Gas turbine control and load sharing of a shipboard power system

The objective of this research is to design a controller for a gas turbine of an Electric Shipboard Power System (ESPS) and to develop a load sharing strategy for its energy management. A suitable model for the gas turbine is selected and the effects of the dynamics are investigated for the different loads of the ESPS. The gas turbine controller is a Proportional Integral Derivative (PID) controller, whose parameters are tuned using the Particle Swarm Optimization (PSO) technique. The load on the system has three components: a propulsion load, a pulsed load to simulate a high energy weapon system and a power supply load for the remaining loads such as pumps, lighting systems, etc. Load sharing is inevitable when demand exceeds the available power supply. In this case, based on the priorities of the loads and the available power, a strategy is presented to supply power to the most critical loads. To illustrate this, a load allocation algorithm is developed using stateflow diagrams. The potential of this algorithm is demonstrated by two case studies performed using the three loads, with the highest priority assigned to the propulsion load in case 1, and power supply load in case 2. The results of this research can be further extended to real time applications

Measurements, Models, Systems and Design

531 s.

Advances in PID Control

Since the foundation and up to the current state-of-the-art in control engineering, the problems of PID control steadily attract great attention of numerous researchers and remain inexhaustible source of new ideas for process of control system design and industrial applications. PID control effectiveness is usually caused by the nature of dynamical processes, conditioned that the majority of the industrial dynamical processes are well described by simple dynamic model of the first or second order. The efficacy of PID controllers vastly falls in case of complicated dynamics, nonlinearities, and varying parameters of the plant. This gives a pulse to further researches in the field of PID control. Consequently, the problems of advanced PID control system design methodologies, rules of adaptive PID control, self-tuning procedures, and particularly robustness and transient performance for nonlinear systems, still remain as the areas of the lively interests for many scientists and researchers at the present time. The recent research results presented in this book provide new ideas for improved performance of PID control applications

An expert fuzzy logic controller employing adaptive learning for servo systems

An expert fuzzy logic controller with adaptive learning is proposed as an intelligent controller for servo systems. A key component of this controller is an adaptive learning mechanism which is used to self-regulate the scaling factors and the control action based on the error between the desired value and the plant output. The inference engine of this controller is based on the principle of approximate reasoning and the learning strategy is based on reinforcement learning. A novel approach of model reference adaptive control is also proposed for servo systems. The comparison of the performance between the proposed controller and PID controllers is discussed. The simulation results show that the performance of the proposed controller is better than the conventional approach or previous research. The real-time application demonstrates that a faster response of a servo system can be achieved. Furthermore, the proposed controller is relatively insensitive to variations in the parameters of control systems

Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

Controlled Experiments of Hillslope Coevolution at the Biosphere 2 Landscape Evolution Observatory: Toward Prediction of Coupled Hydrological, Biogeochemical, and Ecological Change

Understanding the process interactions and feedbacks among water, porous geological media, microbes, and vascular plants is crucial for improving predictions of the response of Earth’s critical zone to future climatic conditions. However, the integrated coevolution of landscapes under change is notoriously difficult to investigate. Laboratory studies are limited in spatial and temporal scale, while field studies lack observational density and control. To bridge the gap between controlled laboratory and uncontrollable field studies, the University of Arizona built a macrocosm experiment of unprecedented scale: the Landscape Evolution Observatory (LEO). LEO comprises three replicated, heavily instrumented, hillslope-scale model landscapes within the environmentally controlled Biosphere 2 facility. The model landscapes were designed to initially be simple and purely abiotic, enabling scientists to observe each step in the landscapes’ evolution as they undergo physical, chemical, and biological changes over many years. This chapter describes the model systems and associated research facilities and illustrates how LEO allows for tracking of multiscale matter and energy fluxes at a level of detail impossible in field experiments. Initial sensor, sampler, and soil coring data are already providing insights into the tight linkages between water flow, weathering, and microbial community development. These interacting processes are anticipated to drive the model systems to increasingly complex states and will be impacted by the introduction of vascular plants and changes in climatic regimes over the years to come. By intensively monitoring the evolutionary trajectory, integrating data with mathematical models, and fostering community-wide collaborations, we envision that emergent landscape structures and functions can be linked, and significant progress can be made toward predicting the coupled hydro-biogeochemical and ecological responses to global change

Realising full-scale control in wastewater treatment systems using in situ nutrient sensors

Abstract A major change in paradigm is taking place in the operation of wastewater treatment plants as automatic process control is becoming feasible. This change is due to a number of different reasons, not least the development of online nutrient sensors, which measure the key parameters in the biological nutrient removal processes, i.e. ammonium, nitrate and phosphate. The thesis is about realising full-scale control in wastewater treatment systems using in situ nutrient sensors. The main conclusion of the work is that it is possible to significantly improve the operational performance in full-scale plants by means of relatively simple control structures and controllers based on in situ nutrient sensors. The in situ location should be emphasised as this results in short dead time, hence making simple feedback loops based on proportional and integral actions effective means to control the processes. This conclusion has been reached based on full-scale experiments, where various controllers and control structures for the biological removal of nitrogen and the chemical removal of phosphorous have been tested. The full-scale experiments have shown that it is possible to provide significant savings in energy consumption and precipitation chemicals consumption, reduction in sludge production and improvement of the effluent water quality. The conclusions are supported by model simulations using the COST benchmark simulation platform. The simulations are used for investigating issues regarding the interactions between the main control handles working in the medium time frame (relative gain array analysis). The simulations have also been used for testing various control structures and controllers. Controllers for the following types of control are suggested and tested: „h Control of aeration to obtain a certain effluent ammonium concentration; „h Control of internal recirculation flow rate to obtain maximum inorganic nitrogen removal; „h Control of external carbon dosage together with internal recirculation flow rate to obtain a certain effluent total inorganic nitrogen concentration; „h Optimisation of the choice of sludge age. Additionally, a procedure for implementing new control structures based on nutrient sensor has been proposed. The procedure involves an initial analysis phase, a monitoring phase, an experimenting phase and an automatic process control phase. An international survey with the aim to investigate the correspondence between ICA (instrumentation, control and automation) utilisation and plant performance has been carried out. The survey also gives insight into the current state of ICA applications at wastewater treatment plants

Systems Structure and Control

The title of the book System, Structure and Control encompasses broad field of theory and applications of many different control approaches applied on different classes of dynamic systems. Output and state feedback control include among others robust control, optimal control or intelligent control methods such as fuzzy or neural network approach, dynamic systems are e.g. linear or nonlinear with or without time delay, fixed or uncertain, onedimensional or multidimensional. The applications cover all branches of human activities including any kind of industry, economics, biology, social sciences etc

Tracking Control for Non-Minimum Phase System and Brain Computer Interface

For generations, humans dreamed about the ability to communicate and interact with machines through thought alone or to create devices that can peer into a person’s mind and thoughts. Researchers have developed new technologies to create brain computer interfaces (BCIs), communication systems that do not depend on the brain’s normal output pathways of peripheral nerves and muscles. The objective of the first part of this thesis is to develop a new BCI based on electroencephalography (EEG) to move a computer cursor over a short training period in real time. The work motivations of this part are to increase: speed and accuracy, as in BCI settings, subject has a few seconds to make a selection with a relatively high accuracy. Recently, improvements have been developed to make EEG more accurate by increasing the spatial resolution. One such improvement is the application of the surface Laplacian to the EEG, the second spatial derivative. Tripolar concentric ring electrodes (TCREs) automatically perform the Laplacian on the surface potentials and provide better spatial selectivity and signal-to-noise ratio than conventional EEG that is recorded with conventional disc electrodes. Another important feature using TCRE is the capability to record the EEG and the TCRE EEG (tEEG) signals concurrently from the same location on the scalp for the same electrical activity coming from the brain. In this part we also demonstrate that tEEG signals can enable users to control a computer cursor rapidly in different directions with significantly higher accuracy during their first session of training for 1D and 2D cursor control. Output tracking control of non-minimum phase systems is a highly challenging problem encountered in many practical engineering applications. Classical inversion techniques provide exact output tracking but lead to internal instability, whereas modern inversion methods provide stable asymptotic tracking but produce large transient errors. Both methods provide an approximation of feedback control, which leads to non robust systems, very sensitive to noise, considerable tracking errors and a significant singularity problem. Aiming at the problem of system inversion to the true system, the objective of the second part of this thesis is to develop a new method based on true inversion for minimum phase system and approximate inversion for non-minimum phase systems. The proposed algorithm is automatic and has minimal computational complexities which make it suitable for real-time control. The process to develop the proposed algorithm is partitioned into (1) minimum phase feedforward inverse filter, and (2) non-minimum phase inversion. In a minimum phase inversion, we consider the design of a feedforward controller to invert the response of a feedback loop that has stable zero locations. The complete control system consists of a feedforward controller cascaded with a closed-loop system. The outputs of the resulting inverse filter are delayed versions of the corresponding reference input signals, and delays are given by the vector relative degree of the closed-loop

Full Envelope Control of Nonlinear Plants with Parameter Uncertainty by Fuzzy Controller Scheduling

A full envelope controller synthesis technique is developed for multiple-input single-output (MISO) nonlinear systems with structured parameter uncertainty. The technique maximizes the controller\u27s valid region of operation, while guaranteeing pre-specified transient performance. The resulting controller does not require on-line adaptation, estimation, prediction or model identification. Fuzzy Logic (FL) is used to smoothly schedule independently designed point controllers over the operational envelope and parameter space of the system\u27s model. These point controllers are synthesized using techniques chosen by the designer, thus allowing an unprecedented amount of design freedom. By using established control theory for the point controllers, the resulting nonlinear dynamic controller is able to handle the dynamics of complex systems which can not otherwise be addressed by Fuzzy Logic Control. An analytical solution for parameters describing the membership functions allows the optimization to yield the location of point designs: both quantifying the controller\u27s coverage, and eliminating the need of extensive hand tuning of these parameters. The net result is a decrease in the number of point designs required. Geometric primitives used in the solution all have multi-dimensional interpretations (convex hull, ellipsoid, Voronoi-Delaunay diagrams) which allow for scheduling on n-dimensions, including uncertainty due to nonlinearities and parameter variation. Since many multiple-input multiple-output (MIMO) controller design techniques are accomplished by solving several MISO problems, this work bridges the gap to full envelope control of MIMO nonlinear systems with parameter variation