1,336 research outputs found

    Leader-following consensus for lower-triangular nonlinear multi-agent systems with unknown controller and measurement sensitivities

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    summary:In this paper, a novel consensus algorithm is presented to handle with the leader-following consensus problem for lower-triangular nonlinear MASs (multi-agent systems) with unknown controller and measurement sensitivities under a given undirected topology. As distinguished from the existing results, the proposed consensus algorithm can tolerate to a relative wide range of controller and measurement sensitivities. We present some important matrix inequalities, especially a class of matrix inequalities with multiplicative noises. Based on these results and a dual-domination gain method, the output consensus error with unknown measurement noises can be used to construct the compensator for each follower directly. Then, a new distributed output feedback control is designed to enable the MASs to reach consensus in the presence of large controller perturbations. In view of a Lyapunov function, sufficient conditions are presented to guarantee that the states of the leader and followers can achieve consensus asymptotically. In the end, the proposed consensus algorithm is tested and verified by an illustrative example

    Reinforcement Learning, Intelligent Control and their Applications in Connected and Autonomous Vehicles

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    Reinforcement learning (RL) has attracted large attention over the past few years. Recently, we developed a data-driven algorithm to solve predictive cruise control (PCC) and games output regulation problems. This work integrates our recent contributions to the application of RL in game theory, output regulation problems, robust control, small-gain theory and PCC. The algorithm was developed for H∞H_\infty adaptive optimal output regulation of uncertain linear systems, and uncertain partially linear systems to reject disturbance and also force the output of the systems to asymptotically track a reference. In the PCC problem, we determined the reference velocity for each autonomous vehicle in the platoon using the traffic information broadcasted from the lights to reduce the vehicles\u27 trip time. Then we employed the algorithm to design an approximate optimal controller for the vehicles. This controller is able to regulate the headway, velocity and acceleration of each vehicle to the desired values. Simulation results validate the effectiveness of the algorithms
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