Field programmable gate array based predictive control system for spacecraft rendezvous in elliptical orbits

Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd. Summary A field programmable gate array (FPGA) based model predictive controller for two phases of spacecraft rendezvous is presented. Linear time-varying prediction models are used to accommodate elliptical orbits, and a variable prediction horizon is used to facilitate finite time completion of the longer range manoeuvres, whilst a fixed and receding prediction horizon is used for fine-grained tracking at close range. The resulting constrained optimisation problems are solved using a primal-dual interior point algorithm. The majority of the computational demand is in solving a system of simultaneous linear equations at each iteration of this algorithm. To accelerate these operations, a custom circuit is implemented, using a combination of Mathworks HDL Coder and Xilinx System Generator for DSP, and used as a peripheral to a MicroBlaze soft-core processor on the FPGA, on which the remainder of the system is implemented. Certain logic that can be hard-coded for fixed sized problems is implemented to be configurable online, in order to accommodate the varying problem sizes associated with the variable prediction horizon. The system is demonstrated in closed-loop by linking the FPGA with a simulation of the spacecraft dynamics running in Simulink on a PC, using Ethernet. Timing comparisons indicate that the custom implementation is substantially faster than pure embedded software-based interior point methods running on the same MicroBlaze and could be competitive with a pure custom hardware implementation.

Low power field programmable gate array implementation of fast digital signal processing algorithms: characterisation and manipulation of data locality

Dynamic power consumption is very dependent on interconnect, so clever mapping of digital signal processing algorithms to parallelised realisations with data locality is vital. This is a particular problem for fast algorithm implementations where typically, designers will have sacrificed circuit structure for efficiency in software implementation. This study outlines an approach for reducing the dynamic power consumption of a class of fast algorithms by minimising the index space separation; this allows the generation of field programmable gate array (FPGA) implementations with reduced power consumption. It is shown how a 50% reduction in relative index space separation results in a measured power gain of 36 and 37% over a Cooley-Tukey Fast Fourier Transform (FFT)-based solution for both actual power measurements for a Xilinx Virtex-II FPGA implementation and circuit measurements for a Xilinx Virtex-5 implementation. The authors show the generality of the approach by applying it to a number of other fast algorithms namely the discrete cosine, the discrete Hartley and the Walsh-Hadamard transforms.

System-on-chip field-programmable gate array design for onboard real-time hyperspectral unmixing

Hyperspectral instruments have been incorporated in satellite missions, providing large amounts of data of high spectral resolution of the Earth surface. This data can be used in remote sensing applications that often require a real-time or near-real-time response. To avoid delays between hyperspectral image acquisition and its interpretation, the last usually done on a ground station, onboard systems have emerged to process data, reducing the volume of information to transfer from the satellite to the ground station. For this purpose, compact reconfigurable hardware modules, such as field-programmable gate arrays (FPGAs), are widely used. This paper proposes an FPGA-based architecture for hyperspectral unmixing. This method based on the vertex component analysis (VCA) and it works without a dimensionality reduction preprocessing step. The architecture has been designed for a low-cost Xilinx Zynq board with a Zynq-7020 system-on-chip FPGA-based on the Artix-7 FPGA programmable logic and tested using real hyperspectral data. Experimental results indicate that the proposed implementation can achieve real-time processing, while maintaining the methods accuracy, which indicate the potential of the proposed platform to implement high-performance, low-cost embedded systems, opening perspectives for onboard hyperspectral image processing.

Contribuição para o estudo do embarque de uma rede neural artificial em field programmable gate array (FPGA)

This study shows the implementation and the embedding of an Artificial Neural Network (ANN) in hardware, or in a programmable device, as a field programmable gate array (FPGA). This work allowed the exploration of different implementations, described in VHDL, of multilayer perceptrons ANN. Due to the parallelism inherent to ANNs, there are disadvantages in software implementations due to the sequential nature of the Von Neumann architectures. As an alternative to this problem, there is a hardware implementation that allows to exploit all the parallelism implicit in this model. Currently, there is an increase in use of FPGAs as a platform to implement neural networks in hardware, exploiting the high processing power, low cost, ease of programming and ability to reconfigure the circuit, allowing the network to adapt to different applications. Given this context, the aim is to develop arrays of neural networks in hardware, a flexible architecture, in which it is possible to add or remove neurons, and mainly, modify the network topology, in order to enable a modular network of fixed-point arithmetic in a FPGA. Five synthesis of VHDL descriptions were produced: two for the neuron with one or two entrances, and three different architectures of ANN. The descriptions of the used architectures became very modular, easily allowing the increase or decrease of the number of neurons. As a result, some complete neural networks were implemented in FPGA, in fixed-point arithmetic, with a high-capacity parallel processing

Field programmable gate array based target detection and gesture recognition

The move from Standard Definition (SD) to High Definition (HD) represents a six times increases in data, which needs to be processed. With expanding resolutions and evolving compression, there is a need for high performance with flexible architectures to allow for quick upgrade ability. The technology advances in image display resolutions, advanced compression techniques, and video intelligence. Software implementation of these systems can attain accuracy with tradeoffs among processing performance (to achieve specified frame rates, working on large image data sets), power and cost constraints. There is a need for new architectures to be in pace with the fast innovations in video and imaging. It contains dedicated hardware implementation of the pixel and frame rate processes on Field Programmable Gate Array (FPGA) to achieve the real-time performance. ^ The following outlines the contributions of the dissertation. (1) We develop a target detection system by applying a novel running average mean threshold (RAMT) approach to globalize the threshold required for background subtraction. This approach adapts the threshold automatically to different environments (indoor and outdoor) and different targets (humans and vehicles). For low power consumption and better performance, we design the complete system on FPGA. (2) We introduce a safe distance factor and develop an algorithm for occlusion occurrence detection during target tracking. A novel mean-threshold is calculated by motion-position analysis. (3) A new strategy for gesture recognition is developed using Combinational Neural Networks (CNN) based on a tree structure. Analysis of the method is done on American Sign Language (ASL) gestures. We introduce novel point of interests approach to reduce the feature vector size and gradient threshold approach for accurate classification. (4) We design a gesture recognition system using a hardware/ software co-simulation neural network for high speed and low memory storage requirements provided by the FPGA. We develop an innovative maximum distant algorithm which uses only 0.39% of the image as the feature vector to train and test the system design. Database set gestures involved in different applications may vary. Therefore, it is highly essential to keep the feature vector as low as possible while maintaining the same accuracy and performance^

Experiences with implementing common mathematical operations using field programmable gate arrays

Many computationally intensive scientific applications involve repetitive floating point operations other than addition and multiplication which may present a significant performance bottleneck due to the relatively large latency or low throughput involved in executing such arithmetic primitives on commod- ity processors. A promising alternative is to execute such primitives on Field Programmable Gate Array (FPGA) hardware acting as an application-specific custom co-processor in a high performance reconfig- urable computing platform. The use of FPGAs can provide advantages such as fine-grain parallelism but issues relating to code development in a hardware description language and efficient data transfer to and from the FPGA chip can present significant application development challenges. In this paper, we discuss our practical experiences in developing a selection of floating point hardware designs to be implemented using FPGAs. Our designs include some basic mathemati cal library functions which can be implemented for user defined precisions suitable for novel applications requiring non-standard floating point represen- tation. We discuss the details of our designs along with results from performance and accuracy analysis tests.

Solving tri-diagonal linear systems using field programmable gate arrays

In this paper, we present the outcomes of a project on the exploration of the use of Field Programmable Gate Arrays(FPGAs) as co-processors for scientific computation. We designed a custom circuit for the pipelined solving of multiple tri-diagonal linear systems. The design is well suited for applications that require many independent tri diagonal system solves, such as finite difference methods for solving PDEs or applications utilising cubic spline interpolation. The selected solver algorithm was the Tri Diagonal Matrix Algorithm (TDMA or Thomas Algorithm). Our solver supports user specified precision thought the use of a custom floating point VHDL library supporting addition, subtraction, multiplication and division. The variable precision TDMA solver was tested for correctness in simulation mode. The TDMA pipeline was tested successfully in hardware using a simplified solver model. The details of implementation, the limitations, and future work are also discussed.

Solving tri-diagonal linear systems using field programmable gate arrays

In this paper, we present the outcomes of a project on the exploration of the use of Field Programmable Gate Arrays (FPGAs) as co-processors for scientific computation. We designed a custom circuit for the pipelined solving of multiple tri-diagonal linear systems. The design is well suited for applications that require many independent tri-diagonal system solves, such as finite difference methods for solving PDEs or applications utilising cubic spline interpolation. The selected solver algorithm was the Tri-Diagonal Matrix Algorithm (TDMA or Thomas Algorithm). Our solver supports user specified precision thought the use of a custom floating point VHDL library supporting addition, subtraction, multiplication and division. The variable precision TDMA solver was tested for correctness in simulation mode. The TDMA pipeline was tested successfully in hardware using a simplified solver model. The details of implementation, the limitations, and future work are also discussed.

A current-mode based Field-Programmable Analog Array for signal processing applications

This paper presents a new approach to develop Field Programmable Analog Arrays (FPAAs),(1) which avoids excessive number of programming elements in the signal path, thus enhancing the performance. The paper also introduces a novel FPAA architecture, devoid of the conventional switching and connection modules. The proposed FPAA is based on simple current mode sub-circuits. An uncompounded methodology has been employed for the programming of the Configurable Analog Cell (CAC). Current mode approach has enabled the operation of the FPAA presented here, over almost three decades of frequency range. We have demonstrated the feasibility of the FPAA by implementing some signal processing functions.

A self-timed multipurpose delay sensor for field programmable gate arrays (FPGAs)

This paper presents a novel self-timed multi-purpose sensor especially conceived for Field Programmable Gate Arrays (FPGAs). The aim of the sensor is to measure performance variations during the life-cycle of the device, such as process variability, critical path timing and temperature variations. The proposed topology, through the use of both combinational and sequential FPGA elements, amplifies the time of a signal traversing a delay chain to produce a pulse whose width is the sensor’s measurement. The sensor is fully self-timed, avoiding the need for clock distribution networks and eliminating the limitations imposed by the system clock. One single off- or on-chip time-to-digital converter is able to perform digitization of several sensors in a single operation. These features allow for a simplified approach for designers wanting to intertwine a multi-purpose sensor network with their application logic. Employed as a temperature sensor, it has been measured to have an error of ±0.67 °C, over the range of 20–100 °C, employing 20 logic elements with a 2-point calibration.

An On Demand Queue Management Architecture for a Programmable Traffic Manager

A queue manager (QM) is a core traffic management (TM) function used to provide per-flow queuing in access andmetro networks; however current designs have limited scalability. An on-demand QM (OD-QM) which is part of a new modular field-programmable gate-array (FPGA)-based TM is presented that dynamically maps active flows to the available physical resources; its scalability is derived from exploiting the observation that there are only a few hundred active flows in a high speed network. Simulations with real traffic show that it is a scalable, cost-effective approach that enhances per-flow queuing performance, thereby allowing per-flow QM without the need for extra external memory at speeds up to 10 Gbps. It utilizes 2.3%–16.3% of a Xilinx XC5VSX50t FPGA and works at 111 MHz.

Exploring carrier agents in swarm-array computing

How can a bridge be built between autonomic computing approaches and parallel computing systems? The work reported in this paper is motivated towards bridging this gap by proposing a swarm-array computing approach based on ‘Intelligent Agents’ to achieve autonomy for distributed parallel computing systems. In the proposed approach, a task to be executed on parallel computing cores is carried onto a computing core by carrier agents that can seamlessly transfer between processing cores in the event of a predicted failure. The cognitive capabilities of the carrier agents on a parallel processing core serves in achieving the self-ware objectives of autonomic computing, hence applying autonomic computing concepts for the benefit of parallel computing systems. The feasibility of the proposed approach is validated by simulation studies using a multi-agent simulator on an FPGA (Field-Programmable Gate Array) and experimental studies using MPI (Message Passing Interface) on a computer cluster. Preliminary results confirm that applying autonomic computing principles to parallel computing systems is beneficial.

Teste da rede de interconexões de field programmable analog arrays

Os dispositivos analógicos programáveis (FPAAs, do inglês, Field Programmable Analog Arrays), apesar de ainda não terem a mesma popularidade de seus pares digitais (FPGAs, do inglês, Field Programmable Gate Arrays), possuem uma gama de aplicações bastante ampla, que vai desde o condicionamento de sinais em sistemas de instrumentação, até o processamento de sinais de radiofreqüência (RF) em telecomunicações. Porém, ao mesmo tempo em que os FPAAs trouxeram um impressionante ganho na agilidade de concepção de circuitos analógicos, também trouxeram um conjunto de novos problemas relativos ao teste deste tipo de dispositivo. Os FPAAs podem ser divididos em duas partes fundamentais: seus blocos programáveis básicos (CABs, do inglês, Configurable Analog Blocks) e sua rede de interconexões. A rede de interconexões, por sua vez, pode ser dividida em duas partes: interconexões internas (locais e globais entre CABs) e interconexões externas (envolvendo células de I/O). Todas estas partes apresentam características estruturais e funcionais distintas, de forma que devem ser testadas separadamente, pois necessitam que se considerem modelos de falhas, configurações e estímulos de teste específicos para assegurar uma boa taxa de detecção de defeitos. Como trabalhos anteriores já estudaram o teste dos CABs, o foco desta dissertação está direcionado ao desenvolvimento de metodologias que se propõem a testar a rede de interconexões de FPAAs. Apesar das várias diferenças entre as redes de interconexões de FPGAs e FPAAs, muitas também são as semelhanças entre elas, sendo, portanto, indiscutível que o ponto de partida deste trabalho tenha que ser o estudo das muitas técnicas propostas para o teste de interconexões em FPGAs, para posterior adaptação ao caso dos FPAAs. Além disto, embora o seu foco não recaia sobre o teste de CABs, pretende-se utilizá-los como recursos internos do dispositivo passíveis de gerar sinais e analisar respostas de teste, propondo uma abordagem de auto-teste integrado de interconexões que reduza o custo relativo ao equipamento externo de teste. Eventualmente, estes mesmos recursos poderão também ser utilizados para diagnóstico das partes defeituosas. Neste trabalho, utiliza-se como veículo de experimentação um dispositivo específico (Anadigm AN10E40), mas pretende-se que as metodologias de teste propostas sejam abrangentes e possam ser facilmente adaptadas a outros FPAAs comerciais que apresentem redes de interconexão semelhantes.

A true programmable HPF hybrid three-phase rectifier

In this paper is proposed and analyzed a digital hysteresis modulation using a FPGA (Field Programmable Gate Array) device and VHDL (Hardware Description Language), applied at a hybrid three-phase rectifier with almost unitary input power factor, composed by parallel SEPIC controlled single-phase rectifiers connected to each leg of a standard 6-pulses uncontrolled diode rectifier. The digital control allows a programmable THD (Total Harmonic Distortion) at the input currents, and it makes possible that the power rating of the switching-mode converters, connected in parallel, can be a small fraction of the total average output power, in order to obtain a compact converter, reduced input current THD and almost unitary input power factor. The proposed digital control, using a FPGA device and VHDL, offers an important flexibility for the associated control technique, in order to obtain a programmable PFC (Power Factor Correction) hybrid three-phase rectifier, in agreement with the international standards (IEC, and IEEE), which impose limits for the THD of the AC (Alternate Current) line input currents. Finally, the proposed control strategy is verified through experimental results from an implemented prototype. ©2008 IEEE.