Symbolic Supervisory Control of Resource Allocation Systems

<p>Supervisory control theory (SCT) is a formal model-based methodology for verification and synthesis of supervisors for discrete event systems (DES). The main goal is to guarantee that the closed-loop system fulfills given specifications. SCT has great promise to assist engineers with the generation of reliable control functions. This is, for instance, beneficial to manufacturing systems where both products and production equipment might change frequently.</p> <p>The industrial acceptance of SCT, however, has been limited for at least two reasons: (i) the analysis of DES involves an intrinsic difficulty known as the state-space explosion problem, which makes the explicit enumeration of enormous state-spaces for industrial systems intractable; (ii) the synthesized supervisor, represented as a deterministic finite automaton (FA) or an extended finite automaton (EFA), is not straightforward to implement in an industrial controller.</p> <p>In this thesis, to address the aforementioned issues, we study the modeling, synthesis and supervisor representation of DES using binary decision diagrams (BDDs), a compact data structure for representing DES models symbolically. We propose different kinds of BDD-based algorithms for exploring the symbolically represented state-spaces in an effort to improve the abilities of existing supervisor synthesis approaches to handle large-scale DES and represent the obtained supervisors appropriately.</p> <p>Following this spirit, we bring the efficiencies of BDD into a particular DES application domain -- deadlock avoidance for resource allocation systems (RAS) -- a problem that arises in many technological systems including flexible manufacturing systems and multi-threaded software. We propose a framework for the effective and computationally efficient development of the maximally permissive deadlock avoidance policy (DAP) for various RAS classes. Besides the employment of symbolic computation, special structural properties that are possessed by RAS are utilized by the symbolic algorithms to gain additional efficiencies in the computation of the sought DAP. Furthermore, to bridge the gap between the BDD-based representation of the target DAP and its actual industrial realization, we extend this work by introducing a procedure that generates a set of "guard" predicates to represent the resulting DAP.</p> <p>The work presented in this thesis has been implemented in the SCT tool Supremica. Computational benchmarks have manifested the superiority of the proposed algorithms with respect to the previously published results. Hence, the work holds a strong potential for providing robust, practical and efficient solutions to a broad range of supervisory control and deadlock avoidance problems that are experienced in the considered DES application domain.</p

Motion planning and control: a formal methods approach

Control of complex systems satisfying rich temporal specification has become an increasingly important research area in fields such as robotics, control, automotive, and manufacturing. Popular specification languages include temporal logics, such as Linear Temporal Logic (LTL) and Computational Tree Logic (CTL), which extend propositional logic to capture the temporal sequencing of system properties. The focus of this dissertation is on the control of high-dimensional systems and on timed specifications that impose explicit time bounds on the satisfaction of tasks. This work proposes and evaluates methods and algorithms for synthesizing provably correct control policies that deal with the scalability problems. Ideas and tools from formal verification, graph theory, and incremental computing are used to synthesize satisfying control strategies. Finite abstractions of the systems are generated, and then composed with automata encoding the specifications. The first part of this dissertation introduces a sampling-based motion planning algorithm that combines long-term temporal logic goals with short-term reactive requirements. The specification has two parts: (1) a global specification given as an LTL formula over a set of static service requests that occur at the regions of a known environment, and (2) a local specification that requires servicing a set of dynamic requests that can be sensed locally during the execution. The proposed computational framework consists of two main ingredients: (a) an off-line sampling-based algorithm for the construction of a global transition system that contains a path satisfying the LTL formula, and (b) an on-line sampling-based algorithm to generate paths that service the local requests, while making sure that the satisfaction of the global specification is not affected. The second part of the dissertation focuses on stochastic systems with temporal and uncertainty constraints. A specification language called Gaussian Distribution Temporal Logic is introduced as an extension of Boolean logic that incorporates temporal evolution and noise mitigation directly into the task specifications. A sampling-based algorithm to synthesize control policies is presented that generates a transition system in the belief space and uses local feedback controllers to break the curse of history associated with belief space planning. Switching control policies are then computed using a product Markov Decision Process between the transition system and the Rabin automaton encoding the specification.The approach is evaluated in experiments using a camera network and ground robot. The third part of this dissertation focuses on control of multi-vehicle systems with timed specifications and charging constraints. A rich expressivity language called Time Window Temporal Logic (TWTL) that describes time bounded specifications is introduced. The temporal relaxation of TWTL formulae with respect to the deadlines of tasks is also discussed. The key ingredient of the solution is an algorithm to translate a TWTL formula to an annotated finite state automaton that encodes all possible temporal relaxations of the given formula. The annotated automata are composed with transition systems encoding the motion of all vehicles, and with charging models to produce control strategies for all vehicles such that the overall system satisfies the mission specification. The methods are evaluated in simulation and experimental trials with quadrotors and charging stations

COMPUTATIONAL FOUNDATIONS FOR COMPUTER AIDED CONCEPTUAL DESIGN OF MULTIPLE INTERACTION-STATE MECHATRONIC DEVICES

Increasing autonomy and intelligence in mechatronic devices requires them to be multiple interaction-state devices. Different modes of operations and different types of interactions with the use-environment require the device to have multiple interaction-states, each state capable of producing a different behavior to meet its intended requirements. For multiple interaction-state mechatronic devices, a satisfactory framework does not exist for representing, evaluating, and synthesizing design concepts. Hence, majority of mechatronic designers currently use informal methods for representing and evaluating design concepts during the conceptual design. This leads to the following problems. First, informal representation of design concepts hinders information exchange and reuse. Second, in absence of a validation methodology, it is not clear how to determine if a proposed design concept is consistent with the requirements. Finally, designers cannot perform computer aided evaluation during the conceptual design stage. This dissertation focuses in the area of computational foundations for representing, validating, evaluating, and synthesizing design concepts of multiple interaction-state mechatronic devices. A modeling and simulation framework has been developed for representing design concepts behind multiple interaction-state mechatronic devices. The problem of consistency-checking of interaction-states has been studied and an algorithm has been developed for solving the interaction consistency-checking problem. The problem of determining the presence of unsafe parameter values has been studied and an algorithm has been developed to determine whether an interaction-state in the proposed design concept can attain unsafe parameter values. Algorithms have been developed for evaluating design concepts based on the maximum power consumption and sharability of components. Finally, algorithms have been developed for automatically synthesizing transition diagrams for meeting the desired behavior specifications, given a components library. We believe that the results reported in this dissertation will provide the underlying foundations for constructing the next generation computer aided design tools for conceptual design of mechatronic devices. We expect that these tools would streamline the product development process, facilitate information reuse, and reduce product development time

Resource Management in Multi-Access Edge Computing (MEC)

This PhD thesis investigates the effective ways of managing the resources of a Multi-Access Edge Computing Platform (MEC) in 5th Generation Mobile Communication (5G) networks. The main characteristics of MEC include distributed nature, proximity to users, and high availability. Based on these key features, solutions have been proposed for effective resource management. In this research, two aspects of resource management in MEC have been addressed. They are the computational resource and the caching resource which corresponds to the services provided by the MEC. MEC is a new 5G enabling technology proposed to reduce latency by bringing cloud computing capability closer to end-user Internet of Things (IoT) and mobile devices. MEC would support latency-critical user applications such as driverless cars and e-health. These applications will depend on resources and services provided by the MEC. However, MEC has limited computational and storage resources compared to the cloud. Therefore, it is important to ensure a reliable MEC network communication during resource provisioning by eradicating the chances of deadlock. Deadlock may occur due to a huge number of devices contending for a limited amount of resources if adequate measures are not put in place. It is crucial to eradicate deadlock while scheduling and provisioning resources on MEC to achieve a highly reliable and readily available system to support latency-critical applications. In this research, a deadlock avoidance resource provisioning algorithm has been proposed for industrial IoT devices using MEC platforms to ensure higher reliability of network interactions. The proposed scheme incorporates Banker’s resource-request algorithm using Software Defined Networking (SDN) to reduce communication overhead. Simulation and experimental results have shown that system deadlock can be prevented by applying the proposed algorithm which ultimately leads to a more reliable network interaction between mobile stations and MEC platforms. Additionally, this research explores the use of MEC as a caching platform as it is proclaimed as a key technology for reducing service processing delays in 5G networks. Caching on MEC decreases service latency and improve data content access by allowing direct content delivery through the edge without fetching data from the remote server. Caching on MEC is also deemed as an effective approach that guarantees more reachability due to proximity to endusers. In this regard, a novel hybrid content caching algorithm has been proposed for MEC platforms to increase their caching efficiency. The proposed algorithm is a unification of a modified Belady’s algorithm and a distributed cooperative caching algorithm to improve data access while reducing latency. A polynomial fit algorithm with Lagrange interpolation is employed to predict future request references for Belady’s algorithm. Experimental results show that the proposed algorithm obtains 4% more cache hits due to its selective caching approach when compared with case study algorithms. Results also show that the use of a cooperative algorithm can improve the total cache hits up to 80%. Furthermore, this thesis has also explored another predictive caching scheme to further improve caching efficiency. The motivation was to investigate another predictive caching approach as an improvement to the formal. A Predictive Collaborative Replacement (PCR) caching framework has been proposed as a result which consists of three schemes. Each of the schemes addresses a particular problem. The proactive predictive scheme has been proposed to address the problem of continuous change in cache popularity trends. The collaborative scheme addresses the problem of cache redundancy in the collaborative space. Finally, the replacement scheme is a solution to evict cold cache blocks and increase hit ratio. Simulation experiment has shown that the replacement scheme achieves 3% more cache hits than existing replacement algorithms such as Least Recently Used, Multi Queue and Frequency-based replacement. PCR algorithm has been tested using a real dataset (MovieLens20M dataset) and compared with an existing contemporary predictive algorithm. Results show that PCR performs better with a 25% increase in hit ratio and a 10% CPU utilization overhead

Technology 2002: The Third National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2002 Conference and Exposition, December 1-3, 1992, Baltimore, MD. Volume 2 features 60 papers presented during 30 concurrent sessions

Multi-agent routing in shared guidepath networks

Motivated by a broad spectrum of applications ranging from automated zone-controlled, unit-load material handling systems to the movement of ions within a quantum computer, this thesis considers a class of multi-agent routing problems that seek to minimize the agents’ traveling time subject to certain congestion constraints. In more technical terms, the particular problem addressed in this work concerns the development of efficient, conflict-free, and deadlock-free schedules to route a set of non-interchangeable “agents” between their respective starting locations and destinations. Routes are specified as sequences of adjacent edges of the guidepath network, that are allocated sequentially and exclusively to the traveling agents by a traffic coordinator, according to an allocation protocol that seeks to ensure physical feasibility and other notions of “safety” for the agent motion. On the other hand, efficiency is measured by the schedule “makespan”—i.e., the time required for all agents to reach their respective destinations. In order to formally characterize the addressed scheduling problem and the corresponding notion of optimality for the sought schedules, this thesis first formulates the problem as a mixed-integer program (MIP). In this formulation, the system state at a given time is defined by the allocated edges and the directions of travel for the various agents, and the system is assumed to evolve this state at discrete time intervals that are defined by the required edge-traversal times. The presented MIP is derived according to a resource allocation system (RAS) perspective, and it is based on a set of binary decision variables that characterize the evolution of the system state over a sufficiently long time horizon. An additional auxiliary variable allows the computation of the schedule "makespan"—i.e., the number of discrete time periods required for the last agent to reach its designated destination.  An important feature of the developed MIP formulation is its ability to accommodate a broad range of variations of the considered traffic-scheduling problem that result from the variation of certain structural elements of the underlying traffic system and of the adopted edge-allocation protocol. From a computational standpoint, the optimal solution of all these problems is very complex. In many cases, even the identification of a feasible solution for a given problem instance can be a challenging problem. In view of all this complexity, the second part of the thesis formulates a Lagrangian dual problem for the generation of lower bounds for the original scheduling problem, and then describes two distinct methods to optimize this dual problem: (i) a customized dual-ascent algorithm, and (ii) a reformulation of the dual problem as a single, large linear program (LP). The first approach is proven to find an exact solution in a finite number of iterations, but the availability of very efficient LP solvers renders the second method more robust for larger problem instances. The two approaches provide consistent lower bounds for the optimal makespans of various problem instances, as well as Lagrange multipliers that optimize the Lagrangian dual and may be useful in the guidance of other heuristic algorithms for an optimized schedule. The third part of the thesis presents and analyzes a heuristic, "local-search" type of algorithm for minimizing the makespans of multi-agent routes on a shared guidepath network. For the context of conflict-free ion routing within a quantum computer, the thesis describes a complete algorithm for finding an initial feasible solution, and for optimizing that schedule by iterative reduction of the makespan, using dynamic programming (DP) to revise agent routes while eliminating conflicts between agents. Various methods for strengthening the makespan-reduction procedure (e.g., multi-agent simultaneous route revision, or controlled excursions into the infeasible region) are described and analyzed. Finally, the dissertation provides a set of experimental results that are obtained from the implementation of the developed methods for a carefully selected set of problem instances. For each instance, we find lower bounds (obtained either by hand, or by solving the Lagrangian dual problem) on the optimal objective values, as well as actual makespans for feasible schedules discovered by the heuristic scheduler. The considered problem instances include: (i) a small but difficult problem used to motivate our early research; (ii) a more complex "challenge" problem designed to maximize congestion; and (iii) a series of 150 randomized trials formulated on a grid-based configuration of the guidepath network that is typical of the corresponding structures that are encountered in many practical applications. The third set of experiments is further designed to evaluate the performance of the heuristic scheduler under increasing levels of congestion. The obtained results reveal that our heuristic algorithm can provide very efficient solutions for the targeted variations of the guidepath-based traffic-scheduling problem, in a way that is computationally efficient and complete. The thesis concludes with suggestions for future research that are aimed at (a) the further enhancement of the heuristic algorithm, (b) the extension of this algorithm and of the corresponding methodology to other variations of the considered traffic-scheduling problems, and (c) the embedding of all these results in a broader “rolling-horizon” framework that will address the dynamic nature of the operational (i.e., the transport) requirements of the considered traffic systems.Ph.D

Predicting Alarm And Safety System Performance Using Simulation

Safety is paramount to the chemical process industries. Because many processes operate at high temperatures and/or pressures, involving hazardous chemicals at high concentrations, the potential for accidents involving adverse human health and/or environmental impacts is significant. Thanks to research and operational efforts, both academically and industrially, the occurrences of such incidents are rare. However, disastrous events in the chemical manufacturing industry are still of relevant concern and garner further attention – the Deepwater Horizon incident (2010) and the Texas City refinery explosion (2005) being two recent examples. Many techniques have been developed to understand, quantify, and predict alarm and safety system failures. In practice, hazards are identified using Hazard and Operability (HAZOP) analysis, and a network of independently-acting safety systems works to maintain the probabilities of such events below a Safety Integrity Level (SIL). The network of safety systems is studied with Layer of Protection Analysis (LOPA), which uses failure probability estimates for individual subsystems to project the failures of entire safety system networks. With few alarm and safety system activations over the lifetime of a chemical process, particularly the critical last-line-of-defense systems, the failure probabilities of these systems are difficult to estimate. Statistical techniques have been developed, attempting to decrease the variances of such predictions despite few supporting data. This thesis develops methods to estimate the failure probabilities of rarely activated alarm and safety systems using process and operator models, enhanced by process, alarm, and operator data. Two repeated simulation techniques are explored involving informed prior distributions and transition path sampling. Both use dynamic process models, based upon first-principles, along with process, alarm, and operator data, to better understand and quantify the probability of alarm and safety system failures and the special-cause events leading to those failures. In the informed prior distribution technique, process and alarm data are analyzed to extract information regarding operator behavior, which is used to develop models for repeated simulation. With alarm and safety system failure probabilities estimated for specific special-cause events, near-miss alarm data are used, in real-time, to enhance the predictions. The transition path sampling method was originally developed by the molecular simulation community to understand better rare molecular events. Herein, important modifications are introduced for application to understand better how rare safety incidents evolve from rare special-cause events. This method uses random perturbations to identify likely trajectories leading to system failures – providing a basis for potential alarm and safety system design