Control Systems Design Engineer for Rolls Royce Trent XWB aircraft engine• Analyse and translate gas turbine product requirement into set of validated functional requirements for the control sub-systems.• Design the control sub-systems to meet the functional requirements and design constraints.• Root cause analysis and solution proposals from systems design• Requirements management on DOORS and PTC Integrity.• Detailed analysis of risk associated with the Problem reports generated.

• Responsible for the production and assembly of Vacuum Swing Adsorption (VSA) systems for on-site oxygen concentration.• Preparation and implementation of routing and product manufacturing schedule.• Development and modification of electrical drawings as per customer requirements.• Troubleshooting of electrical connections and Variable Frequency Drives.• Developed a conceptualised model for the remote monitoring of equipment using Raspberry Pi

• Designed stochastic nonlinear Kalman filters for online estimation of states and parameters of a highly nonlinear aircraft system.• Developed an extended Kalman filter (EKF) for the flight path reconstruction of the aircraft.• An adaptive technique is incorporated into the EKF to adjust the value of process and measurement covariance matrices for more accurate estimates.• Developed an Unscented Kalman filter to address higher-order nonlinearities in the system and an adaptive technique was implemented for more accurate estimates.• The developed algorithms are used for the calibration of aircraft airdata system by formulating calibration as a state and parameter estimation problem.• The algorithm is also applied for the estimation of aerodynamic derivatives of the aircraft from the flight data and is established to be more accurate than conventional estimation techniques such as the Output Error Method.• The proposed algorithms are implemented on the simulated aircraft data as well as on actual flight test data.• A robust algorithm is incorporated into the adaptive filter where the measurement fault is detected, isolated and rectified by hypothesis driven approach.• A multilayered Neural Network is trained with the estimated results to yield highly accurate predictions from the sensor data.