Air corridors are an integral part of the advanced air mobility infrastructure. They are the virtual highways in the sky for the transportation of people and cargo in a controlled airspace at an altitude of around 1000 ft. to 2000 ft. above ground level. These corridors will be utilized by (unmanned) air taxis, which will be deployed in rural and metropolitan regions to carry passengers and freight, as well as air ambulances, which will be deployed to offer first responder services such as 911 emergencies. This paper presents fundamental insights into the design of air corridors with high operational efficiency as well as zero collisions. It begins with the definitions of air cube, skylane or track, intersection, vertiport, gate, and air corridor. Then a multi-layered air corridor model is proposed. Traffic at intersections is analyzed in detail with examples of vehicles turning in different directions. The concept of capacity of an air corridor is introduced along with the nature of distribution of locations of vehicles in the air corridor and collision probability inside the corridor are discussed. Finally, results of traffic flow simulations are presented.

This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations.

In this paper, a distributed driver-in-the-loop and hardware-in-the-loop simulator is described with a driver on a motion simulator at the U.S. Army TARDEC Ground Vehicle Simulation Laboratory (GVSL). Realistic power system response is achieved by linking the driver in the GVSL with a full-sized hybrid electric power system located 2,450 miles away at the TARDEC Power and Energy Systems Integration Laboratory (P&E SIL), which is developed and maintained by Science Applications International Corporation (SAIC). The goal is to close the loop between the GVSL and P&E SIL over the Internet to provide a realistic driving experience in addition to realistic power system results. In order to preserve a valid and safe hardware-in-the-loop experiment, the states of the GVSL must track the states of the P&E SIL. In a distributed control system utilizing the open Internet, the communications channel is a primary source of uncertainty and delay that can degrade the overall system performance and stability. The presence of a cross-country network delay and the unavoidable differences between the P&E SIL hardware and GVSL model will cause the GVSL states and P&E SIL states to diverge without any additional action. Thus, two robust strategies for state convergence are developed and presented in this paper. The first strategy is a non-linear Sliding Mode control scheme. The second strategy is an H-infinity control scheme. Both schemes are implemented in simulation, and both schemes show promising results for state convergence in the presence of variable cross-country time delays.

In dynamic analysis of constrained multibody systems (MBS), the computer simulation problem essentially reduces to finding a numerical solution to higher-index differential-algebraic equations (DAE). This paper presents a hybrid method composed of multi-input multi-output (MIMO), nonlinear, variable-structure control (VSC) theory and post-stabilization from DAE solution theory for the computer solution of constrained MBS equations. The primary contributions of this paper are: (1) explicit transformation of constrained MBS DAE into a general nonlinear MIMO control problem in canonical form; (2) development of a hybrid numerical method that incorporates benefits of both Sliding Mode Control (SMC) and DAE stabilization methods for the solution of index-2 or index-3 MBS DAE; (3) development of an acceleration-level stabilization method that draws from SMC’s boundary layer dynamics and the DAE literature’s post-stabilization; and (4) presentation of the hybrid numerical method as one way to eliminate chattering commonly found in simulation of SMC systems. The hybrid method presented can be used to simulate constrained MBS systems with either holonomic, nonholonomic, or both types of constraints. In addition, the initial conditions (ICs) may either be consistent or inconsistent. In this paper, MIMO SMC is used to find the control law that will provide two guarantees. First, if the constraints are initially not satisfied (i.e., for inconsistent ICs) the constraints will be driven to satisfaction within finite time using SMC’s stabilization method, urobust,i=−ηisgnsi. Second, once the constraints have been satisfied, the control law, ueq and hybrid stabilization techniques guarantee surface attractiveness and satisfaction for all time. For inconsistent ICs, Hermite-Birkhoff interpolants accurately locate when each surface reaches zero, indicating the transition time from SMC’s stabilization method to those in the DAE literature. [S0022-0434(00)02404-7]