Within the Control Systems Department, we model the rocket’s dynamics and behavior using Simulink. We develop the active control system to precisely follow the rocket’s desired trajectory, simulating the response of the active control surfaces. Our system regulates the airbrakes’ response to ensure optimal performance, allowing the rocket to precisely handle unexpected deviations from the planned flight trajectory.
The current active control system is based on the airbrakes. Using Simulink, our system employs a precomputed lookup table containing airbrake extension values. Continuously referenced throughout the flight, this table serves as a reference point, generating the desired airbrake extension. The discrepancy between this desired extension and the actual extension is then corrected by a set of factors — gains — meticulously evaluated during the design phase. These gains ensure the system maintains an optimal balance, dynamically adjusting the airbrakes’ position to precisely match the rocket’s trajectory. Our system’s ability to adapt in real time, continuously tuning the airbrakes’ extension, ensures precise tracking of the desired trajectory.
The Kalman Filter, specifically the Extended Kalman Filter (EKF), lies at the heart of our control algorithm. Our strategies benefit enormously from obtaining the most accurate possible state estimate of the sounding rocket. This process relies heavily on processing sensor measurements obtained from the onboard sensors — IMU: accelerometers, magnetometers, gyroscopes, and barometers.
By integrating these measurements with predictive models, our algorithms are able to reconstruct the rocket’s state, enabling robust and precise control even under dynamic environmental conditions.
Software-in-the-Loop (SITL) and Hardware-in-the-Loop (HITL) simulations are fundamental to our development approach. SITL simulations enable rapid prototyping, allowing us to iteratively refine controller gains and validate control strategies in a virtual environment. These simulations are crucial for evaluating how our control algorithms respond to disturbances, letting us fine-tune and optimize our strategies. In addition to SITL simulations, HITL simulations bridge the gap between the virtual and the physical, enabling the evaluation of controller responses using real rocket hardware. This method allows us to further test, validate, and optimize our control strategies by assessing their performance in a real-world context, ensuring robustness and reliability in our control mechanisms.
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