Red neuronal artificial para la extracción de parámetros dinámicos de robots a partir de información incompleta de su movimiento

The artificial neural networks are suitable for processing incomplete data to achieve the desired output. The acquisition system of the manipulator robots takes quantified samples of the position; therefore, it is not possible to execute deterministic algorithms of parameter extraction in a reasonable time. State of the art describes algorithms based on the assumption that the motion signals are not quantified, and the first and second derivatives of the position are sampled instead of estimated. In this paper, a trained neural network-based extraction parameter algorithm for a determined robot is proposed to reduce the robot characterization time. Also, with the proposed methodology is possible to extract the parameters of the same kind of robot used for training the neural network.Las redes neuronales artificiales son útiles para procesar datos con información incompleta para obtener una salida deseada. En los sistemas de medición de robots manipuladores, solo se toman muestras cuantificadas de la posición y, por lo tanto, no se puede ejecutar en un tiempo razonable algoritmos deterministas para extraer los parámetros del robot. En el estado del arte, se abordan algoritmos de extracción de parámetros basados en la suposición de que no existe la cuantificación de las señales del movimiento del robot y que la primera y segunda derivada de la posición son muestreadas y no estimadas. En este trabajo, se propone un algoritmo basado en una red neuronal entrenada para extraer los parámetros de un determinado robot para reducir el tiempo de caracterización del robot, además, con la metodología propuesta se pueden extraer parámetros dinámicos del mismo tipo de robot con el que se ha entrenado la red neuronal

A hardware scalable, software configurable LQG controller using a sequential discrete Kalman filter

This thesis details the motivation, architecture, and analysis of a hardware scalable, software programmable Linear Quadratic Gaussian (LQG) controller using a Sequential Discrete Kalman Filter (SDKF) state estimator. While LQG controllers have been around since the 1980s, these controllers have currently not been widely adopted in industry since this algorithm involves a non-trivial matrix inversion. While many accelerated LQG & DKF architectures have been published, these architectures target specific platforms or applications; switching these architecture\u27s application is a non-trivial and time consuming task. Thus, I designed an open-source hardware scalable, software configurable LQG controller, with the intent of others to use this design as an IP core, which will help ease the transition from abstract control theory to practical implementation. The design allows for a user to scale the accelerated LQG hardware architecture while software configurable registers allow the user to configure their controllers without re-synthesizing the hardware design, thus allowing for them to tune their controller on-the-fly. This controller was designed in Xilinx\u27s Vivado 2018.2 design suite, targeting Xilinx ZYNQ series FPGAs, which contain an embedded dual-core ARM Cortex-A9 processor in addition to the traditional FPGA fabric. To compare the performance of this accelerated design, a software implementation of the algorithm was built and tested on three different processor platforms: an embedded ARM Cortex-A9 processor, an AMD FX-9800 series processor, and an Intel i7-4810MQ series processor. For lower dimensional matrices (n = 4), there were modest performance improvements, ranging from 0.79-14.5x improvement for the AMD \& ARM processor, respectively. For larger dimensional matrices (n = 128), the HW/SW LQG achieved a 73x, 102x, and 1390x performance improvement over the Intel, AMD, and ARM processors, respectively. In addition to the software comparison, the analysis is concluded with a comparison of the proposed architecture\u27s size and performance characteristics versus several of the most relevant and recent comparable architectures

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

Design and implementation of resilient attitude estimation algorithms for aerospace applications

Satellite attitude estimation is a critical component of satellite attitude determination and control systems, relying on highly accurate sensors such as IMUs, star trackers, and sun sensors. However, the complex space environment can cause sensor performance degradation or even failure. To address this issue, FDIR systems are necessary. This thesis presents a novel approach to satellite attitude estimation that utilizes an InertialNavigation System (INS) to achieve high accuracy with the low computational load. The algorithm is based on a two-layer Kalman filter, which incorporates the quaternion estimator(QUEST) algorithm, FQA, Linear interpolation (LERP)algorithms, and KF. Moreover, the thesis proposes an FDIR system for the INS that can detect and isolate faults and recover the system safely. This system includes two-layer fault detection with isolation and two-layered recovery, which utilizes an Adaptive Unscented Kalman Filter (AUKF), QUEST algorithm, residual generators, Radial Basis Function (RBF) neural networks, and an adaptive complementary filter (ACF). These two fault detection layers aim to isolate and identify faults while decreasing the rate of false alarms. An FPGA-based FDIR system is also designed and implemented to reduce latency while maintaining normal resource consumption in this thesis. Finally, a Fault Tolerance Federated Kalman Filter (FTFKF) is proposed to fuse the output from INS and the CNS to achieve high precision and robust attitude estimation.The findings of this study provide a solid foundation for the development of FDIR systems for various applications such as robotics, autonomous vehicles, and unmanned aerial vehicles, particularly for satellite attitude estimation. The proposed INS-based approach with the FDIR system has demonstrated high accuracy, fault tolerance, and low computational load, making it a promising solution for satellite attitude estimation in harsh space environment