Artificial Intelligence
M. Abdollahi; Z. Boujarnezhad
Abstract
Background and Objectives: As cities are developing and the population increases significantly, one of the most important challenges for city managers is the urban transportation system. An Intelligent Transportation System (ITS) uses information, communication, and control techniques to assist the transportation ...
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Background and Objectives: As cities are developing and the population increases significantly, one of the most important challenges for city managers is the urban transportation system. An Intelligent Transportation System (ITS) uses information, communication, and control techniques to assist the transportation system. The ITS includes a large number of traffic sensors that collect high volumes of data to provide information to support and improve traffic management operations. Due to the high traffic volume, the classic methods of traffic control are unable to satisfy the requirements of the variable, and the dynamic nature of traffic. Accordingly, Artificial Intelligence and the Internet of Things meet this demand as a decentralized solution.Methods: This paper presents an optimal method to find the best route and compare it with the previous methods. The proposed method has three phases. First, the area should be clustered under servicing and, second, the requests will be predicted using the time series neural network. then, the Whale Optimization Algorithm (WOA) will be run to select the best route.Results: To evaluate the parameters, different scenarios were designed and implemented. The simulation results show that the service time parameter of the proposed method is improved by about 18% and 40% in comparison with the Grey Wolf Optimizer (GWO) and Random Movement methods. Also, the difference between this parameter in the two methods of Harris Hawks Optimizer (HHO) and WOA is about 5% and the HHO has performed better.Conclusion: The interaction of AI and IoT can lead to solutions to improve ITS and to increase client satisfaction. We use WOA to improve time servicing and throughput. The Simulation results show that this method can be increase satisfaction for clients.
Artificial Intelligence
F. Jamshidi; M. Vaghefi
Abstract
Background and Objectives: A robot arm is a multi-input multi-output and non-linear system that has many industrial applications. Parameter uncertainties and external disturbances attenuate the performance of this system and a controller design is hence necessary to overcome them.Methods: In ...
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Background and Objectives: A robot arm is a multi-input multi-output and non-linear system that has many industrial applications. Parameter uncertainties and external disturbances attenuate the performance of this system and a controller design is hence necessary to overcome them.Methods: In this paper, the interval Type II Fuzzy fractional-order proportional integral differential (IT2FO-FPID) controller is designed to control a robot arm with 2 degrees of freedom (two-link robot arm). Whale optimization algorithm (WOA) is used to determine the optimal value of controller parameters. The performance of IT2FO-FPID is compared with PID, fractional-order PID (FOPID) and Fuzzy FOPID whose parameters are determined by WOA. The performance of IT2FO-FPID whose parameters are determined by WOA, genetic algorithm, and particle swarm optimization methods are compared.Results: Quantitative and qualitative results of simulations indicate performance improvement with the IT2FO-FPID controller. The ability of WOA in optimizing the parameters of the IT2FO-FPID controller is demonstrated.Conclusion: Sensitivity analysis and the study of the effect of parameter variations and disturbances confirm the robust performance of WOA-based IT2FO-FPID.
