RESUMEN
Safe and economic operation of an open-cathode proton exchange membrane fuel cell (PEMFC) requires an efficient thermal management strategy. The stack temperature regulation in PEMFC is, however, challenging due to often stringent set-point tracking tasks, frequent load fluctuations, constrained manipulated variables, and various modeling uncertainties and nonlinearities. To this end, a feed-forward offset-free model predictive control (MPC) approach, aiming at uncertainties resolving and disturbance mitigation, is developed to simultaneously address the above difficulties. In the proposed framework, the information about the measured power load fluctuations is used in the optimization algorithm as feed-forward information to eventually mitigate the influence of load fluctuations on the controlled output and increases the overall control quality. Additionally, the unmodeled dynamics and the other unmeasurable disturbances/uncertainties are collectively considered as an extended state of the system (to achieve zero static errors) and the on-line reconstructed aggregated disturbances is continuously sent to the MPC algorithm to increase its optimization performance and to achieve offset-free control objectives. The obtained results are quantitatively compared with conventional control strategies for PEMFCs, including a model-based PI controller, its modification utilizing disturbance feed-forward, and a standard offset-free MPC (i.e. without feed-forward). Both the simulations, realized in MATLAB/Simulink, and hardware experiments, conducted on a 500 W PEMFC testbed, show excellence of the proposed feed-forward offset-free MPC consisting in faster temperature tracking and higher robustness. The obtained satisfactory results show the introduced control solution to be a promising prospect and help accelerating further applications of PEMFCs.
RESUMEN
The extended state observer (ESO) plays an important role in the design of feedback control for nonlinear systems. However, its high-gain nature creates a challenge in engineering practice in cases where the output measurement is corrupted by non-negligible, high-frequency noise. The presence of such noise puts a constraint on how high the observer gains can be, which forces a trade-off between fast convergence of state estimates and quality of control task realization. In this work, a new observer design is proposed to improve the estimation performance in the presence of noise. In particular, a unique cascade combination of ESOs is developed, which is capable of fast and accurate signals reconstruction, while avoiding over-amplification of the measurement noise. The effectiveness of the introduced observer structure is verified here while working as a part of an active disturbance rejection control (ADRC) scheme. The conducted numerical validation and theoretical analysis of the new observer structure show improvement over standard solution in terms of noise attenuation.
RESUMEN
With high penetration of renewable energy sources in nested multiple-microgrids, conventional solutions for the integration of load frequency control and economic dispatch may degrade frequency control performance and decrease operational economy. In this paper, a fast frequency recovery-oriented distributed optimal control strategy is proposed to deal with these problems. Firstly, a partial primal-dual gradient algorithm is dynamically integrated with an active disturbance rejection control algorithm (instead of conventional Proportional-Integral (PI) controller) to realize the fast frequency recovery and enhance anti-disturbance capability. As a result, frequent adjustments of resources can be avoided and this is crucial in extending the life cycles of batteries. Then, based on the above integration, the distributed optimal control law is derived, which is independent of load measurement and fully distributed, to coordinate the microgrids to share their power economically during the frequency regulation process. This can also relieve the communication and computation burden of the system. Finally, a set of numerical simulations is presented and the effectiveness of the proposed distributed optimal control is verified by the obtained results, which include a comparison with the conventional distributed PI-based optimal control strategy.