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Motivated by optimization of network communication systems, this paper presents an event-driven semi-Markov switching state-space control process with hierarchical dynamic architectures. First, the semi-Markov kernel of the switching control process is constructed, and the sensitivity formula for performance derivatives under average criterion is derived. Then, an online optimization algorithm that combines policy gradient estimation and stochastic approximation is proposed. This analytic model is with constructional flexibility and scalability, and the proposed optimization algorithm is adaptive and with less computational cost. Finally, as an illustrative example, the load balancing problem in a streaming media server cluster is formulated and addressed.

For a class of hierarchical unstructured P2P systems, a Markov Switching-Space model is introduced to describe their behavior of dynamic grouping. It is also formulated as a optimization problem based on Markov Decision Processes. And parameterized gradient algorithms are provided to optimize the performance, where two parameter reductions are mentioned. Following them are the corresponding simulations and discussions.

This paper investigates the problem of adaptive output tracking for a class of nonlinear systems in general lower-triangular form, which not only encompasses some important lower-triangular systems, but also expands the normal lower-triangular form to a more general case. The systems are classified into two cases: polynomial lower-triangular form and function lower-triangular form. Based on Lyapunov stability theory and modified power integrator approach, the adaptive controllers are designed to ensure the global stability property and practical output tracking. Examples and simulations are provided to show the effectiveness of design method.