Design and implementation of an FPGA-based piecewise affine Kalman Filter for Cyber-Physical Systems

The Kalman Filter is a robust tool often employed as a process observer in Cyber-Physical Systems. However, in the general case the high computational cost, especially for large plant models or fast sample rates, makes it an impractical choice for typical low-power microcontrollers. Furthermore, although industry trends towards tighter integration are supported by powerful high-end System-on-Chip software processors, this consolidation complicates the ability for a controls engineer to verify correct behavior of the system under all conditions, which is important in safety-critical systems and systems demanding a high degree of reliability. Dedicated Field-Programmable Gate Array (FPGA) hardware can provide application speedup, design partitioning in mixed-criticality systems, and fully deterministic timing, which helps ensure a control system behaves identically to offline simulations. This dissertation presents a new design methodology which can be leveraged to yield such benefits. Although this dissertation focuses on the Kalman Filter, the method is general enough to be extended to other compute-intensive algorithms which rely on state-space modeling. For the first part, the core idea is that decomposing the Kalman Filter algorithm from a strictly linear perspective leads to a more generalized architecture with increased performance compared to approaches which focus on nonlinear filters (e.g. Extended Kalman Filter). Our contribution is a broadly-applicable hardware-software architecture for a linear Kalman Filter whose operating domain is extended through online model swapping. A supporting application-agnostic performance and resource analysis is provided. For the second part, we identify limitations of the mixed hardware-software method and demonstrate how to leverage hardware-based region identification in order to develop a strictly hardware-only Kalman Filter which maintains a large operating domain. The resulting hardware processor is partitioned from low criticality software tasks running on a supervising software processor and enables vastly simplified timing validation

Dynamically Reconfigurable Systolic Array Accelerators: A Case Study with Extended Kalman Filter and Discrete Wavelet Transform Algorithms

Field programmable grid arrays (FPGA) are increasingly being adopted as the primary on-board computing system for autonomous deep space vehicles. There is a need to support several complex applications for navigation and image processing in a rapidly responsive on-board FPGA-based computer. This requires exploring and combining several design concepts such as systolic arrays, hardware-software partitioning, and partial dynamic reconfiguration. A microprocessor/co-processor design that can accelerate two single precision oating-point algorithms, extended Kalman lter and a discrete wavelet transform, is presented. This research makes three key contributions. (i) A polymorphic systolic array framework comprising of recofigurable partial region-based sockets to accelerate algorithms amenable to being mapped onto linear systolic arrays. When implemented on a low end Xilinx Virtex4 SX35 FPGA the design provides a speedup of at least 4.18x and 6.61x over a state of the art microprocessor used in spacecraft systems for the extended Kalman lter and discrete wavelet transform algorithms, respectively. (ii) Switchboxes to enable communication between static and partial reconfigurable regions and a simple protocol to enable schedule changes when a socket\u27s contents are dynamically reconfigured to alter the concurrency of the participating systolic arrays. (iii) A hybrid partial dynamic reconfiguration method that combines Xilinx early access partial reconfiguration, on-chip bitstream decompression, and bitstream relocation to enable fast scaling of systolic arrays on the PolySAF. This technique provided a 2.7x improvement in reconfiguration time compared to an o-chip partial reconfiguration technique that used a Flash card on the FPGA board, and a 44% improvement in BRAM usage compared to not using compression

FPGAs in Industrial Control Applications

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs

Implementation in Embedded Systems of State Observers Based on Multibody Dynamics

Programa Oficial de Doutoramento en Enxeñaría Naval e Industrial . 5015V01[Abstract] Simulation has become an important tool in the industry that minimizes either the cost and time of new products development and testing. In the automotive industry, the use of simulation is being extended to virtual sensing. Through an accurate model of the vehicle combined with a state estimator, variables that are difficult or costly to measure can be estimated. The virtual sensing approach is limited by the low computational power of invehicle hardware due to the strictest timing, reliability and safety requirements imposed by automotive standards. With the new generation hardware, the computational power of embedded platforms has increased. They are based on heterogeneous processors, where the main processor is combined with a co-processor, such as Field Programmable Gate Arrays (FPGAs). This thesis explores the implementation of a state estimator based on a multibody model of a vehicle in new generation embedded hardware. Different implementation strategies are tested in order to explore the advantages that an FPGA can provide. A new state-parameter-input observer is developed, providing accurate estimations. The proposed observer is combined with an efficient multibody model of a vehicle, achieving real-time execution.[Resumen] La simulación se ha convertido en una importante herramienta para la industria que permite minimizar tanto costes como tiempo de desarrollo y test de nuevos productos. En automoción, el uso de la simulación se extiende al desarrollo de sensores virtuales. Mediante un modelo preciso de un vehículo combinado con un observador de estados, variables que son caras o imposibles de medir pueden ser estimadas. La principal limitación para utilizar sensores virtuales en los vehículos es la baja potencia computacional de los procesadores instalados a bordo, debido a los estrictos requisitos impuestos por los standards de automoción. Con el hardware de nueva generación, el poder de cálculo de las plataformas empotradas se ha visto incrementado. Estos nuevos procesadores son del tipo heterogéneo, donde el procesador principal se complementa con un co-procesador, como una Field Programmable Gate Array (FPGA). Esta tesis explora la implementación de un observador de estados basado en un modelo multicuerpo de un vehículo en hardware empotrado de nueva generación. Se han probado diferentes implementaciones para evaluar las ventajas de disponer de una FPGA en el procesador. Se ha desarrollado un nuevo observador de estados, parámetros y entradas que permite obtener estimaciones de gran precisión. Combinando dicho observador con un eficiente modelo multicuerpo de un vehículo, se consigue rendimiento en tiempo real.[Resumo] A simulación estase a converter nunha importante ferramenta na industria que permite minimizar custes e tempo tanto de desenvolvemento coma de test de novos productos. En automoción, o uso da simulación esténdese á implementación de sensores virtuais. Mediante un modelo preciso dun vehículo combinado cun observador de estados, pódense estimar variables que son caras ou imposíbeis de medir. A principal limitación para utilizar sensores virtuais nos vehículos é a baixa potencia computacional dos procesadores instalados a bordo, debido aos estritos requisitos impostos polos estándares de automoción. Co hardware de nova xeración, o poder de cálculo das plataformas empotradas vese incrementado. Estos novos procesadores son de tipo heteroxéneo, onde o procesador principal compleméntase cun co-procesador, coma unha Field Programmable Gate Array (FPGA). Esta tese explora a implementación dun observador de estados basado nun modelo multicorpo dun vehículo en hardware empotrado de nova xeración. Diferentes implementacións foron probadas para avaliar as vantaxes de dispoñer dunha FPGA no procesador. Un novo observador de estados, parámetros e entradas deseñado nesta tese permite obter estimacións de gran precisión. Combinando dito observador cun eficiente modelo multicorpo dun vehículo, conséguese rendemento de tempo real

A scalable, portable, FPGA-based implementation of the Unscented Kalman Filter

Sustained technological progress has come to a point where robotic/autonomous systems may well soon become ubiquitous. In order for these systems to actually be useful, an increase in autonomous capability is necessary for aerospace, as well as other, applications. Greater aerospace autonomous capability means there is a need for high performance state estimation. However, the desire to reduce costs through simplified development processes and compact form factors can limit performance. A hardware-based approach, such as using a Field Programmable Gate Array (FPGA), is common when high performance is required, but hardware approaches tend to have a more complicated development process when compared to traditional software approaches; greater development complexity, in turn, results in higher costs. Leveraging the advantages of both hardware-based and software-based approaches, a hardware/software (HW/SW) codesign of the Unscented Kalman Filter (UKF), based on an FPGA, is presented. The UKF is split into an application-specific part, implemented in software to retain portability, and a non-application-specific part, implemented in hardware as a parameterisable IP core to increase performance. The codesign is split into three versions (Serial, Parallel and Pipeline) to provide flexibility when choosing the balance between resources and performance, allowing system designers to simplify the development process. Simulation results demonstrating two possible implementations of the design, a nanosatellite application and a Simultaneous Localisation and Mapping (SLAM) application, are presented. These results validate the performance of the HW/SW UKF and demonstrate its portability, particularly in small aerospace systems. Implementation (synthesis, timing, power) details for a variety of situations are presented and analysed to demonstrate how the HW/SW codesign can be scaled for any application

An FPGA Kalman-MPPT implementation adapted in SST-based dual active bridge converters for DC microgrids systems

The design of digital hardware controllers for the integration of renewable energy sources in DC microgrids is a research topic of interest. In this paper, a Kalman filter-based maximum power point tracking algorithm is implemented in an FPGA and adapted in a dual active bridge (DAB) converter topology for DC microgrids. This approach uses the Hardware/Software (HW/SW) co-design paradigm in combination with a pipelined piecewise polynomial approximation design of the Kalman-maximum power point tracking (MPPT) algorithm instead of traditional lookup table (LUT)-based methods. Experimental results reveal a good integration of the Kalman-MPPT design with the DAB-based converter, particularly during irradiation and temperature variations due to changes in weather conditions, as well as a good balanced hardware design in complexity and area-time performance compared to other state-of-art FPGA designs

Industrial applications of the Kalman filter:a review

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