Topic of Research Seminar: Disturbance Rejection and Roll over estimation for control of Non – Linear Robotic System

Abstract: Accurate state estimation is a foundational requirement for ensuring the safety, stability, and optimal performance of vehicles, whether they are ground vehicles or quadrotors. This research explores the critical realm of state estimation for both ground vehicles and quadrotors, addressing fundamental issues related to vehicle control, safety, and performance enhancement. In the domain of ground vehicles, the precise estimation of the roll angle is paramount for advanced applications, including active anti-roll bars. Traditional methods for attitude estimation have been computationally intensive and reliant on costly techniques like dual antenna global positioning systems (GPS). To tackle this challenge, this research employs a multi-phase approach. In the first phase, 3-Dof vehicle roll dynamics model is deployed along with Leuenberger and Sliding Mode Observers to estimate the vehicle’s roll angle. The validation is performed against the commercial software Car Sim”. The second phase involves the implementation of Complementary and Kalman Filters for roll and pitch angle estimation, which are independently applied to measure data under different terrains at various frequencies. The dissertation culminates in the proposal of a cost-effective solution to mitigate the risk of vehicle rollovers, emphasizing the practicality and efficiency of the approach through reduction of root mean square error (RMSE) and sample time. Shifting focus to the domain of quadrotors, state and parameter estimation is equally crucial for stable flight, intricate maneuvers, and responsiveness to external disturbances. The fusion of state estimation with advanced control systems, particularly the Sliding Mode control scheme, is explored. Traditional gain tuning for nonlinear systems like quadrotors has been laborious, prompting the integration of Deep Reinforcement Learning (RL) techniques. A comprehensive 6-degree-of-freedom (6-DOF) nonlinear quadrotor model is employed, where aerodynamic coefficients are estimated using the Blade Element Momentum Theory (BEMT). Lyapunov theory and RL optimization are leveraged to ensure system stability, mitigating chattering effects in control inputs. Extensive simulations demonstrate the remarkable effectiveness of this approach, notably reducing the root mean square error (RMSE) during trajectory tracking. In summary, this dissertation represents a substantial stride forward in the fields of ground vehicle and quadrotor state estimation and control. It underscores the promise of efficient and practical solutions, contributing to the enhancement of safety and performance in these vehicle types. Furthermore, the integration of machine learning techniques exemplifies the potential for optimizing complex control systems, particularly in the context of Unmanned Aerial Vehicles (UAVs), with quadrotors as a specific focus. This research bridges the gap between theory and application, providing innovative solutions for real-world challenges in vehicle control and stability. Keywords: State Estimation, Attitude Estimation, Sensor Fusion, Leuenberger Observers, Sliding Mode Observers, Complementary Filters, Kalman Filters, Deep Reinforcement Learning (RL), Nonlinear Systems, 6-DOF Quadrotor Model, Blade Element Momentum Theory (BEMT), Lyapunov Theory, UAV (Unmanned Aerial Vehicle)

Name of Speaker: Kamal Mazhar Malik

Professorial Rank of Speaker: PhD Student

University Email of Speaker: [email protected]

Research Group Weblink: https://nust.edu.pk/

Affiliation of Speaker: School of Mechanical and Manufacturing Engineering (SMME – NUST)

Date and Venue: 12 Nov 2024 at 1750 hrs in SMME Seminar Hall, School of Mechanical and Manufacturing Engineering, NUST Islamabad