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This study presents a novel solution to agent coalition formation. It focuses on heterogeneous, distributed multi-agent systems deployed in real-world environments. Specifically, we study dynamic, uncertain environments in which tasks may evolve during execution, and agents and resource availability may vary rapidly and unpredictably. We refer to cases in which agent collaboration is needed for efficient...
We investigate stochastic averaging theory in continuous time for locally Lipschitz systems and the applications of this theory to stability analysis of stochastic extremum seeking algorithms. First, we establish a general stochastic averaging principle and some related stability theorems for a class of continuous-time nonlinear systems with stochastic perturbations and remove or weaken several significant...
We investigate stochastic averaging theory in continuous time for locally Lipschitz systems and the applications of this theory to stability analysis of stochastic extremum seeking algorithms. First, we establish a general stochastic averaging principle and some related stability theorems for a class of continuous-time nonlinear systems with stochastic perturbations and remove or weaken several significant...
This work develops a provably stable broadcast feedback approach to regulating the aggregate output of a collection of stochastically behaving cellular units with pronounced hysteresis. Similar to skeletal muscle, comprised of a number of muscle fibers, the cellular units are independent and each contributes to an aggregate system output. The control architecture consists of a central controller that...
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