Zainab Nisar

Experienced Avionics and Electrical Engineer with over five years of multidisciplinary experience in simulation modelling, control systems, and research. Specialized in system identification and controller design for aircraft components including C-130 flight simulator systems. Adept at developing complex engineering solutions with minimal supervision, applying advanced tools like MATLAB/Simulink, and integrating machine learning for diagnostic and predictive tasks.

• Developed a nonlinear dynamic model and performed system identification for a hydraulic actuator in a C-130 flight simulator using MATLAB/Simulink, integrating black-box modelling techniques.
• Designed and tuned a nonlinear PID controller, validated against a high-fidelity Simscape model, demonstrating stability and robustness under varying load conditions.
• Addressed a critical real-world requirement of the Pakistan Air Force by contributing to the upgrade of the C-130 motion platform, culminating in the real-time implementation of a validated control algorithm within the operational simulator.
• Applied state-space control techniques to stabilize fixed-wing UAV models, including eigenvalue placement for rapid convergence and disturbance rejection.
• Implemented an LQR (Linear Quadratic Regulator) controller to enhance path tracking and energy efficiency in unmanned aerial systems.
• Developed adaptive control frameworks for quadrotor flight with dynamic payload changes, ensuring consistent performance across test scenarios.
• Built a lab-scale Stewart platform to simulate 6-DOF aircraft motion, analyzing kinematics and dynamic control stability under real-time conditions.
• Created and verified predictive collision avoidance algorithms using model predictive control (MPC) for autonomous vehicle applications with aviation analogs.
• Analyzed excitation signal properties for optimizing frequency content in system identifications experiments, enhancing model fidelity.
• Supervised Learning Algorithms for Health Diagnostics employing decision trees, SVMs, and ensemble techniques to analyze biomedical datasets for classification.
• MRI-Based Brain Tumour Detection System using MATLAB, combining histogram equalization, median filtering, and edge detection with Haar and Daubechies wavelet features for classification via SVM and k-NN.
• Image Preprocessing and Feature Extraction Pipeline for medical diagnostics, integrating morphological operations with advanced signal transforms to improve classifier performance.
• Model Validation Techniques for Nonlinear Systems through parameter estimation and residual minimization.
• Embedded Systems Co-Simulation using Simulink and C Code Generation for hardware-in-the-loop (HIL) testing environments.