Methodology for object-oriented real-time systems analysis and design: Software engineering

Successful application of software engineering methodologies requires an integrated analysis and design life-cycle in which the various phases flow smoothly 'seamlessly' from analysis through design to implementation. Furthermore, different analysis methodologies often lead to different structuring of the system so that the transition from analysis to design may be awkward depending on the design methodology to be used. This is especially important when object-oriented programming is to be used for implementation when the original specification and perhaps high-level design is non-object oriented. Two approaches to real-time systems analysis which can lead to an object-oriented design are contrasted: (1) modeling the system using structured analysis with real-time extensions which emphasizes data and control flows followed by the abstraction of objects where the operations or methods of the objects correspond to processes in the data flow diagrams and then design in terms of these objects; and (2) modeling the system from the beginning as a set of naturally occurring concurrent entities (objects) each having its own time-behavior defined by a set of states and state-transition rules and seamlessly transforming the analysis models into high-level design models. A new concept of a 'real-time systems-analysis object' is introduced and becomes the basic building block of a series of seamlessly-connected models which progress from the object-oriented real-time systems analysis and design system analysis logical models through the physical architectural models and the high-level design stages. The methodology is appropriate to the overall specification including hardware and software modules. In software modules, the systems analysis objects are transformed into software objects

Restart-Based Fault-Tolerance: System Design and Schedulability Analysis

Embedded systems in safety-critical environments are continuously required to deliver more performance and functionality, while expected to provide verified safety guarantees. Nonetheless, platform-wide software verification (required for safety) is often expensive. Therefore, design methods that enable utilization of components such as real-time operating systems (RTOS), without requiring their correctness to guarantee safety, is necessary. In this paper, we propose a design approach to deploy safe-by-design embedded systems. To attain this goal, we rely on a small core of verified software to handle faults in applications and RTOS and recover from them while ensuring that timing constraints of safety-critical tasks are always satisfied. Faults are detected by monitoring the application timing and fault-recovery is achieved via full platform restart and software reload, enabled by the short restart time of embedded systems. Schedulability analysis is used to ensure that the timing constraints of critical plant control tasks are always satisfied in spite of faults and consequent restarts. We derive schedulability results for four restart-tolerant task models. We use a simulator to evaluate and compare the performance of the considered scheduling models

A platform for real-time control education with LEGO MINDSTORMS.

A set of software development tools for building real-time control systems on a simple robotics platform is described in the paper. The tools are being used in a real-time systems course as a basis for student projects. The development platform is a low-cost PC running GNU/Linux, and the target system is LEGO MINDSTORMS NXT, thus keeping the cost of the laboratory low. Real-time control software is developed using a mixed paradigm. Functional code for control algorithms is automatically generated in C from Simulink models. This code is then integrated into a concurrent, real-time software architecture based on a set of components written in Ada. This approach enables the students to take advantage of the high-level, model-oriented features that Simulink oers for designing control algorithms, and the comprehensive support for concurrency and real-time constructs provided by Ada

A General Simulation Framework for Supply Chain Modeling: State of the Art and Case Study

Nowadays there is a large availability of discrete event simulation software that can be easily used in different domains: from industry to supply chain, from healthcare to business management, from training to complex systems design. Simulation engines of commercial discrete event simulation software use specific rules and logics for simulation time and events management. Difficulties and limitations come up when commercial discrete event simulation software are used for modeling complex real world-systems (i.e. supply chains, industrial plants). The objective of this paper is twofold: first a state of the art on commercial discrete event simulation software and an overview on discrete event simulation models development by using general purpose programming languages are presented; then a Supply Chain Order Performance Simulator (SCOPS, developed in C++) for investigating the inventory management problem along the supply chain under different supply chain scenarios is proposed to readers.Comment: International Journal of Computer Science Issues online at http://ijcsi.org/articles/A-General-Simulation-Framework-for-Supply-Chain-Modeling-State-of-the-Art-and-Case-Study.ph

Integrating real-time simulation models into a SCADA environment : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology at Massey University

Control system engineers have always envisaged the prospect of using the real-time models in an industrial setting. The inclusion of the real-time models can benefit industry in the following ways. 1. Operator Training - The operator can learn about how the various process react to control actions with the help of simulation models without affecting the real process itself. 2. Control Systems testing - The simulation models can be helpful in testing the control system software prior to trialing it on the real process. 3. Proccss Monitoring - Operators can compare the real process outputs with the simulation model outputs. This helps them in stopping the process when unusual conditions occur. 4. Testing for optimum operating conditions - Simulation models can be used to test for optimum operating conditions or for testing a certain operation at a new operating condition without affecting the real process. 5. Implementation of advanced control strategies - Advanced control strategics such as multivariable control, model predictive control and non linear control can be implemented as a real-time model without the development of separate real-time software. Even though using the real-time models can benefit the industry as mentioned modeling and real-time models have not found much favour in the industry. The reasons for this may be as follows: 1. Lack of awareness - Most of the plant managers/operators fail to understand what modeling results in and how it can improve the overall plant operation. 2. Lack of expertise - There is no expertise and/or tools in the company to develop the simulation models and implement it. 3. Cost of modeling - Producing a simulation model incurs significant costs. 4. Cost of implementation - Once the model is developed in the development environment it has to be transferred to the industrial platform. The cost of this transfer is high as the model software has to be more robust than the general purpose software. In order to produce real-time simulation models for an industrial setting there are two significant environments required. These are the development environment where the model is developed and secondly the implementation environment, where the model is used