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Reduced Computational Burden of Modulated Model-Predictive Control for Synchronous Reluctance Motor Drive Applications (2023)
Conference Proceeding

This paper introduces a novel geometric approach to significantly reduce the computational burden of modulated predictive controllers while maintaining the same steady-state performance and satisfactory dynamic behavior. The proposed geometric method... Read More about Reduced Computational Burden of Modulated Model-Predictive Control for Synchronous Reluctance Motor Drive Applications.

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview (2023)
Journal Article

The integration of artificial intelligence (AI) techniques in power converter-based systems has the potential to revolutionize the way these systems are optimized and controlled. With the rapid advancements in AI and machine learning technologies, th... Read More about Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview.

An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on Fast Recursive Algorithm: With Application to A Linear Motor (2022)
Journal Article

The feedforward control can effectively improve the servo performance in applications with high requirements of velocity and acceleration. The iterative feedforward tuning method (IFFT) enables the possibility of both removing the need for prior know... Read More about An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on Fast Recursive Algorithm: With Application to A Linear Motor.

A Direct Model Predictive Control Strategy for High-Performance Synchronous Reluctance Motor Drives (2021)
Conference Proceeding

This paper presents a finite control set model predictive control (FCS-MPC) method that improves the performance of a synchronous reluctance machine drive. As shown, when a high sampling-to-switching frequency ratio is used with FCS-MPC, the stator c... Read More about A Direct Model Predictive Control Strategy for High-Performance Synchronous Reluctance Motor Drives.

Model predictive Control of a Double Stage AC-DC Converter for Grid-Interface of Vanadium Flow Batteries (2021)
Conference Proceeding

This paper presents a multi-objective model predictive control algorithm to implement the control of a bidirectional double stage ac-dc power conversion system, used to interface a Vanadium redox flow battery with a power grid. The converter topology... Read More about Model predictive Control of a Double Stage AC-DC Converter for Grid-Interface of Vanadium Flow Batteries.

A Fixed Frequency Full-Bridge Three-Level DC-DC LCL-Type Series Resonant Converter for Large Scale Solar PV Plants Applications (2021)
Conference Proceeding

Large scale solar PV plants are typically connected into medium voltage AC grids using line frequency transformers, which are bulky. In application where size and/or weight are important an attractive alternative is the use of multilevel inverter con... Read More about A Fixed Frequency Full-Bridge Three-Level DC-DC LCL-Type Series Resonant Converter for Large Scale Solar PV Plants Applications.

Cogging Force Identification Based on Self-Adaptive Hybrid Self-Learning TLBO Trained RBF Neural Network for Linear Motors (2021)
Conference Proceeding

The cogging force arising due to the strong attraction forces between the iron core and the permanent magnets, is a common inherent property of the linear motors, which significantly affects the control performance. Therefore, significant research ef... Read More about Cogging Force Identification Based on Self-Adaptive Hybrid Self-Learning TLBO Trained RBF Neural Network for Linear Motors.