Currently, the safety studies of the system (which are also collectively known as the safety case) cease or reduce in their utility after system certification, and with that, a vast amount of knowledge about the failure (or safe) behaviour of the system is usually rendered useless. In this thesis, we argue that this knowledge could be usefully exploited in the context of an appropriate on-line safety monitoring scheme. As a practical application of our approach, we propose a safety monitor that operates on safety cases to support the on-line detection and control of hazardous failures in safety critical systems. Firstly,

This report outlines the work done in searching the literature on the above mentioned topics.

Abstract: This paper presents a recent development of PID controllers. The proposed controller design aims to overcome the drawbacks of classical PID controller, such as: integrator windup due to the saturation in the actuator and the slow response to reject disturbances. Classical approaches to treat the windup problem in PID controller design are discussed and analyzed such as conditional integration method, limited integrator method, tracking anti-windup and modified tracking anti-windup. The proposed controller consists of basic PID blocks: Proportional, Derivative, and Integral actions plus a fuzzy anticipation block to modify the control signal according to operating conditions. The fuzzy anticipation block provides feed-forward correction terms to speed-up the system response and to retain the desired output. This block receives all different signals in PID block and generates anti-windup action to improve the system behavior. This action also compensates rapidly the disturbance effect on the system response. The proposed control design methodology is tested on a numerical example via simulation. The simulation work is carried out using MATLAB/SIMULINK environment. This environment is very easy to elaborate fuzzy toolbox in simulation. The obtained results affirm the potential of the proposed control methodology to add self-autonomy to the system behavior. 1.

Analysis of machining processes is important in the understanding and improving of manufacturing methods. The modeling of machining processes relies on high-strain-rate, high-temperature material properties. A Split-Hopkinson (or Kolsky) bar has been developed at NIST, for this purpose. By heating the material specimen rapidly with a controlled current pulse prior to the mechanical impact in the bar, structural changes in the specimen are inhibited, thus better simulating conditions during machining. A stress-strain relationship can be determined at various temperatures for a range of materials. For the elevated temperature Kolsky experiments it is essential for the specimen to be maintained at a constant and uniform temperature prior to the dynamic loading. We describe the development and implementation of a near-infrared micro-pyrometer (NIMPY) to the precision control of Kolsky specimen temperature preceding the mechanical impact. The pulse-heating system can be operated either in the transient mode, where the current to the Kolsky specimen is switched off at a preset temperature or time, or in the brief steady-state mode, where the specimen is heated rapidly to achieve the desired temperature (in the range from 400 K to 1300 K) in a short time (about 200 ms) and then held isothermally for a brief period (<2 s). The sensing signal for the feedback is provided by the NIMPY. Based on a feedback control algorithm, a dedicated computer operates a solid-state switch, consisting of field-effect-transistors (FETs), with a fast response time (< 5 ms), which controls the current to the Kolsky specimen to achieve isothermal condition. A brief description of a model of the pulse heating process is provided and the predicted specimen temperature history is compared with measured temperature data.

There is now considerable interest in industry and academia for “autonomic” or self-adaptive networks and distributed systems. Large-scale applications, such as simulation, typically run on large distributed systems in which it is impossible to guarantee at each instant the full reliability and availability of all processing nodes and of all communication links. Nevertheless such systems have to accomplish their mission and provide a high level of dependability and the best possible performance to critical applications. Self-adaptation will therefore be an intrinsic feature of large distributed systems and networks in order to move tasks and files in response to changes in subsystem dependability and system load, and to provide Quality-of-Service and dependable connections in the presence of fluctuating workloads and unknown system behavior. In this paper we review our work on the design of adaptive on-line task management algorithms for distributed systems, and the control of cognitive packet networks (CPN) to offer user specified QoS.

exhibit moderately good performance once the PID gains are properly tuned. However, when the dynamic characteristics of the system are time dependent or the operating conditions of the system vary, it is necessary to retune the gains to obtain desired performance. This situation has renewed the interest of researchers and practitioners in PID control. Self-tuning of PID controllers has emerged as a new and active area of research with the advent and easy availability of algorithms and computers. This study discusses self-tuning (auto-tuning) algorithm for control of autonomous underwater vehicles. Approach: Self-tuning mechanism will avoid time consuming manual tuning of controllers and promises better results by providing optimal PID controller settings as the system dynamics or operating points change. Most of the self-tuning methods available in the literature were based on frequency response characteristics and search methods. In this study, we proposed a method based on Taguchi’s robust design method for self-tuning of an autonomous underwater vehicle controller. The algorithm, based on this method, tuned the controller gains optimally and robustly in real time with less computation effort by using desired and actual state variables. It can be used for the Single-Input Single-Output (SISO) systems as well as Multi-Input Multi-Output (MIMO) systems without mathematical models of plants. Results: A simulation study of the AUV control on the horizontal

Abstract—One of the most basic functions of control engineers is tuning of controllers. There are always several process loops in the plant necessitate of tuning. The auto tuned Proportional Integral Derivative (PID) Controllers are designed for applications where large load changes are expected or the need for extreme accuracy and fast response time exists. The algorithm presented in this paper is used for the tuning PID controller to obtain its parameters with a minimum computing complexity. It requires continuous analysis of variation in few parameters, and let the program to do the plant test and calculate the controller parameters to adjust and optimize the variables for the best performance. The algorithm developed needs less time as compared to a normal step response test for continuous tuning of the PID through gain scheduling. Keywords—Auto tuning; gain scheduling; MIMO Processes; Optimization; PID controller; Process Control. I.