Behzad Ahi was born in Tehran, Iran, in 1991. Since July 2023, as an assistant professor, he joined the Electrical Engineering Department of Sharif University of Technology, Tehran, Iran. Prior to joining the faculty, he was a postdoc researcher granted by Iran National Science Foundation (INSF) from July 2020 to March 2022. He is currently director of Data Fusion and Control Systems Lab.
Electrical Engineering from the Sharif University of Technology, Tehran, Iran, (2009-2013)
Electrical Engineering from the Sharif University of Technology, Tehran, Iran, (2013-2015)
Electrical Engineering from the Sharif University of Technology, Tehran, Iran, (2015-2019)
Sensor fusion is the process of integrating data from multiple sensors to produce more accurate, reliable, and comprehensive information than what could be obtained from any single sensor alone. This technique leverages the strengths of different sensors, compensating for their individual limitations. For example, in autonomous vehicles, sensor fusion combines data from cameras, radar, and lidar to create a detailed and accurate representation of the vehicle’s surroundings, enhancing safety and navigation. Deep learning and Kalman filters are the well-known tools in sensor fusion. By merging diverse data sources, sensor fusion improves decision-making in a wide range of applications, including robotics, navigation, tracking, healthcare, and environmental monitoring. In our lab, we focus on applications of sensor fusion in robotics and autonomous vehicles.
An autonomous vehicle (AV), is designed to navigate and operate without human intervention by utilizing a combination of sensors, cameras, radar, and artificial intelligence. Using a combination of sensors (e.g., radar, lidar, camera), AVs must accurately detect and respond to obstacles, pedestrians, and other vehicles, and handle complex scenarios (such as merging onto highways), ensuring safety in diverse and unpredictable driving conditions. In modular design viewpoint, the problem is break down into 5 category of navigation, perception, prediction, planning and control.
Besides the supervised and unsupervised learnings, the reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with its environment and receiving feedback in the form of rewards or penalties. The agent aims to maximize its cumulative reward over time by exploring different actions and learning from the outcomes. This trial-and-error approach allows the agent to develop optimal strategies for complex tasks (e.g., playing hard games). However, we are interested in applications of RL in control of uncertain control systems.
Guidance, navigation, and control (GNC) module is the main core in each flying vehicles (e.g, aircraft and drones). Navigation answers the question of “where the vehicles is in the word?”. To answer this question, we need to integrate various sensors such as (accelerometer, gyroscope, GPS, magnetometer, vision, lidar, etc). Guidance answers the question of “How to get there?”. To solve guidance problems, we use the well-known control techniques, such as those founded in adaptive and optimal control. The control strategy used in the inner loop of an aerial vehicles is called as “autopilot”. In designing autopilots, we typically use the nonlinear control techniques.
Secure control involves designing control systems that are resilient against cyber threats and unauthorized access, ensuring the integrity, confidentiality, and availability (CIA) of the system. To well-known approaches to ensure the privacy of date are using the homomorphic encryption (HE), and the multi-party computations (MPC). Secure control systems can enhance privacy and security, preventing unauthorized access and ensuring that the system operates safely and reliably even in the presence of potential cyber-attacks.
JOURNAL PAPERS :
[1] B. Ahi and A. Nobakhti. “Hardware Implementation of an ADRC Controller on a Gimbal Mechanism”, IEEE Transactions on Control Systems Technology, vol. 26, pp. 2268-2275, 2017.
[2] B. Ahi and M. Haeri. “Linear Active Disturbance Rejection Control from the Practical Aspects”, IEEE/ASME Transactions on Mechatronics, vol. 23, pp. 2909-2919, 2018.
[3] S. Amini, B. Ahi and M. Haeri. “Control of High Order Integrator Chain Systems Subjected to Disturbance and Saturated Control: A New Adaptive Scheme”, Automatica, vol. 100, pp. 108-113, 2019.
[4] S. Adelipour, B. Ahi and M. Haeri. “Dual-mode global stabilization of high-order saturated integrator chains: LMI-based MPC combined with a nested saturated feedback”, Nonlinear Dynamics, vol. 102, pp. 211-222, 2020.
[5] B. Ahi and M. Haeri. “A High-Performance Guidance Filter Scheme with Exact Dynamic Modeling of a Pitch-Yaw Gimballed Seeker Mechanism”, Mechanical Systems and Signal Processing, vol. 144, p. 106857, 2020.
[6] B. Ahi and M. Haeri. “Novel Command to Line of Sight Guidance with Practical Limitations”, Asian Journal of Control, vol. 24, pp. 1426-1436, 2022.
[7] B. Ahi and M. Haeri. “Practical Distributed Maneuvering Target Tracking Using Delayed Information of Heterogeneous Unregistered Sensors”, Signal Processing, vol. 193, p. 108419, 2022.
CONFERENCE PAPERS :
[1] S. Amini, B. Ahi and M. Haeri. “Nested Saturation Control Based on the Extended State Observer: Twin Rotor MIMO System”, 5th International Conference on Control, Instrumentation, and Automation (ICCIA), Shiraz, Iran. pp.55-59, 2017.
[2] B. Ahi and M. Haeri. “Novel Nested Saturated Feedback Scheme for CLOS Guidance via Cubature Kalman Filter”, 25th International Conference on Integrated Navigation Systems (ICINS), Saint Petersburg, Russia, pp.55-59, 2018.
[3] B. Ahi and M. Haeri. “Active Disturbance Rejection Control Versus Robust Adaptive Sliding Mode: Case Study of Variable-Length Pendulum”, 20th Russian Conference of Young Scientists “Navigation and Motion Control”, Saint Petersburg, Russia, pp. 47-49, 2018.
[4] B. Ahi, S. Adelipour and M. Haeri “A Dual Mode Control Scheme for Global Stabilization of Multiple Integrators Subjected to Disturbance and Bounded Input”, 21th Russian Conference of Young Scientists “Navigation and Motion Control”, Saint Petersburg, Russia, pp. 288-290, 2019.
(+98)-21-66165982
ahi@sharif.edu
https://ee.sharif.edu/~ahi
3th Floor - 315 East, Department of Electrical Engineering, Sharif University of Technology, Azadi Ave., P.O. Box 1155-4363, Tehran, Iran