I contributed to the project titled "Anomaly Detection and Countermeasures in Cyber-Physical System using Advanced Control and Estimation Techniques," sponsored by DST-SERB, India. This project focused on implementing human detection and tracking within a Cyber-Physical System using OpenCV and DeepSORT, enabling continuous target tracking with unique identifiers across frames. By integrating Lidar sensor data with camera-based detection, the system dynamically located human targets relative to the robot's position and orientation. A closed-loop Sliding Mode Control (SMC) trajectory algorithm was developed, leveraging feedback from odometry, IMU, and Lidar sensors to adjust linear and angular velocities based on human position, achieving precise navigation and real-time target following. The trajectory control algorithm was simulated in Gazebo to validate safety and performance before deployment on the TurtleBot3 Waffle-Pi using ROS2, ensuring smooth, controlled movement in real-world environments. Additionally, the system's resilience was enhanced with a fault detection mechanism utilizing a Sliding Mode Observer for state estimation, complemented by an adaptive threshold algorithm that dynamically compared estimated and measured states, successfully identifying faults in dynamic environments.