Date of Award

2025

Degree Name

Mechanical Engineering

College

College of Engineering and Computer Sciences

Type of Degree

M.S.M.E.

Document Type

Thesis

First Advisor

Dr. Yousef Sardahi

Second Advisor

Dr. Asad Salem

Third Advisor

Dr.Gang Chen

Abstract

This thesis investigates the design of many-objective optimal controllers for quadcopter unmanned aerial vehicles (UAVs), focusing on both linear and hybrid control structures. In Chapter 2, a multi-input–multi-output (MIMO) optimal control system is developed for a six-degree-of-freedom UAV actuated by four brushless DC motors. The UAV dynamics are first derived and linearized around an operating point, and then organized into a three-loop nested control structure. The outer loop computes the roll and pitch angles required to maneuver the vehicle in the global X and Y directions, the middle loop regulates attitude and altitude, and the inner loop controls the angular velocities of each axis. A many-objective optimization problem, formulated with twenty design parameters and ten objective functions, is solved using the Strength Pareto Evolutionary Algorithm 2 with Shift-based Density Estimation (SPEA2-SDE). Stability and performance constraints are imposed, and numerical simulations conducted on the nonlinear UAV model confirm the effectiveness of the optimized design.

Chapter 3 extends this framework by presenting a hybrid cascade control structure that blends linear and nonlinear algorithms. The UAV’s nonlinear model is again derived and linearized at an operating point to design position and attitude controllers. The outer loop controllers (XG and Y G) generate roll and pitch commands, while the middle loop computes attitude dynamics under the assumption of small Euler angles. In contrast to Chapter 2, the inner loop employs a sliding mode controller (SMC) to generate motor thrust control laws, enhancing robustness against uncertainties and external disturbances. A corresponding many-objective optimization problem, involving twenty-one design parameters and ten objectives, is formulated and solved using the Hypervolume Estimation (HypE) algorithm. By ranking solutions through hypervolume contributions, HypE effectively captures trade-offs among competing objectives. The resulting Pareto sets and fronts demonstrate the inherent competition between performance measures. Simulations on the nonlinear UAV model validate the superiority of the optimized hybrid control design in terms of robustness, disturbance rejection, and overall trajectory tracking performance.

Overall, this thesis demonstrates that many-objective optimization, when applied to both linear and hybrid control structures, provides a systematic and effective approach for enhancing quadcopter stability, robustness, and performance in complex dynamic environments. Furthermore, the results show the hybrid controller with HypE outperforms the linear controller with SPEA2+SDE, exhibiting enhanced capability to attain the target point while efficiently preserving stability and robustness during the flight process.

Subject(s)

Mechanical engineering.

Drone aircraft.

Algorithms.

Drone aircraft -- Stability.

Drone aircraft -- Performance.

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