Random network delay will introduce uncertainty into trajectory tracking model of the autonomous vehicle, which seriously deteriorates the vehicle's control system stability and trajectory tracking accuracy. In this paper, considering steering angle oscillation caused by random network delay, trajectory tracking system robustness and stability is analyzed and a linear uncertain time-delay system is established. Comprehensively considering control system accuracy, robustness, and computational efficiency in the rolling optimization of Model Predictive Control (MPC), Adaptive Model Predictive Control for Uncertain model (UM-AMPC) algorithm is proposed to predict control variables for the next sampling time and alleviate the target angle discontinuity. This is achieved by operating target angle signal and augmented state variables, which are received by the lower nodes during the period from the current sampling time to network delay upper bound. The hardware-in-the-loop simulation results show that the proposed algorithm can effectively guarantee system stability and tracking accuracy of the autonomous vehicle under random network delay.

The civil aviation sector plays an increasingly significant role in transportation sustainability in the environmental, economic, and social dimensions. Driven by the concerns of sustainability in the aviation sector, more electrified aircraft propulsion technologies have emerged and form a very promising approach to future sustainable and decarbonized aviation. This review paper aims to provide a comprehensive and broad-scope survey of the recent progress and development trends in sustainable aviation electrification. Firstly, the architectures of electrified aircraft propulsion are presented with a detailed analysis of the benefits, challenges, and studies/applications to date. Then, the challenges and technical barriers of electrified aircraft propulsion control system design are discussed, followed by a summary of the control methods frequently used in aircraft propulsion systems. Next, the mainstream energy management strategies are investigated and further utilized to minimize the block fuel burn, emissions, and economic cost. Finally, an overview of the development trends of aviation electrification is provided.

Hybrid electric propulsion systems have been identified as feasible solutions for regional jets and narrow-body aircraft to reduce block fuel burn, emissions, and operating costs. In this article, a nonlinear model predictive control-based optimal energy management scheme (MPC-EMS) has been proposed to minimize the block fuel burn during flight. First, the artificial neural network (ANN) model is adopted to predict turbofan engine performance; meanwhile, gas turbine–electrical powertrain integration is investigated and analyzed for typical operating conditions. Then, by combining a point-mass aircraft dynamic model, nonlinear MPC with the cross-entropy method (CEM) is proposed to obtain optimal energy management based on a fully coupled aerodynamics-propulsion hybrid electric aircraft model. Besides, this state-constrained optimal control problem is reformulated as a state-unconstrained problem with a penalty function to reduce the computational load. Finally, the proposed MPC-EMS algorithm is applied to Boeing 737-800 aircraft with mechanically parallel hybrid electric propulsion configuration to minimize the block fuel burn and compared with the optimization results using global genetic algorithm (GA)-based EMS and equivalent consumption minimization strategy (ECMS). The simulation results indicate that the proposed MPC-EMS can effectively reduce the computational time compared with global GA-based EMS while achieving global optimization performance with only a minor difference of 1.71% of block fuel burn and emissions reductions.

This article proposes a new concept of aviation-to-grid (A2G) that utilizes electric aircraft (EA) charging to provide flexibility to the power grid. Smart EA charging system with battery swap method is developed using photovoltaics, gas turbine, and grid electricity. Hourly energy dispatch strategy is produced on the basis of the mixed-integer linear programming method to meet electrified aviation charging demand and provide A2G frequency response to the power grid. Case studies are conducted in eight major U.K. airports considering seasonal flight schedules and power system operation scenarios. Results show that the EA charging system can provide effective primary and secondary frequency responses to improve the frequency nadir by 0.2–0.3 Hz under grid disturbance. The total A2G frequency response capacity across the eight U.K. airports can reach 900–1200 MW overnight and 300–900 MW daytime in summer and 1100–1300 MW overnight and 200–700 MW daytime in winter. The annual A2G frequency response revenue is estimated to be £46.58 million, which can cover 19.8% to 30% of EA charging costs.