Muhammad Sabih

Event-triggered output feedback control for distributed networked systems.

This paper addresses the problem of output-feedback communication and control with event-triggered framework in the context of distributed networked control systems. The design problem of the event-triggered output-feedback control is proposed as a linear matrix inequality (LMI) feasibility problem. The scheme is developed for the distributed system where only partial states are available. In this scheme, a subsystem uses local observers and share its information to its neighbors only when the subsystems local error exceeds a specified threshold. The developed method is illustrated by using a coupled cart example from the literature.

Experimental Investigations for Distributed Networked Control Systems

Distributed control systems based on communication networks are becoming a common interest in the contemporary control research community. This paper presents simulation and experimental investigations for such systems. We investigate the available simulation environments or tools and introduce an appropriate experimental setup to conduct control designs in distributed networked control systems. We establish experimental setups for networked control systems on pilot-scale plants. Our results enhance the need to continue research into experimental distributed control systems.

Networked event-triggered control: an introduction and research trends

A physical system can be studied as either continuous time or discrete-time system depending upon the control objectives. Discrete-time control systems can be further classified into two categories based on the sampling: (1) time-triggered control systems and (2) event-triggered control systems. Time-triggered systems sample states and calculate controls at every sampling instant in a periodic fashion, even in cases when states and calculated control do not change much. This indicates unnecessary and useless data transmission and computation efforts of a time-triggered system, thus inefficiency. For networked systems, the transmission of measurement and control signals, thus, cause unnecessary network traffic. Event-triggered systems, on the other hand, have potential to reduce the communication burden in addition to reducing the computation of control signals. This paper provides an up-to-date survey on the event-triggered methods for control systems and highlights the potential research directions.

Using OPC technology to support the study of advanced process control.

OPC, originally the Object Linking and Embedding (OLE) for Process Control, brings a broad communication opportunity between different kinds of control systems. This paper investigates the use of OPC technology for the study of distributed control systems (DCS) as a cost effective and flexible research tool for the development and testing of advanced process control (APC) techniques in university research centers. Co-Simulation environment based on Matlab, LabVIEW and TCP/IP network is presented here. Several implementation issues and OPC based client/server control application have been addressed for TCP/IP network. A nonlinear boiler model is simulated as OPC server and OPC client is used for closed loop model identification, and to design a Model Predictive Controller. The MPC is able to control the NOx emissions in addition to drum water level and steam pressure.

Measurement error sensitivity analysis for detecting and locating leak in pipeline using ANN and SVM

This paper presents an approach for detecting, locating and estimating the size of leak in a pipeline using pressure sensors, differential pressure sensors and flow-rate sensors. To overcome the problem with existing approaches we use differential pressure sensors that detect small change in pressure in order to detect small change in leak size. The pipeline system is modeled and simulated in EPANET software, and the input-output data acquired from it (i.e. sensor measurements and the leak locations and sizes) are used in MATLAB and DTREG software to develop Artificial Neural Network (ANN) and Support Vector Machines (SVM) models. Comparison of results shows that SVM is less sensitive and more stable to noise increment than ANN. However the performance of ANN is better with very small noises.

A novel energy-aware approach for locating leaks in water pipeline using a wireless sensor network and noisy pressure sensor data

We propose a novel energy-aware approach to detect a leak and estimate its size and location in a noisy water pipeline using least-squares and various pressure measurements in the pipeline network. The novelty in our work hinges on the fusion of the duty-cycling (DC) and data-driven (DD) strategies, both well-known techniques for energy reduction in a wireless sensor network (WSN). To maximize the information gain and minimize the energy consumed by the WSN, we first study the effects of (a) various levels of sensor measurement uncertainty and (b) the use of the smallest possible number of pressure sensors on the overall accuracy of our approach. Using the DD strategy only, a noisy environment, and a small number of sensors, the performance of our scheme shows that, for small leak sizes, the estimation error in both leak location and size becomes unacceptably high. Next, using as few sensors as possible for an acceptable accuracy, we fused the DD strategy with the DC one to minimize the sensing, processing, and communication energies. The fusion approach yielded a better performance with significant energy saving, even in noisy environments. EPANET was used to model the pipeline network and leak and MATLAB to implement, analyze, and evaluate our fusion approach.

An assessment of distributed state estimation

State estimation is an important topic in the study of dynamical systems. The problem of estimation can be structured into three categories: 1 centralised scheme; 2 decentralised scheme; 3 distributed scheme. Distributed estimation is a compromise between completely centralised and decentralised versions of estimation. In this paper, we will provide an assessment of distributed estimation based on Kalman filtering techniques for large-scale or sensor networks. In simulation, a second order dynamical system is employed in a scenario of ten sensor nodes. The sensor nodes attempt to estimate the states of the dynamical system with embedded consensus filters. The results show that the distributed estimation algorithm effectively approximates the central Kalman filter. It is concluded that the distributed estimation techniques for distributed dynamical system requires further extensive research.

Intelligent sensor for predicting the quality of reduced iron in direct reduction furnaces

Direct Reduction Iron (DRI) furnaces are used to produce iron from iron ore oxides using natural gas. The furnace takes the iron ore in the form of spherical pellets and a mixture of hydrogen and carbon monoxide and produces reduced iron. Accurate estimation of the quality of the reduced iron is essential for proper control and efficient operation of the DRI furnaces. In order to understand the various factors influencing the quality of the produced iron a mathematical model from the literature was utilized for the calculation of the solid and gas flow characteristics inside the DRI furnace. The model presents the differential equations governing the variations of the substance and energy exchange inside the shaft furnace. The objective of this work is to determine the influences of the various operating parameters on the performance of the DRI furnace. In addition to the mathematical model, investigation is carried out to develop a Neural Network model for on-line estimation of the quality of the reduced iron product based on the available process measurements.