Modelling and Control of Aircraft Gas Turbine Engines

In this thesis the main theme is to demonstrate the potential performance improvements of gas turbine engines that are brought about by using multivariable control systems. Particular emphasis is on designing such control systems using the well-established engine thermodynamic models since these models are considered as the true representations of engine thermodynamic process and enable engine variable geometry features to be easily incorporated and their effects studied

Chippe : a system for constraint driven behavioral synthesis

This report describes the Chippe system, gives some background previous work and describes several sample design runs of the system. Also presented are the sources of the design tradeoffs used by Chippe, and overview of the internal design model, and experiences using the system

Integrating MDG variable ordering in a VHDL-MDG design verification system

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

Model checking for a first-order temporal logic using multiway decision graphs

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Integrating SAT with MDG for Efficient Invariant Checking

Multiway Decision Graph (MDG) is a canonical representation of a subset of many-sorted first-order logic. It generalizes the logic of equality with abstract types and uninterpreted function symbols. The area of Satisfiability (SAT) has been the subject of intensive research in recent years, with significant theoretical and practical contributions. From a practical perspective, a large number of very effective SAT solvers have recently been proposed, most of which based on improvements made to the original Davis-Putnam algorithm. Local search algorithms have allowed solving extremely large satisfiable instances of SAT. The combination between various verification methodologies will enhance the capabilities of each and overcome their limitations. In this thesis, we introduce a methodology and propose a new design verification tool integrating MDG and SAT, to check the safety of a design by invariant checking. Using MDG to encode the set of states provide powerful mean of abstraction. We use SAT solver searching for paths of reachable states violating the property under certain encoding constraints. In addition, we also introduce an automated conversion-verification methodology to convert a Directed Formula (DF) into Conjunctive Normal Form (CNF) formula that can be fed to a SAT solver. The formal verification of this conversion is conducted within the HOL theorem prover. Finally, we implement and conduct experiment on some examples along with a case study to show the correctness and the efficiency of our approach

High Level Synthesis for Packet Processing Pipelines

Packet processing is an essential function of state-of-the-art network routers and switches. Implementing packet processors in pipelined architectures is a well-known, established technique, albeit different approaches have been proposed. The design of packet processing pipelines is a delicate trade-off between the desire for abstract specifications, short development time, and design maintainability on one hand and very aggressive performance requirements on the other. This thesis proposes a coherent design flow for packet processing pipelines. Like the design process itself, I start by introducing a novel domain-specific language that provides a high-level specification of the pipeline. Next, I address synthesizing this model and calculating its worst-case throughput. Finally, I address some specific circuit optimization issues. I claim, based on experimental results, that my proposed technique can dramatically improve the design process of these pipelines, while the resulting performance matches the expectations of hand-crafted design. The considered pipelines exhibit a pseudo-linear topology, which can be too restrictive in the general case. However, especially due to its high performance, such an architecture may be suitable for applications outside packet processing, in which case some of my proposed techniques could be easily adapted. Since I ran my experiments on FPGAs, this work has an inherent bias towards that technology; however, most results are technology-independent

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