Acoustics, Dynamics, and Controls Commons^™

Trajectory-Based Model Predictive Control For Rotary Crane Sway Suppression With Only Horizontal Boom Actuation, Haryson J. Nyobuya

Tanzania Journal of Engineering and Technology (TJET)

Safety is of biggest importance in construction and industrial activities involving rotary cranes. These cranes are huge and responsible of moving large parts or/and materials from different positions. Now operating these cranes tend to be subdue to pay load oscillations when moving the load for which is extremely dangerous when not suppressed. This study proposes the generation of trajectory using Model predictive control approach with reduced control horizon compared to state horizon. Reduced control horizon design reduces computational time for trajectory generation while maintaining the pay-load sways to a minimum. In this approach the complex dynamic model is reduced to …

Machine Learning-Enabled Safety-Critical Model Predictive Control For Uncertain Dynamical Systems, Hossein Nejatbakhsh Esfahani

All Dissertations

This dissertation explores the interactions between Model Predictive Control (MPC), Safety-critical Control, and Artificial Intelligence (AI)/Machine Learning (ML) methods, with a particular focus on Reinforcement Learning (RL) and Bayesian Optimization (BO). We then leverage AI/ML to address several challenges in the control design and safe operation of uncertain dynamical systems. In many applications, ranging from autonomous vehicles and robotics to energy systems and industrial processes, ensuring safety is as essential as satisfying control objectives. The use of Control Barrier Functions (CBFs) within the MPC framework recently has emerged as a powerful tool to guarantee safety by enforcing constraints in optimal …

Networked Systems: Synchronization, Decomposition And Transient Dynamics, Amirhossein Nazerian

Mechanical Engineering ETDs

This dissertation investigates synchronization, transient dynamics, control, and signal amplification in networked dynamical systems. First, it examines complete and cluster synchronization, including cases with large parametric mismatches, in both natural (e.g., brain networks, fireflies) and technological systems (e.g., power grids, robotics). Second, it studies transient dynamics in consensus processes, designing synchronization strategies that link reactivity, contraction theory, and Lyapunov exponents. Third, it addresses the computational limits of Model Predictive Control (MPC) for large networks by introducing an algebraic decomposition method that enables parallel online computation of smaller subproblems, enhancing MPC performance under hardware constraints. Finally, it analyzes the structural role …

Simulation-To-Hardware Validation Of Mpc For Binary Thruster 2d Cubesat Control, Aevar Amundinusarson Oefjoerd

Theses and Dissertations--Mechanical and Aerospace Engineering

Small satellites must execute precise, fuel-limited maneuvers under strict actuation and operational constraints. Model Predictive Control (MPC) is a suitable approach because it optimizes future actions while explicitly enforcing these limits. This work evaluates (1) whether MPC can operate in real time using binary (on/off), asymmetric thrusters, and (2) whether a high-fidelity digital twin can be used to tune and validate the controller prior to hardware testing.

An MPC framework was implemented on a planar satellite prototype that floats on air bearings, operates at 16.7 Hz (60 ms period), and uses eight binary thrusters exhibiting up to 13.2% variation in …

Development Of Autonomous Vehicle Motion Planning And Control Algorithm With D* Planner And Model Predictive Control In A Dynamic Environment, Somnath Mondal

Dissertations, Master's Theses and Master's Reports

The research in this report incorporates the improvement in the autonomous driving capability of self-driving cars in a dynamic environment. Global and local path planning are implemented using the D* path planning algorithm with a combined Cubic B-Spline trajectory generator, which generates an optimal obstacle free trajectory for the vehicle to follow and avoid collision. Model Predictive Control (MPC) is used for the longitudinal and the lateral control of the vehicle. The presented motion planning and control algorithm is tested using Model-In-the-Loop (MIL) method with the help of MATLAB® Driving Scenario Designer and Unreal Engine® Simulator by Epic Games®. Different …

Real-Time Predictive Control Of Connected Vehicle Powertrains For Improved Energy Efficiency, Joseph Oncken

Dissertations, Master's Theses and Master's Reports

The continued push for the reduction of energy consumption across the automotive vehicle fleet has led to widespread adoption of hybrid and plug-in hybrid electric vehicles (PHEV) by auto manufacturers. In addition, connected and automated vehicle (CAV) technologies have seen rapid development in recent years and bring with them the potential to significantly impact vehicle energy consumption. This dissertation studies predictive control methods for PHEV powertrains that are enabled by CAV technologies with the goal of reducing vehicle energy consumption.

First, a real-time predictive powertrain controller for PHEV energy management is developed. This controller utilizes predictions of future vehicle velocity …

Mpc-Based Autonomous Driving Control With Localized Path Planning For Obstacle Avoidance And Navigating Signalized Intersections, Sai Rajeev Devaragudi

Dissertations, Master's Theses and Master's Reports

Connected and autonomous vehicles are becoming the major focus of research for the industry and academia in the automotive field. Many companies and research groups have demonstrated the advantages and the requirement of such technology to improve the energy efficiency of vehicles, decrease the number of crash and road accidents, and control emissions.

This research delves into improving the autonomy of self-driving vehicles by implementing localized path planning algorithms to introduce motion control for obstacle avoidance during uncertainties. Lateral path planning is implemented using the A* algorithm combined with piecewise Bezier curve generation which provides an optimum trajectory reference to …

Model-Based Control Of An Rcci Engine, Akshat Abhay Raut

Dissertations, Master's Theses and Master's Reports

Reactivity controlled compression ignition (RCCI) is a combustion strategy that offers high fuel conversion efficiency and near zero emissions of NOx and soot which can help in improving fuel economy in mobile and stationary internal combustion engine (ICE) applications and at the same time lower engine-out emissions. One of the main challenges associated with RCCI combustion is the difficulty in simultaneously controlling combustion phasing, engine load, and cyclic variability during transient engine operations.

This thesis focuses on developing model based controllers for cycle-to-cycle combustion phasing and load control during transient operations. A control oriented model (COM) is developed by using …