One of the main renewable energy sources for the future is photovoltaic (PV) energy. Hence, working of the PV systems at maximum efficiency is taken into consideration in recent years. In this paper, for improving the performance of the global maximum power point tracking under partial shading conditions and uncertainty in parameters of DC-DC converter, a two-level adaptive control scheme is proposed. The proposed controller is capable of efficiently handling the uncertainties in the PV systems and the perturbations in the environment. The first level is global perturbation-based extremum seeking control (GPESC), and the second level is model reference adaptive control (MRAC). GPESC is used to find global maximum power point and MRAC is utilized to handle the dynamics of the DC-DC converter. Adequate difference in the time constants of control levels, causes decoupled control levels, which in turn makes it easy to design the controller. The performance of the proposed control scheme is evaluated through simulation based on four indicators: tracking accuracy, tracking efficiency, tracking speed and searching resolution for different irradiance patterns. The results are compared with GPESC and GPESC with PID controller.
Nowadays, fuel cells (FCs) are considered suitable alternative sources for electrical energy applications. One major challenge encountered in FCs is relevant to the performance of the maximum power point tracking (MPPT) under FC parameter changes and load variations. This challenge is due to the nonlinearity and time-varying dynamics of FC systems. In this paper, the MPPT is studied in a system composed of a FC and a DC-DC converter. To improve the performance of the MPPT, application of perturbation-based extremum seeking (PES) and model reference adaptive control (MRAC) is proposed. This control scheme can efficiently handle the uncertainties in the FC as well as the load, through two control levels. The first level is PES utilized to adjust the duty cycle of the DC-DC converter; and the second level is MRAC employed to achieve the desired dynamic response. Using the proposed control strategy, design and analysis of the control levels can be realized independently, which results in easy implementation. This is achieved due to considerable differences between the time constants of the control levels. The simulation results are utilized to confirm the effectiveness of the proposed scheme in response to the variations of FC parameters and load. Also, comparative studies with a combination of PES and PID controller are provided in the simulation.
In this paper, unstructured system identification algorithm based on orthonormal Laguerre functions is combined with predictive functional control such that similar classical PI controller is constructed. Lack of mathematical model and initial information about process is not a restriction for mentioned algorithm and unstructured system identification based on Laguerre functions can overcome these restrictions. Augmenting new state variables to system state space, a new algorithm is constructed. This algorithm has similar structure with classical PI controller and in predictive control's cost function, in addition to tracking error, system states is utilized, that leads to improve controller dynamical performance. This new algorithm is simulated on the superheated steam temperature system in thermal power plant. Simulation results show capabilities of this algorithm.
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