Researchers and engineers from companies are welcome, and may greatly benefit from the broad vision provided by this conference. ![]() The target audience of FTNCT ranges from Senior researchers to Ph.D. ![]() Application areas include networking, computing, machine learning, data mining, wireless communications, big data, cloud computing, augmented reality, image processing, mobile and optical communications, cyber security. In 2020, FTNCT will visit Taganrog, a port on the Sea of Azov in southern Russia, famous as a birthplace of famous Russian writer Anton Chekhov.The goal of the FTNCT is to explore the challenges, i ssues and opportunities both for academic researchers and industrial innovators from different different domains of Networks, Computing and Communication Technologies to discuss the latest frontiers of technology and novel trends that have not been thought of before. Various simulation results demonstrating the performance of the controller are included. For time-varying lead vehicle velocities, the proposed controller achieves ultimate boundness of the closed-loop system in error coordinate. For constant velocity maneuvers of the leader, at steady state, the two-vehicle convoy will travel concentric arcs of same radii with prescribed inter-vehicle spacing. With only the current inter-vehicle relative position and orientation available for feedback control, the control velocities of the following vehicle are computed using the leader velocity estimates obtained from the dynamic (adaptive) part of the proposed controller. Assuming that the leader linear and angular velocities, as well the curvature radius of the path traveled by the lead vehicle, are unknown constant parameters, an adaptive tracking controller is proposed. We consider autonomous vehicle following without any information obtained from road infrastructure or communicated from the lead vehicle. Kinematic equations of the system are formulated applying standard robotic methodology. This paper describes the modeling of a two-vehicle convoy and the design of a vehicle following controller that tracks the trajectory of the vehicle ahead with prescribed inter-vehicle distance. Simulations results are conducted to show the feasibility and efficiency of the proposed control methods. To go further in the investigation, fuzzy logic type 2 is used to get at each iteration the appropriate controller parameters that give the best performances and robustness. Four fitness functions are selected for this purpose, which is based on the integral of the error square (ISE), the integral of the square of the time-weighted error (ITSE), the integral of the error absolute (IAE) and the integral of the absolute of the time-weighted error (TIAE) criterion. ![]() These parameters depend on the best-selected fitness function. To find the optimised parameters of the SC, the grey wolf optimiser (GWO) algorithm is used. Lyapunov synthesis is adopted to assure controlled system stability. The SC control is used to make the linear velocity and steering velocity converge to references. This paper aims to present the dynamic control of a Car-like Mobile Robot (CLMR) using Synergetic Control (SC).
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