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Articles 1 - 6 of 6
Full-Text Articles in Engineering
Neural Network Predictive Controller For Grid-Connected Virtual Synchronous Generator, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi, Donald C. Wunsch
Neural Network Predictive Controller For Grid-Connected Virtual Synchronous Generator, Sepehr Saadatmand, Mohamad Saleh Sanjari Nia, Pourya Shamsi, Mehdi Ferdowsi, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a neural network predictive controller is proposed to regulate the active and the reactive power delivered to the grid generated by a three-phase virtual inertia-based inverter. The concept of the conventional virtual synchronous generator (VSG) is discussed, and it is shown that when the inverter is connected to non-inductive grids, the conventional PI-based VSGs are unable to perform acceptable tracking. The concept of the neural network predictive controller is also discussed to replace the traditional VSGs. This replacement enables inverters to perform in both inductive and non-inductive grids. The simulation results confirm that a well-trained neural network …
Optimal Direct Yaw Moment Control Of A 4wd Electric Vehicle, Winston James Wight
Optimal Direct Yaw Moment Control Of A 4wd Electric Vehicle, Winston James Wight
Master's Theses
This thesis is concerned with electronic stability of an all-wheel drive electric vehicle with independent motors mounted in each wheel. The additional controllability and speed permitted using independent motors can be exploited to improve the handling and stability of electric vehicles. In this thesis, these improvements arise from employing a direct yaw moment control (DYC) system that seeks to adapt the understeer gradient of the vehicle and achieve neutral steer by employing a supervisory controller and simultaneously tracking an ideal yaw rate and ideal sideslip angle. DYC enhances vehicle stability by generating a corrective yaw moment realized by a torque …
Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball
Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball
Master's Theses
Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum …
Optimal Control Strategies For Complex Biological Systems, Afroza Shirin
Optimal Control Strategies For Complex Biological Systems, Afroza Shirin
Mechanical Engineering ETDs
To better understand and to improve therapies for complex diseases such as cancer or diabetes, it is not sufficient to identify and characterize the interactions between molecules and pathways in complex biological systems, such as cells, tissues, and the human body. It also is necessary to characterize the response of a biological system to externally supplied agents (e.g., drugs, insulin), including a proper scheduling of these drugs, and drug combinations in multi drugs therapies. This obviously becomes important in applications which involve control of physiological processes, such as controlling the number of autophagosome vesicles in a cell, or regulating the …
Verification Of Stochastic Reach-Avoid Using Rkhs Embeddings, Adam J. Thorpe
Verification Of Stochastic Reach-Avoid Using Rkhs Embeddings, Adam J. Thorpe
Electrical and Computer Engineering ETDs
A solution to the terminal-hitting and first-hitting stochastic reach-avoid problem for a Markov control process is presented. This solution takes advantage of a nonparametric representation of the stochastic kernel as a conditional distribution embedding within a reproducing kernel Hilbert space (RKHS). Because the disturbance is modeled as a data-driven stochastic process, this representation avoids intractable integrals in the dynamic recursion of the reach-avoid problem since the expectations can be calculated as an inner product within the RKHS. An example using a high-dimensional chain of integrators is presented, as well as for Clohessy-Wiltshire-Hill (CWH) dynamics.
Sufficient Conditions For Optimal Control Problems With Terminal Constraints And Free Terminal Times With Applications To Aerospace, Sankalp Kishan Bhan
Sufficient Conditions For Optimal Control Problems With Terminal Constraints And Free Terminal Times With Applications To Aerospace, Sankalp Kishan Bhan
McKelvey School of Engineering Theses & Dissertations
Motivated by the flight control problem of designing control laws for a Ground Collision Avoidance System (GCAS), this thesis formulates sufficient conditions for a strong local minimum for a terminally constrained optimal control problem with a free-terminal time. The conditions develop within the framework of a construction of a field of extremals by means of the method of characteristics, a procedure for the solution of first-order linear partial differential equations, but modified to apply to the Hamilton-Jacobi-Bellman equation of optimal control. Additionally, the thesis constructs these sufficient conditions for optimality with a mathematically rigorous development. The proof uses an approach …