To improve the durability of the fuel cell unmanned aircraft vehicle hybrid power system, a model predictive control based energy management strategy with minimum energy loss is presented in this article. The proposed algorithm can effectively reduce the internal loss and power stress of the hybrid power system, which can help to extend fuel cell lifespan. Specifically, a calculation formula with observable parameters is proposed to predict the internal loss of the battery in both the charging and discharging mode. Besides, to increase the hybrid UAV adaptability under complex flying conditions, the fuel cell operating trajectory is also defined in the algorithm to increase the system dynamic performance and efficiency. To verify the effectiveness of the strategy, the hardware-in-the-loop experimental test platform is built. Compared to the conventional equivalent consumption minimum strategy, the proposed strategy can effectively alleviate the voltage degradation of the fuel cell and decrease the energy loss of the battery. The proposed strategy can help to contribute to the applications of fuel cell hybrid power system in the aviation fields.

To optimize the voltage regulation performance and enhance the robustness, an adaptive model predictive controller is proposed in this article for an interleaved dc–dc boost converter. The predictive model is constructed by linearizing the nonlinear equations of the converter at the current operating point. A novel parameter update mechanism is developed based on the static model, which enables fast and accurate identification of the model parameters, thus the control law can guarantee the desired control performance in the wide operating range. Based on the adaptive model, a disturbance observer is constructed to correct the model predictions and mitigate parameter uncertainties. Then an explicit control law is derived by solving the optimization problem offline, and the stability analysis is carried out using the linear control theory. Finally, the effectiveness of the proposed method is verified by both simulation and experimental results.

To improve the durability and efficiency, a novel energy management strategy (EMS) considering internal loss of fuel cell unmanned aircraft vehicle (UAV) hybrid power system is proposed. First, the equivalent circuit model of the hybrid power system is established to calculate its internal energy loss. The hydrogen consumption of fuel cells and the discharge behavior of the battery can be regarded as energy consumption. Then, the total energy consumption of system is used as the first cost function, and the average efficiency of the system is considered as the second cost function. And the Pontryagin's minimum principle (PMP) control strategy is applied to achieve optimization of system cost function. Finally, the output power of the fuel cell system is controlled by solving the cost function to realize power distribution. A real-time experimental hardware platform is built to compare the superiority between the proposed EMS and traditional equivalent hydrogen consumption minimum strategy (ECMS). The experimental results indicate that the proposed algorithm can reduce the equivalent hydrogen consumption of the system by 13.6%, and it can reduce the internal energy loss of fuel cell by 21.8% and can reduce the power supply stress of fuel cell by 49.1%, which can extend the lifetime of the fuel cell. Besides, the proposed strategy can effectively improve the operation efficiency of the system and maintain the battery SOC.

Due to the nonminimum phase characteristics and nonlinearity of boost converters, the control design is always a challenging issue. A novel model predictive control strategy is proposed for the boost converter in this work. First, the Super-Twisting algorithm is applied to current control, and the input–output plant for voltage control is derived based on the linearization technique. All the model uncertainties are defined as lumped disturbances, and a generalized proportional integral observer is designed to estimate the lumped disturbance. Second, a composite predictive approach is developed on the basis of the predictive model and disturbance estimations. By solving the cost function directly, the optimal control law is derived explicitly. Lastly, the effectiveness of the proposed control strategy is verified by both simulation and experimental results.

The power converter plays a crucial role in energy conversion systems, while advanced control algorithms can help stabilize the bus voltage and match the power demand. In this work, a novel fast terminal sliding mode control is proposed for boost converters to strengthen the disturbance rejection ability. First, a full-order sliding mode manifold is proposed to guarantee the finite-time convergence of system states, and an integral-type switching control law is proposed to deal with large disturbances. Second, the disturbance estimation method is used to identify the system uncertainties and theoretical analysis indicates that it can further enhance the robustness and reduce the chattering. Third, a parameter update mechanism is developed to calculate the model parameters at the current operating point, which contributes to improving the control performance and flexibility to different working conditions. Finally, simulation and experimental tests are conducted to verify the proposed method.

The single-bus dc electrical power system is a promising architecture for more electric aircraft. However, the high penetration of the constant power loads (CPLs) will seriously threaten the system stability. To this end, in this article, a novel model predictive control (MPC) is proposed to eliminate the destabilizing effects of CPLs and optimize the transient performance of the electrical power systems over a wide operating range. First, a state coordinate system is introduced to obtain a canonical form model, and the newly defined output state is disturbed by load power and system uncertainties. Then, the finite-time disturbance observers are utilized to estimate the dynamic load power and system uncertainties. Second, the estimated values are integrated into MPC to regulate the output state to its reference value. By solving the optimization problem offline, a continuous-time MPC scheme is obtained explicitly. Besides, different from the existing MPC schemes that make system states converge to equilibrium points in infinite time, the concept of reference trajectory in the discrete-time MPC schemes is introduced into the continuous-time MPC framework for the first time, and a new second-order reference trajectory with terminal attractor is proposed, yields a finite-time MPC law. Finally, the effectiveness of the proposed MPC scheme is verified by experimental tests.