In order to obtain more output power of photovoltaic (PV) array, which depends on solar irradiation and ambient temperature, maximum power point tracking (MPPT) techniques are employed. Among all the MPPT strategies, the Perturb and Observe (P&O) algorithm is more attractive due to the simple control structure. Nevertheless, steady-state oscillations always appear due to the perturbation. In this paper, a new MPPT method based on BP Neural Networks and P&O is proposed for searching maximum power point (MPP) fast and exactly, and its effectiveness is validated by experimental results using hardware platform based on microcomputer.
The output feedback exponential robust control for networked control systems with stochastic network-induced delay and packet dropout is studied. Firstly, a state observer is designed for estimating the state values and compensating data dropouts. Then the closed system is modeled as asynchronous dynamical system constrained by configuration event rate. By using Lyapunov principle, sufficient and necessary conditions for robust stability and exponential stability of the closed loop system is obtained and the output feedback controller design method is presented. Simulation example for the design is done, comparing with the other relative methods, the results shows the validity of the method.
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