4,135 research outputs found
Nonminimal state space approach to multivariable ramp metering control of motorway bottlenecks
The paper discusses the automatic control of motorway traffic flows utilising ramp metering, i.e. traffic lights on the on-ramp entrances. A multivariable ramp metering system is developed, based on the nonminimal state space (NMSS) approach to control system design using adaptive proportional-integral-plus, linear quadratic (PIP–LQ) optimal controllers. The controller is evaluated on a nonlinear statistical traffic model (STM) simulation of the Amsterdam motorway ring road near the Coen Tunnel
Space Structures: Issues in Dynamics and Control
A selective technical overview is presented on the vibration and control of large space structures, the analysis, design, and construction of which will require major technical contributions from the civil/structural, mechanical, and extended engineering communities. The immediacy of the U.S. space station makes the particular emphasis placed on large space structures and their control appropriate. The space station is but one part of the space program, and includes the lunar base, which the space station is to service. This paper attempts to summarize some of the key technical issues and hence provide a starting point for further involvement. The first half of this paper provides an introduction and overview of large space structures and their dynamics; the latter half discusses structural control, including control‐system design and nonlinearities. A crucial aspect of the large space structures problem is that dynamics and control must be considered simultaneously; the problems cannot be addressed individually and coupled as an afterthought
Data-based mechanistic modelling, forecasting, and control.
This article briefly reviews the main aspects of the generic data based mechanistic (DBM) approach to modeling stochastic dynamic systems and shown how it is being applied to the analysis, forecasting, and control of environmental and agricultural systems. The advantages of this inductive approach to modeling lie in its wide range of applicability. It can be used to model linear, nonstationary, and nonlinear stochastic systems, and its exploitation of recursive estimation means that the modeling results are useful for both online and offline applications. To demonstrate the practical utility of the various methodological tools that underpin the DBM approach, the article also outlines several typical, practical examples in the area of environmental and agricultural systems analysis, where DBM models have formed the basis for simulation model reduction, control system design, and forecastin
Rudder roll stabilization for ships
This paper describes the design of an autopilot for rudder roll stabilization for ships. This autopilot uses the rudder not only for course keeping but also for reduction of the roll. The system has a series of properties which make the controller design far from straightforward: the process has only one input (the rudder angle) and two outputs (the heading and the roll angle); the transfer from rudder to roll is non-minimum-phase; because large and high-frequency rudder motions are necessary, the non-linearities of the steering machine cannot be disregarded; the disturbances caused by the waves vary considerably in amplitude and frequency spectrum.\ud
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In order to solve these problems a new approach to the LQG method has been developed. The control algorithms were tested by means of computer simulations, scale-model experiments and full-scale trials at sea. The results indicate that a rudder roll stabilization system is able to reduce the roll as well as a conventional fin stabilization system, while it requires less investments. Based on the results obtained in this project the Royal Netherlands Navy has decided to implement rudder roll stabilization on a series of ships under construction at this moment
Cost effective combined axial fan and throttling valve control of ventilation rate
This paper is concerned with Proportional-Integral-Plus (PIP) control of ventilation rate in mechanically ventilated agricultural buildings. In particular, it develops a unique fan and throttling valve control system for a 22m3 test chamber, representing a section of a livestock building or glasshouse, at the Katholieke Universiteit Leuven. Here, the throttling valve is employed to restrict airflow at the outlet, so generating a higher static pressure difference over the control fan. In contrast with previous approaches, however, the throttling valve is directly employed as a second control actuator, utilising airflow from either the axial fan or natural ventilation. The new combined fan/valve configuration is compared with a commercially available PID-based controller and a previously developed scheduled PIP design, yielding a reduction in power consumption in both cases of up to 45%
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Adaptive Optimal Control The Thinking Man's GPC
Exploring connections between adaptive control theory and practice, this book treats the techniques of linear quadratic optimal control and estimation (Kalman filtering), recursive identification, linear systems theory and robust arguments
Continuous-time self-tuning algorithms
This thesis proposes some new self-tuning algorithms. In contrast to the conventional discrete-time approach to self-tuning control, the continuous-time approach is used here, that is continuous-time design but digital implementation is used. The proposed underlying control methods are combined with a continuous-time version of the well-known discrete recursive least squares algorithms. The continuous-time estimation scheme is chosen to maintain the continuous-time nature of the algorithms. The first new algorithm proposed is emulator-based relay control (which has already been described in a paper by the author). The algorithm is based on the idea of constructing the switching surface by emulators; that is, unrealisable output derivatives are replaced by their emulated values. In particular, the relay is forced to operate in the sliding mode. In this case, it is shown that emulator-based control and its proposed relay version become equivalent in the sense that both give the same control law. The second new algorithm proposed is a continuous-time version of the discrete-time generalized predictive control (GPC) of Clarke et al (which has already been described in a paper by the author). The algorithm, continuous-time generalized predictive control (CGPC), is based on similar ideas to the GPC, however the formulation is very different. For example, the output prediction is accomplished by using the Taylor series expansion of the output and emulating the output derivatives involved. A detailed closed-loop analysis of this algorithm is also given. It is shown that the CGPC control law only changes the closed-loop pole locations leaving the open-loop zeros untouched (except one special case). It is also shown that LQ control can be considered in the CGPC framework. Further, the CGPC is extended to include some design polynomials so that the model-following and pole-placement control can be considered in the same framework. A third new algorithm, a relay version of the CGPC, is described. The method is based on the ideas of the emulator-based relay control and again it is shown that the CGPC and its relay version become equivalent when the relay operates in the sliding mode. Finally, the CGPC ideas are extended to the multivariable systems and the resulting closed-loop system is analysed in some detail. It is shown that some special choice of design parameters result in a decoupled closed-loop system for certain systems. In addition, it is shown that if the system is decouplable, it is possible to obtain model-following control. It is also shown that LQ control, as in the scalar case, can be considered in the same framework. An illustrative simulation study is also provided for all of the above methods throughout the thesis
Optimal Weighting for Exam Composition
A problem faced by many instructors is that of designing exams that
accurately assess the abilities of the students. Typically these exams are
prepared several days in advance, and generic question scores are used based on
rough approximation of the question difficulty and length. For example, for a
recent class taught by the author, there were 30 multiple choice questions
worth 3 points, 15 true/false with explanation questions worth 4 points, and 5
analytical exercises worth 10 points. We describe a novel framework where
algorithms from machine learning are used to modify the exam question weights
in order to optimize the exam scores, using the overall class grade as a proxy
for a student's true ability. We show that significant error reduction can be
obtained by our approach over standard weighting schemes, and we make several
new observations regarding the properties of the "good" and "bad" exam
questions that can have impact on the design of improved future evaluation
methods
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