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In this paper, we present a space-and-time-synchronized control method with application to the simultaneous tracking/formation. In the framework of polar coordinates, through correlating and decoupling the reference/actual kinematics between the self vehicle and target, time and space are separated, controlled independently. As such, the specified state can be achieved at the predetermined terminal time, meanwhile, the relative trajectory in space is independent of time. In addition, for the stabilization before the predesigned time, a cascaded prescribed-time control theorem is provided as the preliminary of vehicle tracking control. The obtained results can be directly extended to the simultaneous tracking/formation of multiple vehicles. Finally, numerical examples are provided to verify the effectiveness and superiority of the proposed scheme.
This paper addresses a formation tracking problem for nonlinear multi-agent systems with time-varying actuator faults, in which only a subset of agents has access to the leaders information over the directed leader-follower network with a spanning tr
Nonholonomic models of automobiles are developed by utilizing tools of analytical mechanics, in particular the Appellian approach that allows one to describe the vehicle dynamics with minimum number of time-dependent state variables. The models are c
This paper studies distributed optimal formation control with hard constraints on energy levels and termination time, in which the formation error is to be minimized jointly with the energy cost. The main contributions include a globally optimal dist
Clustering formation has been observed in many organisms in Nature. It has the desirable properties for designing energy efficient protocols for Wireless Senor Networks (WSNs). In this paper, we present a new approach for energy efficient WSNs protoc
We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The method combines elements of evolving intelligence, granular machine learning, and multi-variable control. We propose a State-Space Fuzzy-set-Based