The RemoteLink effort supports the U.S. Army's objective for developing and fielding next generation hybrid-electric combat vehicles. It is a distributed soldierin-the-Ioop and hardware-in-the-Ioop environment with a 6-DOF motion base for operator realism, a full-scale combat hybrid electric power system, and an operational context provided by OneSAF. The driver/gunner crewstations rest on one of two 6-DOF motion bases at the U.S. Army TARDEC Simulation Laboratory (TSL). The hybrid power system is located 2,450 miles away at the TARDEC Power and Energy System Integration Laboratory (P&E SIL). The primary technical challenge in the RemoteLink is to operate both laboratories together in real time, coupled over the Internet, to generate a realistic power system duty cycle. A topology has been chosen such that the laboratories have real hardware interacting with simulated components at both locations to guarantee local closed loop stability. This layout is robust to Internet communication failures and ensures the long distance network delay does not enter the local feedback loops. The TSL states and P&E SIL states will diverge due to (1) significant communications delays and (2) unavoidable differences between the TSL's powersystem simulation and the P&E SIL's real hardware-inthe-loop power system. Tightly coupled, bi-directional interactions exist among the various distributed simulations and software-and hardware-in-the-Ioop components representing the driver, gunner, vehicle, and power system. These interactions necessitate additional adjustment to ensure that the respective states at the TSL and P&E SIL sites converge. This is called state convergence and ensures the dominant energetic states of both laboratories remain closely matched in real time. State convergence must be performed at both locations to achieve bi-directional, real-time interaction like that found on a real vehicle. The result is a distributed control system architecture with Internet communications in the state convergence feedback loop. The Internet communication channel is a primary source of uncertainty that impacts the overall state convergence performance and stability. Multiple control schemes were developed and tested in simulation. This paper presents robust control techniques that compensate for asynchronous Internet communication delays during closed loop operation of the TSL and P&E SIL sites. The subsequent soldier-and hardware-in-the-Ioop experiments were performed using a combination of nonlinear Sliding-mode and linear PID control laws to achieve state convergence at both locations. The control system development, performance, and duty cycle results are presented in this paper.

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 crosscountry 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 crosscountry time delays.

This paper presents a hybrid hardware/software platform that supports flight control and mission planning algorithms for an autonomous helicopter. The emphasis is on the use of intelligent fuzzy logic based techniques and an object-modeling approach to account for unmodeled dynamics to address uncertainty issues and to provide a flexible platform for development purposes and an operator interface. Fuzzy logic routines are implemented in such critical vehicle modules as the route planner the fuzzy navigator the fault-tolerant tools and the flight controller.

The unmanned ground combat vehicle (UGCV) design evolved by the SAIC team on the DARPA UGCV Program is summarized in this paper. This UGCV design provides exceptional performance against all of the program metrics and incorporates key attributes essential for high performance robotic combat vehicles. This performance includes protection against 7.62 mm threats, C130 and CH47 transportability, and the ability to accept several relevant weapons payloads, as well as advanced sensors and perception algorithms evolving from the PerceptOR program. The UGCV design incorporates a combination of technologies and design features, carefully selected through detailed trade studies, which provide optimum performance against mobility, payload, and endurance goals without sacrificing transportability, survivability, or life cycle cost. The design was optimized to maximize performance against all Category I metrics. In each case, the performance of this design was validated with detailed simulations, indicating that the vehicle exceeded the Category I metrics. Mobility metrics were analyzed using high fidelity VisualNastran vehicle models, which incorporate the suspension control algorithms and controller cycles times. DADS/Easy 5 3-D models and ADAMS simulations were also used to validate vehicle dynamics and control algorithms during obstacle negotiation.

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