Bifurcations on a spring-pendulum oscillator

In this paper we study experimentally the dynamics of a non linear system: a spring pendulum coupled to an oscillator. The system was tested using a hybrid technique called Real Time Dynamic Substructuring (RTDS)[1, 2]. RTDS is a testing technique that involves splitting the system under study into two subsystems: one will be physically tested (physical substructure) and the other will be simulated in the computer (numerical model). These substructures interact in real time through a set of transfer systems. RTDS is a very powerful experimental methodology that not only allows full scale and real time testing but also real-time bifurcation tracking in complex engineering systems [3, 4]. In our hybrid experiment the spring pendulum is taken to be the physical substructure while oscillator is the simulated numerical model. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability

Smart grid is referred to a modernized power grid which can mitigate fault detection and allow self‐healing of the system without the intervention of operators. This article proposes an innovative analytical formulation using Markov method to evaluate electric power distribution system reliability in smart grids, which incorporates the impact of smart monitoring on the overall system reliability. An accurate reliability model of the main network components and the communication infrastructure have been also considered in the methodology. The proposed approach was applied to a well‐known test bed (Roy Billinton Test System) and various reliability case studies with monitoring provision and monitoring deficiency are analyzed. This article involves the developing possibilities of communication technologies and next‐generation control systems of the entire smart network based on the real‐time monitoring and modern control system to achieve a reliable, economical, safe, and high efficiency of electricity. The implementations indicate that using an appropriate set of the smart grid monitoring devices for power system components can virtually influence all the reliability indices although the amount of improvement varies between techniques. The proposed approach determined that smart monitoring for which components of the electric power distribution systems are tailored and deduce to major economical benefits. The described approach also reveals which reliability indices drastically influenced using monitoring. © 2014 Wiley Periodicals, Inc. Complexity 21: 99–113, 2015     

Tracking control of high-performance robots via stabilizing controllers for uncertain systems

The development of flexible manufacturing systems calls for industrial robots characterized by robustness of performance with regard to the variations of the loads and real time specification of the trajectory in the work space. In this paper, the design of a feedback controller guaranteeing such performance is considered. At first, the manipulator dynamics are embedded into a larger class of uncertain dynamical systems and a class of feedback controls is proposed that guarantees uniform ultimate boundedness of the tracking error. Successively, the methodology is specialized for the case of robotic manipulators to track trajectories described in task-oriented coordinates; the proposed control algorithm operates without requiring any explicit coordinate transformation.     

Modeling fuzzy information in UML class diagrams and object-oriented database models

Conceptual data modeling has become essential for non-traditional application areas. Some conceptual data models have been proposed as tools for database design and object-oriented database modeling. Information in real-world applications is often vague or ambiguous. Currently, a little research is underway on modeling the imprecision and uncertainty in conceptual data modeling and the conceptual design of fuzzy databases. The unified modeling language (UML) is a set of object-oriented modeling notations and a standard of the object management group (OMG) with applications to many areas of software engineering and knowledge engineering, increasingly including data modeling. This paper introduces different levels of fuzziness into the class of UML and presents the corresponding graphical representations, with the result that UML class diagrams may model fuzzy information. The fuzzy UML data model is also formally mapped into the fuzzy object-oriented database model.     

Frequency planning as a set partitioning problem

Some aspects of a new computerized method for automatic generation of frequency plans for radio communication systems are presented. The emphasis is on problem formulation where the frequency planning problem is recast as a set partitioning problem. The objective is minimization of total system interference. A solution algorithm that has been found useful in practical applications is presented. An alternative algorithm is also demonstrated.     

Developing Systems for Real-Time Streaming Analysis

Sources of streaming data are proliferating and so are the demands to analyze and mine such data in real time. Statistical methods frequently form the core of real-time analysis, and therefore, statisticians increasingly encounter the challenges and implicit requirements of real-time systems. This work recommends a comprehensive strategy for development and implementation of streaming algorithms, beginning with exploratory data analysis in a flexible computing environment, leading to specification of a computational algorithm for the streaming setting and its initial implementation, and culminating in successive improvements to computational efficiency and throughput. This sequential development relies on a collaboration between statisticians, domain scientists, and the computer engineers developing the real-time system. This article illustrates the process in the context of a radio astronomy challenge to mitigate adverse impacts of radio frequency interference (noise) in searches for high-energy impulses from distant sources. The radio astronomy application motivates discussion of system design, code optimization, and the use of hardware accelerators such as graphics processing units, field-programmable gate arrays, and IBM Cell processors. Supplementary materials, available online, detail the computing systems typically used for streaming systems with real-time constraints and the process of optimizing code for high efficiency and throughput.     

Graph labeling and radio channel assignment

The vertex-labeling of graphs with nonnegative integers provides a natural setting in which to study problems of radio channel assignment. Vertices correspond to transmitter locations and their labels to radio channels. As a model for the way in which interference is avoided in real radio systems, each pair of vertices has, depending on their separation, a constraint on the difference between the labels that can be assigned. We consider the question of finding labelings of minimum span, given a graph and a set of constraints. The focus is on the infinite triangular lattice, infinite square lattice, and infinite line lattice, and optimal labelings for up to three levels of constraint are obtained. We highlight how accepted practice can lead to suboptimal channel assignments. © 1998 John Wiley & Sons, Inc. J Graph Theory 29: 263–283, 1998     

Knowledge Representation, Reasoning and Integration Using Temporal Logic with Clocks

Representation, reasoning about and integrating knowledge based on multiple time granularities in knowledge-based systems is important, especially when talking about events that take place in the real world. Formal approaches based on temporal logics have been successfully applied in many application domains of knowledge-based systems where the evolution of a system and its environment through time is central. This paper presents a methodology based on temporal logic to deal with knowledge based on multiple time granularities in knowledge-based systems. The temporal logic we consider is especially suitable for modelling events with different rates and/or scales of progress. The methodology includes an approach to the representation of timing systems, a method used for representing facts and rules in a knowledge-based system that involve multiple time granularities using temporal logic, and several deductive reasoning techniques. The work presented in this article has been supported in part by The Australian Research Council and Macquarie University. Note that this paper is an extended and revised version of Orgun, Liu and Nayak [37].     

Simulation Model Development and Analysis in UNITY

We evaluate UNITY – a computational model, specification language and proof system defined by Chandy and Misra [5] for the development of parallel and distributed programs – as a platform for simulation model specification and analysis. We describe a UNITY-based methodology for the construction, analysis and execution of simulation models. The methodology starts with a simulation model specification in the form of a set of coupled state transition systems. Mechanical methods for mapping the transition systems first into a set of formal assertions, permitting formal verification of the transition systems, and second into an executable program are described. The methodology provides a means to independently verify the correctness of the transition systems: one can specify properties formally that the model should obey and prove them as theorems using the formal specification. The methodology is illustrated through generation of a simulation program solving the machine interference problem using the Time Warp protocol on a distributed memory parallel architecture.     

A methodology to customize pull control systems

The objective of this paper is to develop a methodology for customizing pull control systems, replacing traditional control systems such as Kanban, Conwip, and Base Stock. The basic principle is to start with a pull control system that connects each stage of a given production line with each preceding stage; next, the optimization of the corresponding simulation model shows which of these potential control loops are actually implemented. The result of this methodology may be one of the traditional systems, but it may also be one of the following three new types: (1) the total line may be decomposed into several segments, each with its own classic control system, (2) the total line or its segments may combine different traditional systems, or (3) the line may be controlled through a new type of control system. Indeed, these three systems are found in a simulation experiment with a set of twelve production lines. These twelve lines are selected through a statistical design with ten factors that characterize production lines.     

Model Predictive Control for Heating Ventilating and Air Conditioning Systems

In heating ventilating and air conditioning (hvac) systems, model predictive control (mpc) is rarely used up to now. However, the following properties of hvac systems make mpc a well suited control methodology: the plant is a multiple input, multiple output system and its inputs are constrained – both in their value and in their rate of change. Moreover, several disturbances acting on the plant like varying outdoor air temperature or outdoor humidity can be measured. Furthermore, time constants are relatively large which makes it easy to perform the required optimization of the mpc strategy in time. This contribution presents the application of mpc to a real world hvac system. The considered hvac system consists of standard industrial components. The core components of the system are water-to-air heat exchangers, both for heating and for cooling purposes as well as a steam humidifier. Hence, air temperature and air humidity can be varied with the help of the investigated hvac system. The derivation of the plant model based on thermodynamic relations is presented as well as the application of the mpc strategy to the real world system. The plant contains some nonlinear elements which have to be dealt with when applying the mpc strategy. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Connectivity modeling and optimization of linear consecutively connected systems with repairable connecting elements

Linear consecutively connected systems (LCCSs) are systems containing a linear sequence of ordered nodes. Connection elements (CE) characterized by diverse connection ranges, time-to-failure and time-to-repair distributions are allocated to different nodes to provide the system connectivity, i.e., a connection between the source and sink nodes of the LCCS. Examples of LCCSs abound in practical applications such as flow transmission systems and radio communication systems. Considerable research efforts have been expended in modeling and optimizing LCCSs. However, most of the existing works have assumed that CEs either are non-repairable or undergo a restrictive minimal repair policy with constant repair time. This paper makes new technical contributions by modeling and optimizing LCCSs with CEs under corrective maintenance with random repair time and different repair policies (minimal, perfect, and imperfect). The characteristics of CEs can depend on their location because the distance between adjacent nodes and conditions of CE operation and maintenance at different nodes can be different, which further complicates the problem. We first propose a discrete numerical algorithm to evaluate the instantaneous availability of each CE. A universal generating function based method is then implemented for assessing instantaneous and expected system connectivity for a specific CE allocation. As the CE allocation can have significant impacts on the system connectivity, we further define and solve the optimal CE allocation problem, whose objective is to find the CE allocation among LCCS nodes maximizing the expected system connectivity over a given mission time. Effects of different parameters including repair efficiency, mission time and repair time are investigated. As illustrated through examples, optimization results can facilitate optimal decisions on robust design and effective operation and maintenance managements of LCCSs.     

Constrained MPC design of nonlinear Markov jump system with nonhomogeneous process

In this paper, a differential-inclusion-based MPC scheme is developed for the controller design for a discrete time nonlinear Markov jump system with nonhomogeneous transition probability. By adopting a differential-inclusion-based convex model predictive control mechanism, the nonlinear Markov jump system with nonhomogeneous transition probability is enclosed by a set of linear Markov jump systems. In this way, the controller design for the nonlinear Markov jump system can be solved via solving a set of linear Markov jump systems. Two numerical examples with different weighting parameters R are presented to illustrate the applicability of the results obtained.     

A Branch-and-Bound Algorithm to Solve a Multi-level Network Optimization Problem

Multi-level network optimization problems arise in many contexts such as telecommunication, transportation, and electric power systems. A model for multi-level network design is formulated as a mixed-integer program. The approach is innovative because it integrates in the same model aspects of discrete facility location, topological network design, and dimensioning. We propose a branch-and-bound algorithm based on Lagrangian relaxation to solve the model. Computational results for randomly generated problems are presented showing the quality of our approach. We also present and discuss a real world problem of designing a two-level local access urban telecommunication network and solving it with the proposed methodology.     

Standard passivity-based control for multi-hydro-turbine governing systems with surge tank

This paper addresses the problem of control design for hydro-turbine governing systems with surge tanks from the perspective of standard passivity-based control. The dynamic model of a synchronous machine is considered in conjunction with a model of the hydro-turbine to generate an eleventh-order nonlinear set of differential equations. An Euler–Lagrange representati of the system and its open-loop dynamics is developed. Then, the standard passivity-based control is applied to design a global and asymptotically stable controller in closed-loop operation. The proposed control is decentralized to avoid challenges of communication between the hydro-turbine governing systems. The proposed standard passivity-based control approach is compared with two control approaches. First, a classical standard cascade proportional-integral-derivative controller is applied for the governing system, the automatic voltage regulator, and the excitation system. Second, a sliding mode control is also implemented in the governing system. Two test systems were used to validate the performance of the proposed controller. The first test system is a single machine connected to an infinite bus, and the second test system is the well-known Western System Coordinating Council’s multimachine system. Overall, simulation results show that the proposed controller exhibits a better dynamic response with shorter stabilization times and lower peaks during the transient periods.     

Path loss prediction in urban environment using learning machines and dimensionality reduction techniques

Path loss prediction is a crucial task for the planning of networks in modern mobile communication systems. Learning machine-based models seem to be a valid alternative to empirical and deterministic methods for predicting the propagation path loss. As learning machine performance depends on the number of input features, a good way to get a more reliable model can be to use techniques for reducing the dimensionality of the data. In this paper we propose a new approach combining learning machines and dimensionality reduction techniques. We report results on a real dataset showing the efficiency of the learning machine-based methodology and the usefulness of dimensionality reduction techniques in improving the prediction accuracy.     

Design of hybrid regrouping PSO–GA based sub-optimal networked control system with random packet losses

In this paper, a new approach has been presented to design sub-optimal state feedback regulators over networked control systems with random packet losses. The optimal regulator gains, producing guaranteed stability are designed with the nominal discrete time model of a plant using Lyapunov technique which produces a few set of bilinear matrix inequalities (BMIs). In order to reduce the computational complexity of the BMIs, a genetic algorithm (GA) based approach coupled with the standard interior point methods for LMIs has been adopted. A regrouping particle swarm optimization based method is then employed to optimally choose the weighting matrices for the state feedback regulator design that gets passed through the GA based stability checking criteria i.e. the BMIs. This hybrid optimization methodology put forward in this paper not only reduces the computational difficulty of the feasibility checking condition for optimum stabilizing gain selection but also minimizes other time domain performance criteria like expected value of the set-point tracking error with optimum weight selection based LQR design for the nominal system.     

The relevance of system dynamics to engineering systems design

With the recent advent of new technology using micro computers for the centralised monitoring of information, the scope for totally automatic, real time control of large engineering systems has been advanced. We are entering a period which, retrospectively, may well be seen as the era of control; where for the first time it is both feasible and necessary to design system in terms of the way in which they will be operated, as well as in the traditional terms of their capacity requirements. However, before such comprehensive design can be undertaken compatible advances are required in information usage technology.The purpose of this paper is to demonstrate how the subject of System Dynamics can be used to assist with this problem, though its capacity to model information feedback and hence to model and test alternative system control rules based on the information retrieved. This type of application in the hard system area of engineering provides a strong contrast to the more usual applications of System Dynamics in softer socio-economic systems.The problem described is taken from the coal mining industry and related to the design of large scale underground conveyor belt systems used for the clearance of coal from mines. The demonstration presented, therefore, concerns research in this field, but has considerable scope for application in the general area of large scale bulk handling system.Of particular importance in the results is an indication of the potential savings in physical capacity which can stem from the careful design and implementation of control in such systems.