Deep Learning-Based, Passive Fault Tolerant Control Facilitated by a Taxonomy of Cyber-Attack Effects

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control (FTC) is unique in the research literature. The proposed controller is applied to both linear and nonlinear systems. Additionally, the application and testing are accomplished with both actuators and sensors being affected by attacks and /or faults

Data based predictive control: Application to water distribution networks

In this thesis, the main goal is to propose novel data based predictive controllers to cope with complex industrial infrastructures such as water distribution networks. This sort of systems have several inputs and out- puts, complicate nonlinear dynamics, binary actuators and they are usually perturbed by disturbances and noise and require real-time control implemen- tation. The proposed controllers have to deal successfully with these issues while using the available information, such as past operation data of the process, or system properties as fading dynamics. To this end, the control strategies presented in this work follow a predic- tive control approach. The control action computed by the proposed data- driven strategies are obtained as the solution of an optimization problem that is similar in essence to those used in model predictive control (MPC) based on a cost function that determines the performance to be optimized. In the proposed approach however, the prediction model is substituted by an inference data based strategy, either to identify a model, an unknown control law or estimate the future cost of a given decision. As in MPC, the proposed strategies are based on a receding horizon implementation, which implies that the optimization problems considered have to be solved online. In order to obtain problems that can be solved e ciently, most of the strategies proposed in this thesis are based on direct weight optimization for ease of implementation and computational complexity reasons. Linear convex combination is a simple and strong tool in continuous domain and computational load associated with the constrained optimization problems generated by linear convex combination are relatively soft. This fact makes the proposed data based predictive approaches suitable to be used in real time applications. The proposed approaches selects the most adequate information (similar to the current situation according to output, state, input, disturbances,etc.), in particular, data which is close to the current state or situation of the system. Using local data can be interpreted as an implicit local linearisation of the system every time we solve the model-free data driven optimization problem. This implies that even though, model free data driven approaches presented in this thesis are based on linear theory, they can successfully deal with nonlinear systems because of the implicit information available in the database. Finally, a learning-based approach for robust predictive control design for multi-input multi-output (MIMO) linear systems is also presented, in which the effect of the estimation and measuring errors or the effect of unknown perturbations in large scale complex system is considered

Learning robotic milling strategies based on passive variable operational space interaction control

This paper addresses the problem of robotic cutting during disassembly of products for materials separation and recycling. Waste handling applications differ from milling in manufacturing processes, as they engender considerable variety and uncertainty in the parameters (e.g. hardness) of materials which the robot must cut. To address this challenge, we propose a learning-based approach incorporating elements of interaction control, in which the robot can adapt key parameters, such as feed rate, depth of cut, and mechanical compliance during task execution. We show how a mathematical model of cutting mechanics, embedded in a simulation environment, can be used to rapidly train the system without needing large amounts of data from physical cutting trials. The simulation approach was validated on a real robot setup based on four case study materials with varying structural and mechanical properties. We demonstrate the proposed method minimises process force and path deviations to a level similar to offline optimal planning methods, while the average time to complete a cutting task is within 25% of the optimum, at the expense of reduced volume of material removed per pass. A key advantage of our approach over similar works is that no prior knowledge about the material is required.Comment: 15 pages, 14 figures, accepted for publication in IEEE Transactions on Automation Science and Engineering (T-ASE

Modeling and Control of a Multibody Hinge-BargeWave Energy Converter

Wave Energy Converters (WECs) are devices used to extract energy from the waves. The particular WEC considered in this thesis is a three-body hinge-barge WEC, which is an articulated floating structure composed of 3 rectangular bodies interconnected by hinges, and it operates longitudinally to the direction to the incoming wave. The relative motion between each pair of bodies drives a Power Take-Off (PTO) system, which extracts the energy from the waves. The objective of this thesis is to increase the energy that can be extracted by a three-body hinge-barge WEC using an optimal control strategy, which computes the optimal loads applied by the PTOs driven by the relative motion between the bodies. The optimal control is formulated in the time domain, and computes the PTO loads in a coordinated way, so that the total energy extracted by the device is maximized. The optimal control strategy is formulated for a three-body hinge-barge WEC that is equipped with either passive or active PTOs. In this thesis, an optimal control strategy, for the maximization of the energy extracted by a three-body hinge-barge WEC, is derived with Pseudo-Spectral (PS) methods, which are a subset of the class of techniques used for the discretisation of integral and partial differential equations known as mean weighted residuals. In particular, PS methods based on Fourier basis functions, are used to derive an optimal control strategy, for a finite time horizon. Therefore, an optimal control strategy, with PS methods based on Fourier basis functions, cannot be applied for realtime control of the WEC, as Fourier basis functions can only represent the steady-state response of the WEC. However, PS methods based on Fourier basis functions provide a useful framework for the evaluation of the achievable power absorption performance of the WEC, with both active and passive PTOs. The Receding Horizon (RH) real-time optimal control of a three-body hingebarge WEC is derived with PS methods based on Half-Range Chebyshev-Fourier (HRCF) basis functions. The RH optimal real-time controller, with PS methods based on HRCF basis functions, maximizes the energy extracted by the WEC at each time step over a moving control horizon. In contrast to Fourier basis functions, HRCF basis functions are well suited for the approximation of non-periodic signals, allowing the representation of both the transient and steady-state response of the WEC. The optimal control strategy, with PS methods based on either Fourier or HRCF basis functions, is based on a dynamic model of the device, which is derived with two different modeling methodologies, that can be also applied to other types of multiple body WECs. The modeling methodologies are validated against wave-tank tests carried out on a 1/7th scale two-body hingebarge device, and a 1/25th and 1/20th scale three-body hinge-barge device

Design and control of the energy management system of a smart vehicle

This thesis demonstrates the design of two high efficiency controllers, one non-predictive and the other predictive, that can be used in both parallel and power-split connected plug-in hybrid electric vehicles. Simulation models of three different commercially available vehicles are developed from measured data for necessary testing and comparisons of developed controllers. Results prove that developed controllers perform better than the existing controllers in terms of efficiency, fuel consumption, and emissions

ADAPTIVE MODEL BASED COMBUSTION PHASING CONTROL FOR MULTI FUEL SPARK IGNITION ENGINES

This research describes a physics-based control-oriented feed-forward model, combined with cylinder pressure feedback, to regulate combustion phasing in a spark-ignition engine operating on an unknown mix of fuels. This research may help enable internal combustion engines that are capable of on-the-fly adaptation to a wide range of fuels. These engines could; (1) facilitate a reduction in bio-fuel processing, (2) encourage locally-appropriate bio-fuels to reduce transportation, (3) allow new fuel formulations to enter the market with minimal infrastructure, and (4) enable engine adaptation to pump-to-pump fuel variations. These outcomes will help make bio-fuels cost-competitive with other transportation fuels, lessen dependence on traditional sources of energy, and reduce greenhouse gas emissions from automobiles; all of which are pivotal societal issues. Spark-ignition engines are equipped with a large number of control actuators to satisfy fuel economy targets and maintain regulated emissions compliance. The increased control flexibility also allows for adaptability to a wide range of fuel compositions, while maintaining efficient operation when input fuel is altered. Ignition timing control is of particular interest because it is the last control parameter prior to the combustion event, and significantly influences engine efficiency and emissions. Although Map-based ignition timing control and calibration routines are state of art, they become cumbersome when the number of control degrees of freedom increases are used in the engine. The increased system complexity motivates the use of model-based methods to minimize product development time and ensure calibration flexibility when the engine is altered during the design process. A closed loop model based ignition timing control algorithm is formulated with: 1) a feed forward fuel type sensitive combustion model to predict combustion duration from spark to 50% mass burned; 2) two virtual fuel property observers for octane number and laminar flame speed feedback; 3) an adaptive combustion phasing target model that is able to self-calibrate for wide range of fuel sources input. The proposed closed loop algorithm is experimentally validated in real time on the dynamometer. Satisfactory results are observed and conclusions are made that the closed loop approach is able to regulate combustion phasing for multi fuel adaptive SI engines

Supervisory Autonomous Control of Homogeneous Teams of Unmanned Ground Vehicles, with Application to the Multi-Autonomous Ground-Robotic International Challenge

There are many different proposed methods for Supervisory Control of semi-autonomous robots. There have also been numerous software simulations to determine how many robots can be successfully supervised by a single operator, a problem known as fan-out, but only a few studies have been conducted using actual robots. As evidenced by the MAGIC 2010 competition, there is increasing interest in amplifying human capacity by allowing one or a few operators to supervise a team of robotic agents. This interest provides motivation to perform a more in-depth evaluation of many autonomous/semiautonomous robots an operator can successfully supervise. The MAGIC competition allowed two human operators to supervise a team of robots in a complex search-and mapping operation. The MAGIC competition provided the best opportunity to date to study through practice the actual fan-out with multiple semi-autonomous robots. The current research provides a step forward in determining fan-out by offering an initial framework for testing multi-robot teams under supervisory control. One conclusion of this research is that the proposed framework is not complex or complete enough to provide conclusive data for determining fan-out. Initial testing using operators with limited training suggests that there is no obvious pattern to the operator interaction time with robots based on the number of robots and the complexity of the tasks. The initial hypothesis that, for a given task and robot there exists an optimal robot-to-operator efficiency ratio, could not be confirmed. Rather, the data suggests that the ability of the operator is a dominant factor in studies involving operators with limited training supervising small teams of robots. It is possible that, with more extensive training, operator times would become more closely related to the number of agents and the complexity of the tasks. The work described in this thesis proves an experimental framework and a preliminary data set for other researchers to critique and build upon. As the demand increases for agent-to-operator ratios greater than one, the need to expand upon research in this area will continue to grow