Project

Project 2. Development of real-time intelligent controllers for Unmanned Aircraft Systems (UAS) and other robotic systems

Due to recent advances in technology and the formalization of clearer regulations, Unmanned Aircraft systems (UAS) are being proposed for safely executing different kind of applications considered to be too risky for humans. One of the most versatile UAS platforms is the quad rotorctraft, a vehicle with Vertical Takeoff and Landing (VTOL) and hovering capabilities. Due to their popularity, UAS attract enormous interests from diverse research communities.

My proposed approach, is numerically and experimentally validated at the Unmanned Systems Laboratory from the University of Nevada Reno, on an aerial robot correspond to the Bepob drone, manufactured by Parrot where these results demonstrated the effectiveness of my proposed approach, as well as its applicability to real-time systems. Ultimately, I proved that my proposed methodology guarantees the convergence to intelligent tracking control of UAS asymptotically.

Related Publications

[A10] Reyhanoglu, M., Jafari, M., & Rehan, M. (2022). Simple Learning-Based Robust Trajectory Tracking Control of a 2-DOF Helicopter System, Electronics, 11(13), 2075.

[A9] Vakilinia, I., Jafari, M., Tosh, D., & Vakilinia, S. (2020, October). Privacy Preserving Path Planning in an Adversarial Zone. In 2020 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE.

[A8] Jafari, M., Xu, H., & Garcia Carrillo, L. R. (2019). A neurobiologically-inspired intelligent trajectory tracking control for unmanned aircraft systems with uncertain system dynamics and disturbance. Transactions of the Institute of Measurement and Control, 41(2), 417-432.

[A7] Jafari, M., & Xu, H. (2018). Intelligent control for unmanned aerial systems with system uncertainties and disturbances using artificial neural network. Drones, 2(3), 30.

[A6] Nourmohammadi, A., Jafari, M., & Zander, T. O. (2018). A survey on unmanned aerial vehicle remote control using brain–computer interface. IEEE Transactions on Human-Machine Systems, 48(4), 337-348.

[A5] Jafari, M., Xu, H., & Carrillo, L. R. G. (2018, November). Brain emotional learning-based path planning and intelligent control co-design for unmanned aerial vehicle in presence of system uncertainties and dynamic environment. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1435-1440). IEEE.

[A4] Jafari, M., & Xu, H. (2018, June). Adaptive neural network based intelligent control for unmanned aerial systems with system uncertainties and disturbances. In 2018 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1010-1016). IEEE.

[A3] Jafari, M., Fehr, R., Carrillo, L. R. G., & Xu, H. (2017, June). Brain emotional learning-based intelligent tracking control for unmanned aircraft systems with uncertain system dynamics and disturbance. In 2017 International conference on unmanned aircraft systems (ICUAS) (pp. 1470-1475). IEEE.

[A2] Jafari, M., Shahri, A. M., & Shouraki, S. B. (2013, August). Attitude control of a quadrotor using brain emotional learning based intelligent controller. In 2013 13th Iranian Conference on Fuzzy Systems (IFSC) (pp. 1-5). IEEE.

[A1] Jafari, M. (2012). Controlling a Quadrotor Using Brain Emotional Learning Based Intelligent Controller (Master's thesis).