Volume 13, Number 1, 2016
Special Issue on Innovative Applications of Automation and Computing Technology (pp.1-88)
Recent advances in wireless communication technologies and auto-mobile industry have triggered a significant research interest in the field of vehicular ad-hoc networks (VANETs) over the past few years. A vehicular network consists of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications supported by wireless access technologies such as IEEE 802.11p. This innovation in wireless communication has been envisaged to improve road safety and motor traffic efficiency in near future through the development of intelligent transportation system (ITS). Hence, governments, auto-mobile industries and academia are heavily partnering through several ongoing research projects to establish standards for VANETs. The typical set of VANET application areas, such as vehicle collision warning and traffic information dissemination have made VANET an interesting field of mobile wireless communication. This paper provides an overview on current research state, challenges, potentials of VANETs as well as the ways forward to achieving the long awaited ITS.
This paper presents a simultaneous H2/H∞ stabilization problem for the chemical reaction systems which can be modeled as a finite collection of subsystems. A single dynamic output feedback controller which simultaneously stabilizes the multiple subsystems and captures the mixed H2/H∞ control performance is designed. To ensure that the stability condition, the H2 characterization and the H∞ characterization can be enforced within a unified matrix inequality framework, a novel technique based on orthogonal complement space is developed. Within such a framework, the controller gain is parameterized by the introduction of a common free positive definite matrix, which is independent of the multiple Lyapunov matrices. An iterative linear matrix inequality (ILMI) algorithm using Matlab Yalmip toolbox is established to deal with the proposed framework. Simulation results of a typical chemical reaction system are exploited to show the validity of the proposed methodology.
This paper reviews existing approaches to the airport gate assignment problem (AGAP) and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods (to get robust optimization) while ensuring appropriate matching between the size of each aircraft and its assigned gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem. An illustrative example is given to show the effectiveness and efficiency of the algorithm. The algorithm performance is further demonstrated using data of a terminal from Beijing Capital International Airport (PEK).
This paper proposes an automatic model-based viewpoint planning method, which can achieve high precision and high efficiency for freeform surfaces inspection using plane structured light scanners. The surface model is utilized in stereolithography format, which is widely used as an industrial standard. The proposed method consists of 4 steps: topology reconstruction, mesh refinement, scan direction determination and viewpoint generation. In the first step, the topology structure of the surface model is reconstructed according to a designed data structure, based on which a neighborhood search algorithm is developed. In the second step, big facets in the surface model are segmented into several small ones, which are suitable for viewpoint planning. In the third step, an initial scan region of a viewpoint is grouped by the neighborhood search algorithm combining with total area and normal vector restrictions. Accordingly, the scan direction is determined by the normal vectors of facets in the initial scan region. In the fourth step, the position, the orientation, and the final scan region of the viewpoint are determined by 4 scan constraints, i.e., field of view, working distance range, view angle and overlap. Experimental results verify the effectiveness and advantages of the proposed method.
Combined heat and power (CHP) refers to a process/system designed to utilize the waste or residual heat from a power generation process. Thus, a CHP plant can produce both electricity and heat. The nature of such a combination makes the process more complex than any single power generation process or boiler heating system. The paper focuses on modelling study and analysis of energy efficiency of the University of Warwick micro-CHP power plant. In this CHP modelling study, a gas turbine module is built to provide driving power and methane is used as fuel gas. Heat recovery system and auxiliary boiler modules are developed for thermal power generation. All the sub-systems are validated by comparing the simulation results with the operating data collected from the CHP plant. The dynamic performance of the key CHP process outputs is studied with respect to the variation of the input syngas stream, including electricity generation, thermal power output and water output temperature. Simplified controllers are also applied to the gas engineheat recovery subsystem and auxiliary boiler. Simulation results with/without feedback control are both analyzed. The study has highlighted the key factors which influence the plant performance and suggested the strategy for potential energy efficiency improvement.
Standard genetic algorithms (SGAs) are investigated to optimise discrete-time proportional-integral-derivative (PID) controller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms (SGAs) are used to identify control oriented models of the plant which are subsequently used for controller optimisation. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction.
Waterflooding is a process where water is injected into an oil reservoir to supplement its natural pressure for increment in productivity. The reservoir properties are highly heterogeneous, its states change as production progresses which require varying injection and production settings for economic recovery. As water is injected into the reservoir, more oil is expected to be produced. There is also likelihood that water is produced in association with the oil. The worst case is when the injected water meanders through the reservoir, it bypasses pools of oil and gets produced. Therefore, any effort geared toward finding the optimal settings to maximize the value of this venture can never be over emphasized. Waterflooding can be formulated as an optimal control problem. However, traditional optimal control is an open-loop solution, hence cannot cope with various uncertainties inevitably existing in any practical systems. Reservoir models are highly uncertain. Its properties are known with some degrees of certainty near the well-bore region only. In this work, a novel data-driven approach for control variable (CV) selection was proposed and applied to reservoir waterflooding process for a feedback strategy resulting in optimal or near optimal operation. The results indicated that the feedback control method was close to optimal in the absence of uncertainty. The loss recorded in the value of performance index, net present value (NPV) was only 0.26%. Furthermore, the new strategy performs better than the open-loop optimal control solution when system/model mismatch was considered. The performance depends on the scale of the uncertainty introduced. A gain in NPV as high as 30.04% was obtained.
Severe slugging flow is always challenging in oil & gas production, especially for the current offshore based production. The slugging flow can cause a lot of problems, such as those relevant to production safety, fatigue as well as capability. As one typical phenomenon in multi-phase flow dynamics, the slug can be avoided or eliminated by proper facility design or control of operational conditions. Based on a testing facility which can emulate a pipeline-riser or a gas-lifted production well in a scaled-down manner, this paper experimentally studies the correlations of key operational parameters with severe slugging flows. These correlations are reflected through an obtained stable surface in the parameter space, which is a natural extension of the bifurcation plot. The maximal production opportunity without compromising the stability is also studied. Relevant studies have already showed that the capability, performance and efficiency of anti-slug control can be dramatically improved if these stable surfaces can be experimentally determined beforehand. The paper concludes that obtaining the stable surface on the new developed map can significantly improve the production rate in a control scheme. Even though the production rate can be further improved by moving the stable surface using advanced control strategies, the constant inputs can in some cases be preferable due to the easier implementation.