  
Ranking Method ◽  
Optimal Placement ◽  
Total System ◽  
Travel Times ◽  
Urban Networks ◽  
High Performing

Reservation-based traffic control is a revolutionary intersection management system which involves the communication of autonomous vehicles and an intersection to request space-time trajectories through the intersection. Although previous studies have found congestion and throughput benefits of reservation-based control that surpass signalized control, other studies have found negative impacts at peak travel times. The main purpose of this paper is to find and characterize favorable mixed configurations of reservation-based controls and signalized controls in a large city network which minimize total system travel times. As this optimization problem is bi-level and challenging, three different methods are proposed to heuristically find effective mixed configurations. The first method is an intersection ranking method that uses simulation to assign a score to each intersection in a network based on localized potential benefit to system travel time under reservation control and then ranks all intersections accordingly. The second is another ranking method; however, it uses linear regression to predict an intersection’s localized score. Finally, a genetic algorithm is presented that iteratively approaches high-performing network configurations yielding minimal system travel times. The methods were tested on the downtown Austin network and configurations found that are less than half controlled by reservation intersections that improve travel times beyond an all-reservation controlled network. Overall, the results show that the genetic algorithm finds the best performing configurations, with the initial score-assigning ranking method performing similarly but much more efficiently. It was finally find that favorable reservation placement is in consecutive chains along highly trafficked corridors.

scholarly journals Calculation of vehicle delay at signal-controlled intersections with adaptive traffic control algorithm

2018 ◽  
Vol 143 ◽  
pp. 04008 ◽  
Author(s):  
Roman Andronov ◽  
Evgeny Leverents
Keyword(s):  
Traffic Control ◽  
Control Algorithm ◽  
Flow Simulation ◽  
Traffic Light ◽  
The Monte Carlo Method ◽  
Traffic Flow Simulation ◽  
Traffic Light Control ◽  
Vehicle Delay ◽  
Pedestrian Traffic ◽  
Adaptive Traffic Control

By widely introducing information technology tools in the field of traffic control, it is possible to increase the capacity of hubs and reduce vehicle delays. Adaptive traffic light control is one of such tools. Its effectiveness can be assessed through traffic flow simulation. The aim of this study is to create a simulation model of a signal-controlled intersection that can be used to assess the effectiveness of adaptive control in various traffic situations, including the presence or absence of pedestrian traffic through an intersection. The model is based on a numerical experiment conducted using the Monte Carlo method. As a result of the study, vehicle delays, queue length and duration of traffic light cycles are calculated subject to different intensities of incoming traffic flows, and the presence or absence of pedestrian traffic.

scholarly journals An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach

2013 ◽  
Vol 23 (1) ◽  
pp. 183-200 ◽  
Author(s):  
Fei Yan ◽  
Mahjoub Dridi ◽  
Abdellah El Moudni
Keyword(s):  
Genetic Algorithm ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Control Methods ◽  
Traffic Condition ◽  
Sequencing Problem ◽  
Genetic Algorithm Approach ◽  
Short Time ◽  
Intersection Management

This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.

scholarly journals Marine Traffic Optimization Using Petri Net and Genetic Algorithm

1970 ◽  
Vol 24 (6) ◽  
pp. 469-478 ◽  
Author(s):  
Anita Gudelj ◽  
Danko Kezić ◽  
Stjepan Vidačić
Keyword(s):  
Genetic Algorithm ◽  
Petri Net ◽  
Traffic Control ◽  
Formal Method ◽  
Discrete Event ◽  
Control Policy ◽  
Shared Resources ◽  
Marine Traffic ◽  
Event Dynamic ◽  
Scheduling Tool

The paper deals with the traffic control and job optimization in the marine canal system. The moving of vessels can be described as a set of discrete events and states. Some of these states can be undesirable such as conflicts and deadlocks. It is necessary to apply adequate control policy to avoid deadlocks and blocks the vessels’ moving only in the case of dangerous situation. This paper addresses the use of Petri net as modelling and scheduling tool in this context. To find better solutions the authors propose the integration of Petri net with a genetic algorithm. Also, a matrix based formal method is proposed for analyzing discrete event dynamic system (DEDS). The algorithm is developed to deal with multi-project, multi-constrained scheduling problem with shared resources. It is verified by a computer simulation using MATLAB environment.

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