Brian MacCleery

New Mechatronics Development Techniques for FPGA-Based Control and Simulation of Electromechanical Systems

Abstract Field programmable gate arrays (FPGAs) have been widely adopted in high volume commercial applications, but not as much in the industrial control and simulation arenas. Due to the attractive features of FPGAs, such as their inherent flexibility, performance, parallelism, and low-level reconfigurability, industrial control design and simulation vendors have been creating the next generation FPGA development tool chains that are designed for engineers with little or no digital design expertise. The goal of these next generation system-level design tools is to empower control design, simulation, and signal processing engineers to harness the full power of the FPGA technology, while providing relatively competitive performance and resource usage, as compared to traditional text-based hardware description level (HDL) methods. This paper discusses some of the traditional challenges that prohibited wide adoption of FPGAs in the industrial control and simulation fields, and how new graphical system design tools are helping mechatronics engineers leverage the full power of FPGAs as deployment platforms. Moreover, it discusses some particularly useful development techniques for FPGA-based control and simulation in mechatronics applications.

A new platform and methodology for system-level design of next-generation FPGA-based digital SMPS

Increasing adoption of FPGAs in digital switched-mode power supply (SMPS) control is driving significant interest in improved platforms for development and commercial deployment. A new methodology for high-level system design of SMPS controllers is proposed and developed commercially. The feasibility, validity and accuracy of the approach is evaluated through the design and testing of a single-phase and three-phase DC-to-AC inverter. Simulation and experimental data are compared to demonstrate the high accuracy of the novel continuous time co-simulation interface. The user defined FPGA software, developed without requiring any knowledge of HDL languages, is then transferred to a COTS FPGA-chip-on-board SMPS control system for high-volume commercial deployment. The same graphical programming tools are used to develop an FPGA-based real-time SMPS hardware-in-the-loop (HIL) simulator for exhaustive validation testing of the control system hardware and software.

Integrated Intelligent Control and Fault System for Wind Generators

The goal of this paper is to show the possibility of combining fault detection analysis, detection, modeling, and control of the doubly-fed induction generator (DFIG) wind turbine using intelligent control and diagnostic techniques. To enable online detection of problems inside the power electronics converter we apply the wave direct analysis method which enables a complete model for fault detection that includes the power electronic stage itself. A neural network system based on Hebbian networks is applied for fault classification with good detection results in simulation. For controlling the wind turbine a number different artificial intelligence techniques are presented including fuzzy logic and an adaptive fuzzy inference systems (ANFIS) which combines the characteristics of fuzzy logic and neural networks. A Grey predictor is also integrated in the control scheme for predicting the wind profile. The combined fault detection and control scheme are validated using simulation results. The software devel...

Fuzzy Logic Type 1 and Type 2 LabVIEW FPGA Toolkit

This chapter presents the toolkit developed for LabVIEW FPGA, so that it is possible to implement Fuzzy Logic systems in LabVIEW FPGA in a fast way by the Toolkit. The main blocks can be used for implementing complex fuzzy logic control systems that can be adjusted to different applications according to the user needs. The theoretical part was covered in chapter one; thus this chapter deals with the implementation of fuzzy logic systems.

Real Time Simulation for DC and AC Motors Based on LabVIEW FPGAs

Abstract Increased manufacturing expenses and increased competition have made it an automation process to improve the overall operational and energy efficiency at the manufacturing systems. Electric motors impact a wide range of manufacturing equipment, especially the manufacturing equipment that has an advance level of automation. An accurate real time simulation is required for predicting the high-quality motor performance and making the motor systems more reliable and easier to use inside of the complete manufacturing system. FPGAs for real simulation have become both increasingly powerful and increasingly affordable. As a result, the use of highly sophisticated hardware that not only could run high-fidelity simulation of electric motors but also this hardware allows getting result in real time. This paper shows real time simulation models of DC and AC machines on FPGAs in state space. Implementation of the real time numerical integration method with digital logic elements is presented and discussed using fixed point. A real-time simulation of DC and AC machines is carried out on FPGAs platform and real time results are shown. These results are compared to simulation results obtained through commercial off line simulation software.

Software-defined intelligent grid research integration and development platform

A complication to control schemes is the fact that models can only represent the systems to the fidelity of the model. There is a need for hardware testing which can truly represent the dynamics of the system and the integration of the controls. A flexible and reconfigurable hardware low-power microgrid testbed platform is proposed in this paper for the verification of control schemes, protection and cyber-security. The design of multi-microgrid hardware configuration, shadow network architecture, and data collection and analytics are discussed.

Literature Review for Digital Implementations of Fuzzy Logic Type-1 and Type-2

Some works are described below where optimization Type-1 and Type-2 FLS have had relative success according to different areas, illustrating the advantages of using methods to automate process with fuzzy controllers.

Real-Time Fuzzy Logic Controllers

Nonlinear control involves a nonlinear relationship between the controller’s inputs and outputs and is more complicated than linear control; however, it is able to achieve better performance than linear control for many real-world control applications.

Grid-side inverter thermal cycling analysis of 1.6 MW Doubly-Fed Induction Generation wind turbine and life-time estimation

This paper presents a simulation of wind turbine driven by a Doubly-Fed Induction Generator (DFIG), the rotor is fed through a two level inverter using Space Vectors Pulse Width Modulation (SVM). LabVIEW and Multisim are used to co-simulate the control and power electronics steps respectively. The thermal models of Multisim are used in order to analyze the thermal performance of IGBTs under different zones of operation of the electric machine. After that, the lifetime of the power electronics devices is estimated using the modified Coffin-Manson law of the LESIT project. The work demonstrate that the most demanding conditions are at nominal power of the converter while for underrated condition the damage due to thermal loading is minimum. This could be exploit increasing the switching frequency in order to enhance power output to the electric grid.

Soft Computing Based on LabVIEW

Abstract When we are dealing with online dynamical systems, i.e. Adaptive-Network-Based Fuzzy Inference Systems (ANFIS), one of the major problems is the time used into the learning process. In this way, Backpropagation algorithm is used for training the premise parameters in a hybrid learning procedure. However, it depends on parameters that must be tuned heuristically. This provides that, in some way, training is not useful completely. Nevertheless, fuzzy logic can be put into practice and assume parameters involved in the backpropagation algorithm could be treated as fuzzy elements. In this paper, we implement fuzzy logic controllers inside the backpropagation algorithm and expand this model for training Neural Networks and ANFIS to achieve convergence in short periods of time in order to decrease the time process on-line. At this point, LabVIEW will be used as platform to validate this approach.