A run-of-mine ore milling circuit is primarily used to grind incoming ore containing precious metals to a particle size smaller than a specification size. A traditional run-of-mine (ROM) ore single-stage closed milling circuit comprises of the operational units: mill, sump and cyclone. These circuits are difficult to control because of significant nonlinearities, large time delays, large unmeasured disturbances, process variables that are difficult to measure and modelling uncertainties. A nonlinear model predictive controller with state estimation could yield good control of the ROM ore milling circuit despite these difficulties. Additionally, the ROM ore milling circuit is an energy intensive unit and a controller or power optimizer could bring significant cost savings. A nonlinear model predictive controller requires good state estimates and therefore a neural network for state estimation as an alternative to the particle filter has been addressed. The neural network approach requires fewer process variables that need to be measured compared to the particle filter. A neural network is trained with three disturbance parameters and used to estimate the internal states of the mill, and the results are compared with those of the particle filter implementation. The neural network approach performed better than the particle filter approach when estimating the volume of steel balls and rocks within the mill. A novel combined neural network and particle filter state estimator is presented to improve the estimation of the neural network approach for the estimation of volume of fines, solids and water within the mill. The estimation performance of the combined approach is promising when the disturbance magnitude used is smaller than that used to train the neural network. After state estimation was addressed, this work targets the implementation of a nonlinear controller combined with full state estimation for a grinding mill circuit. The nonlinear controller consists of a suboptimal nonlinear model predictive controller coupled with a dynamic inversion controller. This allows for fast control that is asymptotically stable. The nonlinear controller aims to reconcile the opposing objectives of high throughput and high product quality. The state estimator comprises of a particle filter for five mill states as well as an additional estimator for three sump states. Simulation results show that control objectives can be achieved despite the presence of noise and significant disturbances. The cost of energy has increased significantly in recent years. This increase in price greatly affects the mineral processing industry because of the large energy demands. A run-of-mine ore milling circuit provides a suitable case study where the power consumed by a mill is in the order of 2 MW. An attempt has been made to reduce the energy consumed by the mill in the two ways: firstly, within the nonlinear model predictive control in a single-stage circuit configuration and secondly, running multiple mills in parallel and attempting to save energy while still maintaining an overall high quality and good quantity. A formulation for power optimization of multiple ROM ore milling circuits has been developed. A first base case consisted not taking power into account in a single ROM ore milling circuit and a second base case split the load and throughput equally between two parallel milling circuits. In both cases, energy can be saved using the NMPC compared to the base cases presented without significant sacrifice in product quality or quantity. The work presented covers three topics that has yet to be addressed within the literature: a neural network for mill state estimation, a nonlinear controller with state estimation integrated for a ROM ore milling circuit and power optimization of a single and multiple ROM ore milling circuit configuration. / Dissertation (MEng)--University of Pretoria, 2015. / Electrical, Electronic and Computer Engineering / Unrestricted

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Practical control problems are subject to dealing with instrumentation noise and inaccurate models. These can be modelled as measurement and state noise, respectively. Nonlinear state estimators, for example a particle filter, can be used to mitigate these effects. However, they are usually computationally expensive which makes them impractical for industrial use. This text investigates using General Purpose Graphics Processing Units (GPGPU) to improve the performance particle and Gaussian sum filters by parallelizing their prediction, update and resampling steps. GPGPU accelerated filters are found to outperform non-accelerated filters as the number of particle increases. GPGPU acceleration also allows particle filters with 2^19.5 particles to be used on systems with dynamic time constants on the order of 0.1 second and for Gaussian sum filters with 2^18.5 particles to be used with time constants on the order of 1 second. The filters are applied to a bioreactor system containing R. Oryzae, where MPC control is applied to the production phase fumaric acid and glucose concentrations. The bioreactor is modelled using results from Iplik (2017) and Swart (2019). It is found that the GPGPU filters improved run times allow for more particles to be used which provides increased filter accuracy and thus better performance. This improved performance comes at the cost of consuming more energy. Thus, it is believed that the GPGPU implementations should be used for applications with complex dynamics/noise that require large numbers of particles and/or high sampling rates. / Dissertation (MEng (Control Engineering))--University of Pretoria, 2021. / Chemical Engineering / MEng (Control Engineering) / Unrestricted

The necessity of accurate localization in mobile robotics is obvious - if a robot does not know where it is, it cannot navigate accurately to reach goal locations. Robots learn about their environment via sensors. Small robots require small, efficient, and, if they are to be deployed in large numbers, inexpensive sensors. The sensors used by robots to perceive the world are inherently inaccurate, providing noisy, erroneous data or even no data at all. Combined with estimation error due to imperfect modeling of the robot, there are many obstacles to successfully localizing in the world. Sensor fusion is used to overcome these difficulties - combining the available sensor data in order to derive a more accurate pose estimation for the robot. In this thesis, we dissect and analyze a wide variety of sensor fusion algorithms, with the goal of using a set of inexpensive sensors in a suite to provide real-time localization for a robot given unknown sensor errors and malfunctions. The sensor fusion algorithms will fuse GPS, INS, compass and control inputs into a more accurate position. The filters discussed include a SPKF-PF (Sigma-Point Kalman Filter - Particle Filter), a MHSPKF (Multi-hypothesis Sigma-Point Kalman Filter), a FSPKF (Fuzzy Sigma-Point Kalman Filter), a DFSPKF (Double Fuzzy Sigma-Point Kalman Filter), an EKF (Extended Kalman Filter), a MHEKF (Multi-hypothesis Extended Kalman Filter), a FEKF (Fuzzy Extended Kalman Filter), and a standard SIS PF (Sequential Importance Sampling Particle Filter). Our goal in this thesis is to provide a toolbox of algorithms for a researcher, presented in a concise manner. I will also simultaneously provide a solution to a difficult sensor fusion problem - an algorithm that is of low computational complexity (< O(n³)), real-time, accurate (equal in or more accurate than a DGPS (differential GPS) given lower quality sensors), and robust - able to provide a useful localization solution even when sensors are faulty or inaccurate. The goal is to find a locus between power requirements, computational complexity and chip requirements and accuracy/robustness that provides the best of breed for small robots with inaccurate sensors. While other fusion algorithms work well, the Sigma Point Kalman filter solves this problem best, providing accurate localization and fast response, while the Fuzzy EKF is a close second in the shorter sample with less error, and the Sigma-Point Kalman Particle Filter does very well in a longer example with more error. Fuzzy control is also discussed, especially the reason for its applicability and its use in sensor fusion.

This study compares independent use of two known algorithms (Kalmar filter with background subtraction and Particle Filter) that are commonly deployed in object tracking applications. Object tracking in general is very challenging; it presents numerous problems that need to be addressed by the application in order to facilitate its successful deployment. Such problems range from abrupt object motion, during tracking, to a change in appearance of the scene and the object, as well as object to scene occlusions, and camera motion among others. It is important to take into consideration some issues, such as, accounting for noise associated with the image in question, ability to predict to an acceptable statistical accuracy, the position of the object at a particular time given its current position. This study tackles some of the issues raised above prior to addressing how the use of either of the aforementioned algorithm, minimize or in some cases eliminate the negative effects

This thesis studies novel and efficient computational sampling methods for appli- cations in three types of stochastic inversion problems: seismic waveform inversion, filtering problems, and static parameter estimation. A primary goal of a large class of seismic inverse problems is to detect parameters that characterize an earthquake. We are interested to solve this task by analyzing the full displacement time series at a given set of seismographs, but approaching the full waveform inversion with the standard Monte Carlo (MC) method is prohibitively expensive. So we study tools that can make this computation feasible. As part of the inversion problem, we must evaluate the misfit between recorded and synthetic seismograms efficiently. We employ as misfit function the Wasserstein metric origi- nally suggested to measure the distance between probability distributions, which is becoming increasingly popular in seismic inversion. To compute the expected values of the misfits, we use a sampling algorithm called Multi-Level Monte Carlo (MLMC). MLMC performs most of the sampling at a coarse space-time resolution, with only a few corrections at finer scales, without compromising the overall accuracy. We further investigate the Wasserstein metric and MLMC method in the context of filtering problems for partially observed diffusions with observations at periodic time intervals. Particle filters can be enhanced by considering hierarchies of discretizations to reduce the computational effort to achieve a given tolerance. This methodology is called Multi-Level Particle Filter (MLPF). However, particle filters, and consequently MLPFs, suffer from particle ensemble collapse, which requires the implementation of a resampling step. We suggest for one-dimensional processes a resampling procedure based on optimal Wasserstein coupling. We show that it is beneficial in terms of computational costs compared to standard resampling procedures. Finally, we consider static parameter estimation for a class of continuous-time state-space models. Unbiasedness of the gradient of the log-likelihood is an important property for gradient ascent (descent) methods to ensure their convergence. We propose a novel unbiased estimator of the gradient of the log-likelihood based on a double-randomization scheme. We use this estimator in the stochastic gradient ascent method to recover unknown parameters of the dynamics.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第17869号 / 工博第3778号 / 新制||工||1578(附属図書館) / 30689 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 寶 馨, 教授 細田 尚, 准教授 立川 康人 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Robotic systems are known to be computationally intensive. To improve performance, developers tend to implement custom robotic algorithms in hardware. However, a full robotic system typically consists of many interconnected algorithmic components that can easily max-out FPGA resources, thus requiring the designer to adjust each algorithm design for each new robotic systems in order to meet specific systems requirements and limited resources. Furthermore, manual development of digital circuitry using a hardware description language (HDL) such as verilog or VHDL, is error-prone, time consuming, and often takes months or years to develop and verify. Recent developments in high-level synthesis (HLS), enable automatic generation of digital circuit designs from high-level languages such as C or C++. In this thesis, we propose to develop a database of HLS-generated pareto-optimal hardware designs for various robotic algorithms, such that a fully automated process can optimally compose a complete robotic system given a set of system requirements. In the first part of this thesis, we take a first step towards this goal by developing a system for automatic selection of an Occupancy Grid Mapping (OGM) implementation given specific system requirements and resource thresholds. We first generate hundreds of possible hardware designs via Vitis HLS as we vary parameters to explore the designs space. We then present results which evaluate and explore trade-offs of these designs with respect to accuracy, latency, resource utilization, and power. Using these results, we create a software tool which is able to automatically select an optimal OGM implementation. After implementing selected designs on a PYNQ-Z2 FPGA board, our results show that the runtime of the algorithm improves by 35x over a C++-based implementation. In the second part of this thesis, we extend these same techniques to the Particle Filter (PF) algorithm by implementing 7 different resampling methods and varying parameters on hardware, again via HLS. In this case, we are able to explore and analyze thousands of PF designs. Our evaluation results show that runtime of the algorithm using Local Selection Resampling method reaches the fastest performance on an FPGA and can be as much as 10x faster than in C++. Finally, we build another design selection tool that automatically generates an optimal PF implementation from this design space for a given query set of requirements.

Aircraft in the US are equipped with Emergency Locator Transmitters (ELTs). In emergency situations these beacons are activated, providing a radio signal that can be used to locate the aircraft. Recent developments in UAV technologies have enabled mini-UAVs (5-foot wingspan) to possess a high level of autonomy. Due to the small size of these aircraft they are human-packable and can be easily transported and deployed in the field. Using a custom-built Radio Direction Finder, we gathered readings from a known transmitter and used them to compare various Bayesian reasoning-based filtering algorithms. Using a custom-developed simulator, we were able to test and evaluate filtering and control methods. In most non-trivial conditions we found that the Sequential Importance Resampling (SIR) Particle Filter worked best. The filtering and control algorithms presented can be extended to other problems that involve UAV control and tracking with noisy non-linear sensor behavior.

To operate, autonomous systems of necessity employ a variety of sensors to perceive their environment. Many small unmanned aerial vehicles (UAV) are unable to carry redundant sensors due to size, weight, and power (SWaP) constraints. Faults in these sensors can cause undesired behavior, including system instability. Thus, detection of faults in these non-redundant sensors is of paramount importance.The problem of detecting sensor faults in non-redundant sensors on board autonomous aircraft is non-trivial. Factors that make development of a solution difficult include both an inability to perfectly characterize systems and sensors as well as the SWaP constraints inherent with small UAV. An additional challenge is the ability of a fault-detection method to strike a balance between false-alarm rate and detection rate.This thesis explores two model-based methods of fault-detection for non-redundant sensors, a Kalman filter based method and a particle filter based method. The Kalman filter based method employs tests of mean and covariance on the normalized innovation sequence to detect faults, while the particle filter based method uses a function of the average particle weights.The Kalman filter based approach was implemented in real time on board an autonomous rotorcraft using an extended Kalman Filter (EKF). Faults tested included varied levels of bias, drift, and increased noise. Metrics included false-alarm rate, detection rate, and delay to detection. The particle filter based approach was implemented on a simulated system. This was then compared with an implementation of the EKF based approach for the same system. The same fault types and metrics were also used for these tests.The EKF based method of fault-detection performed well onboard the autonomous rotorcraft and should be generalizable to other systems for which an EKF or Kalman filter can be implemented. The theory indicates that the particle filter based algorithm should have performed better, though the simulations showed poor detection characteristics in comparison to the Kalman filter based method. Future work should be performed to explore improvements to the particle filter based method.