Mahmut Reyhanoglu

By Mahmut Reyhanoglu

Part of the book: Advances in Spacecraft Technologies

By Mahmut Reyhanoglu and Muhammad Rehan

Aerial robotics is one of the fastest growing industry and has a number of evolving applications. Higher agility make aerial robots ideal candidate for applications like rescue missions especially in difficult to access areas. This chapter first derives the complete nonlinear dynamics of an aerial robot consisting of a quadcopter with a two-link robot manipulator. Precise control of such an aerial robot is a challenging task due to the fact that the translational and rotational dynamics of the quadcopter are strongly coupled with the dynamics of the manipulator. We extend our previous results on the control of quadrotor UAVs to the control of aerial robots. In particular, we design a backstepping and Lyapunov-based nonlinear feedback control law that achieves point-to-point control of the areal robot. The effectiveness of this feedback control law is illustrated through a simulation example.

Part of the book: Aerial Robots

By William MacKunis, Mahmut Reyhanoglu and Krishna Bhavithavya Kidambi

Adaptive nonlinear control of self-excited oscillations in Rijke-type thermoacoustic systems is considered. To demonstrate the methodology, a well-accepted thermoacoustic dynamic model is introduced, which includes arrays of sensors and monopole-like actuators. To facilitate the derivation of the adaptive control law, the dynamic model is recast as a set of nonlinear ordinary differential equations, which are amenable to control design. The control-oriented nonlinear model includes unknown, unmeasurable, nonvanishing disturbances in addition to parametric uncertainty in both the thermoacoustic dynamic model and the actuator dynamic model. To compensate for the unmodeled disturbances in the dynamic model, a robust nonlinear feedback term is included in the control law. One of the primary challenges in the control design is the presence of input-multiplicative parametric uncertainty in the dynamic model for the control actuator. This challenge is mitigated through innovative algebraic manipulation in the regulation error system derivation along with a Lyapunov-based adaptive control law. To address practical implementation considerations, where sensor measurements of the complete state are not available for feedback, a detailed analysis is provided to demonstrate that system observability can be ensured through judicious placement of pressure (and/or velocity) sensors. Based on this observability condition, a sliding-mode observer design is presented, which is shown to estimate the unmeasurable states using only the available sensor measurements. A detailed Lyapunov-based stability analysis is provided to prove that the proposed closed-loop active thermoacoustic control system achieves asymptotic (zero steady-state error) regulation of multiple thermoacoustic modes in the presence of the aforementioned model uncertainty. Numerical Monte Carlo-type simulation results are also provided, which demonstrate the performance of the proposed closed-loop control system under various sets of operating conditions.

Part of the book: Adaptive Robust Control Systems

By Mahmut Reyhanoglu

Part of the book: Acoustic Emission

By Mahmut Reyhanoglu

Part of the book: Advances in Robust Control and Applications

By Mohammad Jafari and Mahmut Reyhanoglu

This chapter presents a learning-enhanced robust control framework for a broad class of nonlinear autonomous systems whose dynamics can be expressed in a feedback linearizable form. The core idea is to enhance classical feedback linearization with adaptive mechanisms that compensate for unknown disturbances and model uncertainties without requiring persistent excitation or detailed disturbance models. The method is grounded in nonlinear control theory and uses gradient descent learning rules to continuously adapt control parameters and disturbance estimates. The resulting closed-loop system achieves robust trajectory tracking while maintaining simplicity and interpretability in both implementation and analysis.

Part of the book: Modeling and Control of Autonomous Systems

By Mahmut Reyhanoglu

Part of the book: Modeling and Control of Autonomous Systems