Mobile systems, such as ground robots and electric vehicles, inherently operate in stochastic environments where load demands are largely unknown. Onboard energy storage, most commonly an electrochemical battery system, can significantly constrain operation. As such, mission planning and control of mobile systems can benefit from a priori knowledge about battery dynamics and constraints, especially the rate-capacity and recovery effects. To help overcome overly conservative predictions common with most existing battery remaining run-time algorithms, a prediction scheme was proposed. For characterization of a priori unknown power loads, an unsupervised Gaussian mixture routine identifies/clusters the measured power loads, and a jump-Markov chain characterizes the load transients. With the jump-Markov load forecasts, a model-based particle filter scheme predicts battery remaining run-time. Monte Carlo simulation studies demonstrate the marked improvement of the proposed technique. It was found that the increase in computational complexity from using a particle filter was justified for power load transient jumps greater than 13.4% of total system power. A multivariable reliability method was developed to assess the feasibility of a planned mission. The probability of mission completion is computed as the reliability integral of mission time exceeding the battery run-time. Because these random variables are inherently dependent, a bivariate characterization was necessary and a method is presented for online estimation of the process correlation via Bayesian updating. Finally, to abate transient shutdown of mobile systems, a model predictive control scheme is proposed that enforces battery terminal voltage constraints under stochastic loading conditions. A Monte Carlo simulation study of a small ground vehicle indicated significant improvement in both time and distance traveled as a result. For evaluation of the proposed methodologies, a laboratory terrain environment was designed and constructed for repeated mobile system discharge studies. The test environment consists of three distinct terrains. For each discharge study, a small unmanned ground vehicle traversed the stochastic terrain environment until battery exhaustion. Results from field tests with a Packbot ground vehicle in generic desert terrain were also used. Evaluation of the proposed prediction algorithms using the experimental studies, via relative accuracy and [alpha]-[lambda] prognostic metrics, indicated significant gains over existing methods. / text

Two application applications of Fuzzy Logic to improve the performance of two controllers are presented. The first application takes a Sliding Mode Controller designed for chemical process to reject disturbances. A fuzzy element is added to the sliding surface to improve the controller performance when set point change affects the control loop; especially for process showing highly nonlinear behavior. This fuzzy element, , is calculated by means of a set of fuzzy rules designed based on expert knowledge and experience. The addition of improved the controller response because accelerate or smooth the controller as the control loop requires. The Fuzzy Sliding Mode Controller (FSMCr) is a completely general controller. The FSMCr was tested with two models of nonlinear process: mixing tank and neutralization reactor. In both cases the FSMCr improves the performance shown for other control strategies, as the industrial PID, the conventional Sliding Mode Control and the Stan dard Fuzzy Logic Controller. The second part of this research presents a new way to implement the Dynamic Matrix Control Algorithm (DMC). A Parametric structure of DMC (PDMC) control algorithm is proposed, allowing to the controller to adapt to process nonlinearities. For a standard DMC a process model is used to calculate de controller response. This model is a matrix calculated from the dynamic response of the process at open loop. In this case the process parameters are imbibed into the matrix. The parametric structure isolates the process parameters allowing adjust the model as the nonlinear process changes its behavior. A Fuzzy supervisor was developed to detect changes in the process and send taht [sic]information to the PDMCr. The modeling error and other parameters related were used to estimate those changes. Some equations were developed to calculate the PDMCr tuning parameter,lambda, as a function of the process parameters. The performance of PDMCr was tested using to model of nonlinear process and compare with the standard DMC; in most the cases PDMCr presents less oscillations and tracks with less error the set point. Both control strategies presented in this research can be implemented into industrial applications easily.

This master thesis was performed at the business area of Aeronautics at Saab AB in collaboration with Linköping University during the spring of 2015. In a complex product development environment, having knowledge about different processes is advantageous for efficiency. Model Based Definition (MBD) is a product development process where a 3D-model is the main source of information and the same 3D-model is applied all the way from design to production. In assembly, the operator follows work instructions where the 3D-model, and its requirements, is visualized. The model is always updated to the latest version and no 2D-drawings are needed. Saab applied MBD during the development of the new generation of the fighter aircraft JAS 39 Gripen. This change, from previously 2D to 3D, has caused that both internal and external people have minor knowledge about the new developing process of MBD. The purpose of the thesis was to develop a demonstrator acting as an educational environment to share knowledge about the MBD-process and the 3D work instructions. New methods and processes could be tested and evaluated in the demonstrator before implementing into the real product development process. By following and developing an interdependent and iterative product development process, this work has visualized the MBD-process. Designing and developing a demonstrator, using the same tools as in the real product development process, accomplished this. This thesis has developed a demonstrator that includes the main components of a physical model with corresponding 3D work instructions and a conceptual layout. A physical Lego model of Gripen provides a flexible and interesting way of sharing knowledge to the user who interacts with the demonstrator. The 3D work instructions were created in a way so that the user can assemble and interact with the same expressions and terms in order to get an understanding about how they are used. The educational aspect is important where simplifications and additional notes to the instructions help to get a better understanding. Depending on who the user is, different levels of preparations are needed. The recognition factor is important to a user with experience of MBD; it needs to be able to understand how different terms and requirements are used in the development process. The future work is about setting up the demonstrator and conduct usability tests to evaluate, modify and implement more details. Using a demonstrator in this purpose can be helpful for evaluating different techniques, methods or systems and reduce the errors in the product development process. It can also encourage people to a new enjoyable way of learning.

Model based fault detection techniques utilize functional redundancies in the static and dynamic relationships among system inputs and outputs for fault detection and isolation. Analytical models based on the underlying physics of the system can capture the dependencies between different measured signals in terms of system states and parameters. These physical models of the system can be used as a tool to detect and isolate system faults. As a machine degrades, system outputs deviate from desired outputs, generating residuals defined by the error between sensor measurements and corresponding model simulated signals. These error residuals contain valuable information to interpret system states and parameters. Setting up the measurements from a faulty system as baseline, the parameters of the idealistic model can be varied to minimize these residuals. This process is called “Parameter Tuning”. A framework to automate this “Parameter Tuning” process is presented with a focus on DC motors and 3-phase induction motors. The parameter tuning module presented is a multi-tier module which is designed to operate on real system models that are highly non-linear. The tuning module combines artificial intelligence techniques like Quasi-Monte Carlo (QMC) sampling (Hammersley sequencing) and Genetic Algorithm (Non Dominated Sorting Genetic Algorithm) with an Extended Kalman filter (EKF), which utilizes the system dynamics information available via the physical models of the system. A tentative Graphical User Interface (GUI) was developed to simplify the interaction between a machine operator and the module. The tuning module was tested with real measurements from a DC motor. A simulation study was performed on a 3-phase induction motor by suitably adjusting parameters in an analytical model. The QMC sampling and genetic algorithm stages worked well even on measurement data with the system operating in steady state condition. But the downside was computational expense and inability to estimate the parameters online – ‘batch estimator’. The EKF module enabled online estimation where update was made based on incoming measurements. But observability of the system based on incoming measurements posed a major challenge while dealing with state estimation filters. Implementation details and results are included with plots comparing real and faulty systems. / text

Formal methods is a discipline of using a collection of mathematical techniques and formalisms to model and analyze software systems. Motivated by the new formal methods-based certification recommendations for safety-critical embedded software and the significant increase in the cost of verification and validation (V\&V), this research is about creating a software development process for control systems that can provide mathematical guarantees of high-level functional properties on the code. The process, dubbed credible autocoding, leverages control theory in the automatic generation of control software documented with proofs of their stability and performance. The main output of this research is an automated, credible autocoding prototype that transforms the Simulink model of the controller into C code documented with a code-level proof of the stability of the controller. The code-level proof, expressed using a formal specification language, are embedded into the code as annotations. The annotations guarantee that the auto-generated code conforms to the input model to the extent that key properties are satisfied. They also provide sufficient information to enable an independent, automatic, formal verification of the auto-generated controller software.

Spectrally pure signals are an indispensable requirement when the Signal Generator (SG) is to be used as part of a test bed. However, even sophisticated equipment may not comply with the needs imposed by certain applications. This work approaches the problem by using Digital Pre-Distortion (DPD) based on a polynomial memory-less model obtained for the SG. Using the SG in arbitrary mode (ARB) an input signal is computer-generated and reproduced by the SG. Measurement accuracy is ensured using coherence sampling and grid matching to the Signal Analyzer (SA). Finally, careful time alignment is used to compare the transmitted and received three-tone signals to obtain the polynomials coefficients. Results show that the accuracy of the model and the effectiveness of pre-distortion may vary depending on the amplitude of the three-tone signal. However, using polynomials of 5th and 9th degrees up to 15dB reduction of the 3rd order Inter-Modulation products can be obtained, and spurious powers may be lowered down to 70dBc.

Several different air pollutions are produced during combustion in a diesel engine, for example nitric oxides, NOx, which can be harmful for humans. This has led to stricter emission legislations for heavy duty trucks. The law requires both lower emissions and an On-Board Diagnosis system for all manufactured heavy duty trucks. The OBD system supervises the engine in order to keep the emissions below legislation demands. The OBD system shall detect malfunctions which may lead to increased emissions. To design the OBD system an automatic model based diagnosis approach has been developed at Scania CV AB where residual generators are generated from an engine model. The main objective of this thesis is to improve the existing methods at Scania CV AB to extract residual generators from a model in order to generate more residual generators. The focus lies on the methods to find possible residual generators given an overdetermined subsystem. This includes methods to estimate derivatives of noisy signals. A method to use both integral and derivative causality has been developed, called mixed causality. With this method it has been shown that more residual generators can be found when designing a model based diagnosis system, which improves the fault isolation. To use mixed causality, derivatives are estimated with smoothing spline approximation.

Requirements engineers have to elicit, document, and validate how stakeholders act and interact to achieve their common goals in collaborative scenarios. Only after gathering all information concerning who interacts with whom to do what and why, can a software system be designed and realized which supports the stakeholders to do their work. To capture and structure requirements of different (groups of) stakeholders, scenario-based approaches have been widely used and investigated. Still, the elicitation and validation of requirements covering collaborative scenarios remains complicated, since the required information is highly intertwined, fragmented, and distributed over several stakeholders. Hence, it can only be elicited and validated collaboratively. In times of globally distributed companies, scheduling and conducting workshops with groups of stakeholders is usually not feasible due to budget and time constraints. Talking to individual stakeholders, on the other hand, is feasible but leads to fragmented and incomplete stakeholder scenarios. Going back and forth between different individual stakeholders to resolve this fragmentation and explore uncovered alternatives is an error-prone, time-consuming, and expensive task for the requirements engineers. While formal modeling methods can be employed to automatically check and ensure consistency of stakeholder scenarios, such methods introduce additional overhead since their formal notations have to be explained in each interaction between stakeholders and requirements engineers. Tangible prototypes as they are used in other disciplines such as design, on the other hand, allow designers to feasibly validate and iterate concepts and requirements with stakeholders. This thesis proposes a model-based approach for prototyping formal behavioral specifications of stakeholders who are involved in collaborative scenarios. By simulating and animating such specifications in a remote domain-specific visualization, stakeholders can experience and validate the scenarios captured so far, i.e., how other stakeholders act and react. This interactive scenario simulation is referred to as a model-based virtual prototype. Moreover, through observing how stakeholders interact with a virtual prototype of their collaborative scenarios, formal behavioral specifications can be automatically derived which complete the otherwise fragmented scenarios. This, in turn, enables requirements engineers to elicit and validate collaborative scenarios in individual stakeholder sessions – decoupled, since stakeholders can participate remotely and are not forced to be available for a joint session at the same time. This thesis discusses and evaluates the feasibility, understandability, and modifiability of model-based virtual prototypes. Similarly to how physical prototypes are perceived, the presented approach brings behavioral models closer to being tangible for stakeholders and, moreover, combines the advantages of joint stakeholder sessions and decoupled sessions. / Anforderungsingenieure erheben, dokumentieren und validieren wie Bedarfsträger in einzelnen und gemeinsamen Aktivitäten die Ziele ihrer kollaborativen Szenarios erreichen. Auf Grundlage von Angaben darüber, wer warum mit wem zusammen was erledigt, kann anschließend ein Softwaresystem spezifiziert und umgesetzt werden, welches die Bedarfsträger bei der Durchführung ihrer Abläufe unterstützt. Um Anforderungen verschiedener (Gruppen von) Bedarfsträger zu erfassen und zu strukturieren, werden szenariobasierte Ansätze genutzt und erforscht. Die Erhebung und Validierung von Anforderungen, die kollaborative Szenarios abdecken, ist dennoch kompliziert, da derartige Informationen hochgradig verknüpft, fragmentiert und über mehrere Bedarfsträger verteilt sind, wodurch sie nur in Gruppensitzungen effizient erhoben und validiert werden können. In Zeiten global verteilter Firmen ist die Planung und Durchführung solcher Workshops mit Gruppen von Bedarfsträgern nur selten praktikabel. Mit einzelnen Bedarfsträgern zu sprechen ist hingegen oft realisierbar, führt aber zu fragmentierten, unvollständigen Szenariobeschreibungen. Durch eine Vielzahl von Einzelgesprächen mit wechselnden Bedarfsträgern kann diese Fragmentierung aufgelöst werden – dies ist aber eine fehleranfällige und zeitaufwändige Aufgabe. Zwar bieten formale Modellierungsmethoden z.B. automatische Konsistenzchecks für Szenarios, doch führen derartige Methoden zu Mehraufwand in allen Gesprächen mit Bedarfsträgern, da diesen die verwendeten formalen Notationen jedes Mal erläutert werden müssen. Handfeste Prototypen, wie sie in anderen Disziplinen eingesetzt werden, ermöglichen es Designern, ihre Konzepte und erhobenen Anforderungen ohne viel Aufwand mit Bedarfsträgern zu validieren und zu iterieren. In dieser Dissertation wird ein modellbasierter Generierungsansatz vorgeschlagen, der kollaborative Szenarios prototypisch auf Grundlage von formalen Verhaltensmodellen für die beteiligten Bedarfsträger darstellt. Durch die Simulation dieses Verhaltens und dessen Animation innerhalb einer webbasierten, domänenspezifischen Visualisierung, können Bedarfsträger diese Modelle erleben und die bisher erfassten Szenarios validieren. Eine derartige interaktive Szenariosimulation wird als modellbasierter virtueller Prototyp bezeichnet. Basierend auf den Interaktionen zwischen Bedarfsträgern und einem virtuellen Prototypen ihrer Szenarios können zudem formale Verhaltensspezifikationen automatisch abgeleitet werden, die wiederum die fragmentierten kollaborativen Szenarios vervollständigen. Dies ermöglicht es den Anforderungsingenieuren, die kollaborativen Szenarios in individuellen Sitzungen mit einzelnen Bedarfsträgern zu erheben und zu validieren – entkoppelt voneinander, da Bedarfsträger webbasiert teilnehmen können und dabei nicht darauf angewiesen sind, dass andere Bedarfsträger ebenfalls in der gleichen Sitzung teilnehmen. Diese Dissertation diskutiert und evaluiert die Machbarkeit, Verständlichkeit sowie die Änderbarkeit der modellbasierten virtuellen Prototypen. Auf die gleiche Art wie physikalische Prototypen wahrgenommen werden, erlaubt es der vorgestellte Ansatz, Verhaltensmodelle für Bedarfsträger erlebbar zu machen und so die Vorteile von Gruppensitzungen mit denen entkoppelter Sitzungen zu verbinden.

In modern times, human life is intrinsically depending on real-time embedded systems (RTES) with increasingly safety-critical and mission-critical features, for instance, in domains such as automotive and avionics. These systems are characterized by stringent functional requirements and require predictable timing behavior. However, the complexity of RTES has been ever increasing requiring systematic development methods. To address these concerns, model-based frameworks and component-based design methodologies have emerged as a feasible solution. Further, system artifacts such as requirements/specifications, architectural designs as well as behavioral models like statemachine views are integrated within the development process. However, several challenges remain to be addressed, out of which two are especially important: expressiveness, to represent the real-time and causality behavior, and analyzability, to support verification of functional and timing behavior. As the main research contribution, this thesis presents design and verification techniques for model-based development of RTES, addressing expressiveness and analyzability for architectural and behavioral models. To begin with, we have proposed a systematic design process to support component-based development. Next, we have provided a real-time semantic basis, in order to support expressiveness and verification for structural and behavioral models. This is achieved by defining an intuitive formal semantics for real-time component models, using ProCom, a component model developed at our research centre, and also using the CCSL (Clock Constraint Specification Language), an expressive language for specification of timed causality behavior. This paves the way for formal verification of both architectural and behavioral models, using model checking, as we show in this work, by transforming the models into timed automata and performing verification using UPPAAL, a model checking tool based on timed automata. Finally, the research contributions are validated using representative examples of RTES as well as an industrial case-study. / ARROWS

Behrad Dehghan: Position Control of a Pneumatic System using Adaptive Intelligent Methods. M.A.Sc. Thesis, Queen’s University, June, 2012. A large body of research is devoted to the development of advanced control techniques to improve the positioning performance of pneumatic systems, which are known to be highly nonlinear systems. Although model based controllers show good results, the requirement for a system model makes these methods difficult to implement. So-called intelligent algorithms, such as neural networks and fuzzy rule based controllers, are attractive since they do not require a model. The performance of these controllers can be enhanced by adding an adaptive mechanism to adjust controller parameters in a continuous on-line fashion. The objective of this thesis was to explore different adaptive intelligent controllers for position control of a pneumatic system. The application was the x-axis and z-axis of a pneumatic gantry robot. They were tested independently for their ability to track step and sine wave trajectories. The rodded x-axis cylinder was an example of a short stroke low friction application. The rodless z-axis cylinder was an example of a long stroke high friction application. Five different controllers were tested: 1) PID, 2) Fuzzy, 3) PID+Adaptive Neural Network Compensator (ANNC), 4) ANNonly and 5) Fuzzy Adaptive PID (FAPID). Results with FAPID and PID+ANNC showed improvement in tracking performance over PID by 60% for the rodded and 35% for the rodless cylinder. This level of improvement was expected given the adaptive nature of the controller. Unfortunately, both required significant effort to setup and tune. In order to reduce the tuning effort, a second adaptive mechanism was added to FAPID, to adjust output weights. Results with adaptive PID and modified FAPID (MFAPID) showed further improvement performance over PID by 87% for the rodded and 70% for the rodless cylinder (in addition to being easier to tune). To provide a measure of robustness, experiments were conducted at two supply pressures and three tracking frequencies. The fact that MFAPID was able to improve performance for both cylinders, is considered further evidence of its robustness. MFAPID is considered novel for two reasons: 1) fuzzy rule set is reduced in size relative previous work and 2) addition of an adaptive mechanism for output weights is new. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2012-06-20 11:09:19.694