In addition to making use of MapleSim’s built-in component library, custom components were also readily developed. A model to estimate the solar radiation that a tilted surface (i.e. solar panels) would receive on Mars was implemented using MapleSim’s Custom Component Block. This model took into account the sun’s position, the rover’s latitudinal and longitudinal position as well as orientation and tilt as it traveled from point A to point B. This was used together with a solar array model (see figure below) to estimate the power generation of a rover throughout the day.
“The intuitive nature of MapleSim allowed my team to create high fidelity models in a short period of time,” said Khajepour. “This played a key role in the success of this modular HIL test platform which allowed for component testing, power level estimation, as well as the validation of power management and path planning algorithms.”
The team also used MapleSim as a tool in an earlier part of the project to develop the power management system of the autonomous rovers. They used the software to rapidly develop high-fidelity, multidomain models of the rover subsystems. The goal was to develop a path planning algorithm that took rover power demands (and generation) into account. Using the models developed, the path planner determined the optimum path between point A and point B, such that the rover maintained the highest level of internal energy storage—while avoiding obstacles and high risk sections of the terrain.
Step one of this three-year project was to develop the initial rover model, including such aspects as battery, solar power-generation, and terrain and soil conditions. Including a full range of HIL testing phases with real-time hardware and software using system models was critical for optimizing system parameters that maximized power conservation while still achieving mission goals.
“With the use of MapleSim, the base model of the rover was developed in a month,” says Khajepour. “We now have the mathematical model of the 6-wheeled rover without writing down a single equation. MapleSim was able to generate an optimum set of equations for the rover system automatically, which is essential in the optimization phase.” The symbolic techniques that lie at the heart of the software generate efficient system equations, without loss of fidelity—thus eliminating the need to simplify the model manually to reduce its computational complexity.
Khajepour also noted the graphical interface. In MapleSim, a design engineer can readily re-create the system diagram on his/her screen using components that represent the physical model. The resulting system diagram looks very similar to what an engineer might draw by hand. MapleSim can then easily transform the models into realistic animations. These animations make it substantially easier to validate the system diagram and give greater insight into the system behavior.
“The ability to see the model, to see the moving parts, is very important to a model developer,” says Khajepour. “I am now moving to MapleSim in most of my projects.”
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