Optimal predictive control of water transport systems: Arrêt-Darré/Arros case study

This paper proposes the use of predictive optimal control as a suitable methodology to manage efficiently transport water networks. The predictive optimal controller is implemented using MPC control techniques. The Arrêt-Darré/Arros dam-river system located in the Southwest region of France is proposed as case study. A high-fidelity dynamic simulator based on the full Saint-Venant equations and able to reproduce this system is developed in MATLAB/SIMULINK to validate the performance of the developed predictive optimal control system. The control objective in the Arrêt-Darré/Arros dam-river system is to guarantee an ecological flow rate at a control point downstream of the Arrêt-Darré dam by controlling the outflow of this dam in spite of the unmeasured disturbances introduced by rainfalls incomings and farmer withdrawals

USCID fourth international conference

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Includes bibliographical references.The irrigation network of this study consists of three branch canals (the Machai Branch Canal, the Pehure High Level Canal (PHLC) and the Maira Branch Canal) connected to each other in such a way that the Machai Branch and the PHLC feed the Maira Branch Canal for providing a reliable irrigation service. The Machai Branch Canal has limited and erratic discharges and cannot fulfill the peak water requirements of the Maira Branch Canal and therefore any deficiency in the supplies to the Maira Branch Canal is automatically compensated by the PHLC. PHLC is an automatic canal and has been equipped with Proportional Integral Derivative (PID) discharge controllers at its head whereas the Machai Branch Canal has fixed supply based operations. The Maira Branch Canal is also an automatically downstream controlled irrigation canal, which is operated according to crop water requirements using Crop Based Irrigation Operations (CBIO) model. Under this scheme of operations the flows remain changing most of the time following the crop water requirements curve. The frequent changes in discharges keep the canal in unsteady state conditions, which affect the functioning of automatic discharge and water level regulation structures. Efficient system operation is a prerequisite for getting better water productivity and the precise understanding of the behavior of the structures and canal's hydrodynamics against such changes is a key for getting effective system operations. In this paper the canal's hydrodynamic behavior and the automatic structures' functioning have been assessed and suggestions have been provided to fine tune the automatic discharge controllers in order to avoid the oscillatory and abrupt hydrodynamic behavior in the canal. The guidelines have been provided for the operation of the secondary system for achieving smooth and sustainable operations of the canals. In addition to this the effects of any discharge variation in the Machai Branch Canal on the automatic discharge controller's behavior also has been assessed

Reactive Rules for Emergency Management

The goal of the following survey on Event-Condition-Action (ECA) Rules is to come to a common understanding and intuition on this topic within EMILI. Thus it does not give an academic overview on Event-Condition-Action Rules which would be valuable for computer scientists only. Instead the survey tries to introduce Event-Condition-Action Rules and their use for emergency management based on real-life examples from the use-cases identified in Deliverable 3.1. In this way we hope to address both, computer scientists and security experts, by showing how the Event-Condition-Action Rule technology can help to solve security issues in emergency management. The survey incorporates information from other work packages, particularly from Deliverable D3.1 and its Annexes, D4.1, D2.1 and D6.2 wherever possible

Adaptive model-based control systems and methods for controlling a gas turbine

Adaptive model-based control systems and methods are described so that performance and/or operability of a gas turbine in an aircraft engine, power plant, marine propulsion, or industrial application can be optimized under normal, deteriorated, faulted, failed and/or damaged operation. First, a model of each relevant system or component is created, and the model is adapted to the engine. Then, if/when deterioration, a fault, a failure or some kind of damage to an engine component or system is detected, that information is input to the model-based control as changes to the model, constraints, objective function, or other control parameters. With all the information about the engine condition, and state and directives on the control goals in terms of an objective function and constraints, the control then solves an optimization so the optimal control action can be determined and taken. This model and control may be updated in real-time to account for engine-to-engine variation, deterioration, damage, faults and/or failures using optimal corrective control action command(s)

On-line fault diagnosis of industrial processes based on artificial intelligence techniques

In this research the application of artificial intelligence techniques for on-line process control and fault detection and diagnosis are investigated. The majority of the research is on using artificial intelligence techniques in on-line fault detection and diagnosis of industrial processes. Several on-line approaches, including a rule based controller and several fault detection and diagnosis systems, have been developed and implemented and are described throughout this thesis. The research results obtained demonstrate that rule based controllers can be an alternative in situations where conventional mathematical modelling fails to give a high level of automation. The research on on-line fault detection and diagnosis emphasises the use of deep knowledge based approaches. Therefore, two on-line fault detection and diagnosis systems based on qualitative modelling have been implemented. For the first one only single abrupt faults have been considered while the second one can cope with single and multiple simultaneous abrupt faults. In order to overcome the problems associated with the inherent ambiguity of qualitative reasoning, a fuzzy qualitative simulation algorithm, which allows a semiquantitative extension to qualitative simulation, has been investigated. The adoption of fuzzy sets allows a more detailed description of physical variables, through an arbitrary, but finite, discretisation of the quantity space, and also allows common-sense knowledge to be represented rough the use of graded membership.F urther research concerning self-reasoning has been one for qualitative model based diagnosis approaches. A self-learning system which can find any inappropriate settings of fault detection and diagnosis parameters and also learn fault symptoms from on-line sampled data, has been developed. Through machine learning techniques, the system can adjust fuzzy membership functions of the process variables automatically, as well as build the knowledge base on-line very efficiently. In order to cope with incipient faults and transient behaviour of the process under concern, a distributed online fault detection and diagnosis system, consisting of a knowledge based approach coupled with a fuzzy neural network, has been developed. The fault detection task is performed through the knowledge based approach. A systematic methodology for formulating fault detection heuristic rules from knowledge of system structure and component -functions has been investigated. Since structural decomposition corresponds to plant topology, such a method could be easier to implement. A fuzzy neural network approach has been used for fault diagnosis. This system combines the advantages of both fuzzy reasoning and neural networks. In order to speed up the fuzzy neural network training task, an extension of the classical backpropagation learning algorithm is also investigated. The research results achieved with this fault detection and diagnosis system reveal a very good performance and reliability provided that the training data is available

Approaches to Safety in Inverse Reinforcement Learning

As the capabilities of robotic systems increase, we move closer to the vision of ubiquitous robotic assistance throughout our everyday lives. In transitioning robots and autonomous systems from traditional factory and industrial settings, it is critical that these systems are able to adapt to uncertain environments and the humans who populate them. In order to better understand and predict the behavior of these humans, Inverse Reinforcement Learning (IRL) uses demonstrations to infer the underlying motivations driving human actions. The information gained from IRL can be used to improve a robot’s understanding of the environment as well as to allow the robot to better interact with or assist humans.In this dissertation, we address the challenge of incorporating safety into the application of IRL. We first consider safety in the context of using IRL for assisting humans in shared control tasks. Through a user study, we show how incorporating haptic feedback into human assistance can increase humans’ sense of control while improving safety in the presence of imperfect learning. Further, we present our method for using IRL to automatically create such haptic feedback policies from task demonstrations.We further address safety in IRL by incorporating notions of safety directly into the learning process. Currently, most work on IRL focuses on learning explanatory rewards that humans are modeled as optimizing. However, pure reward optimization can fail to effectively capture hard requirements, such as safety constraints. We draw on the definition of safety from Hamilton-Jacobi reachability analysis to infer human perceptions of safety and to modify robot behavior to respect these learned safety constraints. We also extend this work on learning constraints by adapting the framework of Maximum Entropy IRL in order to learn hard constraints given nominal task rewards, and we show how this technique infers the most likely constraints to align expected behavior with observed demonstrations

Verification and Anomaly Detection for Event-Based Control of Manufacturing Systems.

Many important systems can be described as discrete event systems, including a manufacturing cell and patient flow in a clinic. Faults often occur in these systems and addressing these faults is important to ensure proper functioning. There are two main ways to address faults. Faults can be prevented from ever occurring, or they can be detected at the time at which they occur. This work develops methods to address faults in event-based systems for which there is no formal, pre-existing model. A primary application is manufacturing systems, where reducing downtime is especially important and pre-existing formal models are not commonly available. There are three main contributions. The first contribution is formalizing input order robustness - inputs occurring in different orders and yielding the same final state and set of outputs - and creating a method for its verification for logic controllers and networks of controllers. Theory is developed for a class of networks of controllers to be verified modularly, reducing the computational complexity. Input order robustness guarantees determinism of the closed-loop system. The second contribution is an anomaly detection solution for event-based systems without a pre-existing formal model. This solution involves model generation, performance assessment, and anomaly detection itself. A new variation of Petri nets was created to model the systems in this solution that incorporates resources in a less restrictive way. The solution detects anomalies and provides information about when the anomaly was first observed to help with debugging. The third contribution is the identification and resolution of five inconsistencies found between typical academic assumptions and industry practice when applying the anomaly detection solution to an industrial system. Resolutions to the inconsistencies included working with industry collaborators to change logic, and developing new algorithms to incorporate into the anomaly detection solution. Through these resolutions, the anomaly detection solution was improved to make it easier to apply to industrial systems. These three contributions for handling faults will help reduce down-time in manufacturing systems, and hence increase productivity and decrease costs.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78897/1/lzallen_1.pd