Topic
CompactRIO
About: CompactRIO is a research topic. Over the lifetime, 246 publications have been published within this topic receiving 1096 citations.
Papers
TL;DR: Results of the experimental tests confirm that the multilayer NN, implemented in the FPGA with the use of the higher level programming language, ensures a high-quality state variable estimation of the two-mass drive system.
Abstract: This paper presents a practical realization of a neural network (NN)-based estimator of the load machine speed for a drive system with elastic coupling, using a reconfigurable field-programmable gate array (FPGA). The system presented is unique because the multilayer NN is implemented in the FPGA placed inside the NI CompactRIO controller. The neural network used as a state estimator was trained with the Levenberg-Marquardt algorithm. Special algorithm for implementation of the multilayer neural networks in such hardware platform is presented, focused on the minimization of the used programmable blocks of the FPGA matrix. The algorithm code for the neural estimator implemented in C-RIO was realized using the LabVIEW software. The neural estimators are tested: offline (based on the measured testing database) and online (in the closed-loop control structure). These estimators are tested also for changeable inertia moment of the load machine of the drive system with elastic joint. Presented results of the experimental tests confirm that the multilayer NN, implemented in the FPGA with the use of the higher level programming language, ensures a high-quality state variable estimation of the two-mass drive system.
155 citations
TL;DR: The paper presents the application of an adaptive neural controller used for speed control of electrical drives with elastic joint based on Adaptive Linear Neuron (ADALINE) model with on-line updated weights coefficients based on CompactRIO controller equipped with an FPGA chip.
Abstract: The paper presents the application of an adaptive neural controller used for speed control of electrical drives with elastic joint. The described project is realized in CompactRIO controller (cRIO-real-time embedded controller with reconfigurable input and output modules) equipped with an FPGA chip. The proposed speed controller is based on Adaptive Linear Neuron (ADALINE) model with on-line updated weights coefficients. The main advantages of the tested controller are simplicity and a reduced number of parameters for selection in the design process. Several stages of the real implementation are described. The two-mass drive system is modeled using the main processor of the cRIO, to emulate the real system, while the structure of the ADALINE model and its adaptation law are implemented in the FPGA module. Thus, hardware in the loop simulation is obtained. The obtained results present correct speed control with high dynamics and show the influence of the adaptation coefficient of the ADALINE-based controller on drive transients. Except for this the robustness of the proposed controller against changes of mechanical time constant of the load machine is presented.
47 citations
TL;DR: Test if FPGAs are able to achieve better position tracking performance than software-based soft real-time platforms using a Multi-state Fuzzy Logic controller implemented both in a Xilinx Virtex-II FPGA and in a NI CompactRIO-9002 platform.
Abstract: The main aim of this paper is to test if FPGAs are able to achieve better position tracking performance than software-based soft real-time platforms. For comparison purposes, the same controller design was implemented in these architectures. A Multi-state Fuzzy Logic controller (FLC) was implemented both in a Xilinx ® Virtex-II FPGA (XC2v1000) and in a soft real-time platform NI CompactRIO ® - 9002 . The same sampling time was used. The comparative tests were conducted using a servo-pneumatic actuation system. Steady-state errors lower than 4 μm were reached for an arbitrary vertical positioning of a 6.2 kg mass when the controller was embedded into the FPGA platform. Performance gains up to 16 times in the steady-state error, up to 27 times in the overshoot and up to 19.5 times in the settling time were achieved by using the FPGA-based controller over the software-based FLC controller.
42 citations
TL;DR: In this article, a research ECU for the 2004 Yamaha YZF-R6 motorcycle was developed, which relies heavily on field programmable gate arrays (FPGAs).
Abstract: A research ECU for the 2004 Yamaha YZF-R6 motorcycle was developed. Engine control, which involves fuel and spark events control, depends heavily on field programmable gate arrays (FPGAs). For the engine-control algorithm development as well as road testing, a National Instruments (NI) CompactRIO embedded control system was selected because of its flexibility, small size, and rugged form factor. With this system, sensors and actuators were added while readily visualizing the data. The unique computational feature of the CompactRIO system is that it includes both a real-time processor and an FPGA. Both devices are programmable using the Lab VIEW graphical development environment. With this combined architecture, multiple control approaches and algorithms can be quickly designed and tested on the motorcycle. The modified motorcycle was test driven extensively. Experienced drivers could not detect significant differences between the factory ECU and CompactRIO control system
41 citations
15 Jun 2015
TL;DR: The experimental results show the efficiency of the proposed methodology through the accuracy in the gain and phase margins of the PID control system compared to the specified ones and tracking of the reference trajectory.
Abstract: A robust fuzzy control design, with time delay, based on gain and phase margins specifications for nonlinear systems, in the continuous time delay, is proposed. From input and output data of the process, a Fuzzy C-Means (FCM) clustering algorithm estimates the antecedent parameters and the rules number of a Takagi-Sugeno fuzzy model, whereas the least squares algorithm estimates the consequent parameters. A multiobjective genetic strategy is developed to tune the fuzzy digital controller parameters, so the gain and phase specified margins are obtained for the fuzzy control system. The fuzzy PID controller was implemented on a real time acquisition data platform, based on CompactRIO (NI cRIO-9073) and LabVIEW, from National Instruments, for temperature control of a thermic process. The experimental results show the efficiency of the proposed methodology through the accuracy in the gain and phase margins of the PID control system compared to the specified ones and tracking of the reference trajectory. Fuzzy PID controller also is more efficient when compared to the fuzzy delay and lead compensator.
37 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 4 |
| 2020 | 3 |
| 2019 | 12 |
| 2018 | 21 |
| 2017 | 21 |
| 2016 | 25 |