Hardware In-the-Loop

An autonomous controller for an underground mining truck is presented in this paper. The controlled truck is an articulated type with 4 electric drive motors. Dynamic model of this truck including steering and 4-wheel differential control is setup. A sliding mode control law is proposed for asymptotically stabilizing the truck to a desired trajectory. It is shown that the proposed scheme is robust to bounded external disturbances. Experimental results on Hardware-in-the-loop demonstrate the effectiveness of accurate tracking capability and the robust performance of the proposed scheme. 

The devices in the hardware in the loop simulation are NIPXI-8110 embedded controller and cRIO-9024 real-time controller (Figure 4). Simulation time is 100s. Start is (-3, -25). 

In this paper, the research subject is a 35-tonne electrical transmission underground mining articulated dump truck. And the hardware in the loop simulation is based on NI cRIO and PXI controller. The Ackermann s formula is used for solving the control law u. 

Second, the loose coupling of individual components must be hierarchically controlled. FDMU is therefore distributed on four building blocks (Fig. 13.9) CAD/PDM system, hardware-in-the-loop (HIL)/software-in-the-loop (SIL) system. 

Another useful analysis and development tool is provided by the use of the hardware in the loop (HIL) technique. HIL testing is based on the use of an embedded set of... 

P.J. Gawthrop, D.W. Virden, S.A. NeUd, and D.J. Wagg. Emulator-based control for actuator-based hardware-in-the-loop testing. Control Engineering Practice, 16(8) 897-908, 2008. 10.1016/j.conengprac.2007.10.009. Available online 3 December 2007. 

These are generated in symbolic form and have other uses not related to the type of simulations presented here. This can be used to program real-time simulations with hardware in the loop where the mathematical model of the controlled device is programmed using the state space form of the equations of the physical system, in this case produced by CAMPG in symbolic form. 

Current methods of validating software for autonomy in aerospace systems involve a series of expensive evaluation steps to heuristically develop confidence in the system. For example, UAV flight software typically requires validation first on a software simulator, then on a hardware-in-the-loop simulator, and then on flight tests. Fault-management systems continue to operate during flights, as required. 

As far as possible, the automatic integration phase will also integrate a test using hardware-in-the-loop tests. This is a very interesting and a very important test. Some functionality or some behavior can only be tested with hardware in the loop. Timing issues may be mentioned as an example. It is our goal to test early and to find errors or undesired behavior as early as possible. The earlier an error is found the easier and the cheaper it is to correct it. 

Regarding to fault models and their simulation, the Model-Based Generation of Test-Cases for Embedded Systems (MOGENTES) project [20] specifies a number of HW and SW related fault and failure models and taxonomies. On the other hand, the international ASAM AE HIL [26] standard defines an interface to perform error simulation in Hardware in the Loop testing. 

The selected XML schema for the definition of fault injection campaigns complies with the international ASAM AE HIL standard for hardware-in-the-loop testing. Although the aim of this work is not to perform fault injection at... 

A Software Validation Facility (SVF) overcomes the impossibility of testing the ADCS in its real environment. Based on a Hardware-in-the-Loop (HIL) approach, the SVF allows the execution of the application software with a simulated environment. Following this approach, the ADCS software executes on the embedded computer, while the system sensors, actuators and the spacecraft dynamics are simulated on a development computer using the Simulink model discussed in section 3. 

Hardware In the Loop. HIL consists of models for vehicle dynamics, vehicle equipment (e.g. sensors and actuators), guidance infrastructure (e.g. the road surface and the magnet grid) and the environment (e.g. wind and slippery road conditions) and rans on a dedicated separate platform from the algorithms under development, which ran on the intended target platform. 

In our development we have focused on the logical aspects of data monitoring - the data freshness and integrity properties. Implicitly, we assume that the time-related constraints have been obtained by the corresponding real-time analysis. Such analysis allows us to derive the constraints on how often sensor data should be read, the DPU worst case execution time (WCET), the upper bound of network delay and how often the local clocks should be adjusted. Usually, this analysis is performed when the system is implemented, i.e., with hardware in the loop. In our previous work, we have also experimented with the verification of real-time properties in Event-B [4] and demonstrated how to assess interdependencies between timing constraints at the abstract specification level. 

A Prototyping Framework The environment should be structured and be able to communicate with simulation systems (Matlab, Simulink, Labview, etc.) to quickly perform prototyping and Hardware In-the-Loop (HIL). 

---