253 research outputs found

    Process control of a laboratory combustor using neural networks

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    Active feedback and feedforward-feedback control systems based on static-trained feedforward multi-layer-perceptron (FMLP) neural networks were designed and demonstrated, by experiment and simulation, for selected species from a laboratory two stage combustor. These virtual controllers functioned through a Visual Basic platform. A proportional neural network controller (PNNC) was developed for a monotonic control problem - the variation of outlet oxygen level with overall equivalence ratio (Φ0). The FMLP neural network maps the control variable to the manipulated variable. This information is in turn transferred to a proportional controller, through the variable control bias value. The proposed feedback control methodology is robust and effective to improve control performance of the conventional control system without drastic changes in the control structure. A detailed case study in which two clusters of FMLP neural networks were applied to a non-monotonic control problem - the variation of outlet nitric oxide level with first-stage equivalence ratio (Φ0) - was demonstrated. The two clusters were used in the feedforward-feedback control scheme. The key novelty is the functionalities of these two network clusters. The first cluster is a neural network-based model-predictive controller (NMPC). It identifies the process disturbance and adjusts the manipulated variables. The second cluster is a neural network-based Smith time-delay compensator (NSTC) and is used to reduce the impact of the long sampling/analysis lags in the process. Unlike other neural network controllers reported in the control field, NMPC and NSTC are efficiently simple in terms of the network structure and training algorithm. With the pre-filtered steady-state training data, the neural networks converged rapidly. The network transient response was originally designed and enabled here using additional tools \u27and mathematical functions in the Visual Basic program. The controller based on NMPC/NSTC showed a superior performance over the conventional proportional-integral derivative (PID) controller. The control systems developed in this study are not limited to the combustion process. With sufficient steady-state training data, the proposed control systems can be applied to control applications in other engineering fields

    Dynamic modelling and control of a flexible manoeuvring system.

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    In this research a twin rotor multi-input multi-output system (TRMS), which is a laboratory platform with 2 degrees of freedom (DOF) is considered. Although, the TRMS does not fly, it has a striking similarity with a helicopter, such as system nonlinearities and cross-coupled modes. Therefore, the TRMS can be perceived as an unconventional and complex "air vehicle" that poses formidable challenges in modelling, control design and analysis, and implementation. These issues constitute the scope of this research. Linear and nonlinear models for the vertical movement of the TRMS are obtained via system identification techniques using black-box modelling. The approach yields input-output models without a priori defined model structure or specific parameter settings reflecting any physical attributes of the system. Firstly, linear parametric models, characterising the TRMS in its hovering operation mode, are obtained using the potential of recursive least squares (RLS) estimation and genetic algorithms (GAs). Further, a nonlinear model using multi-layer perceptron (MLP) neural networks (NNs) is obtained. Such a high fidelity nonlinear model is often required for nonlinear system simulation studies and is commonly employed in the aerospace industry. Both time and frequency domain analyses are utilised to investigate and develop confidence in the models obtained. The frequency domain verification method is a useful tool in the validation of extracted parametric models. It allows high-fidelity verification of dynamic characteristics over a frequency range of interest. The resulting models are utilized in designing controllers for low frequency vibration suppression, development of suitable feedback control laws for set-point tracking, and design of augmented feedforward and feedback control schemes for both vibration suppression and set-point tracking performance. The modelling approaches presented here are shown to be suitable for modelling complex new generation air vehicles, whose flight mechanics are not well understood. Modelling of the TRMS revealed the presence of resonance modes, which are responsible for inducing unwanted vibrations in the system. Command shaping 11 control strategies are developed to reduce motion and uneven mass induced vibrations, produced by the main rotor during the vertical movement around the lateral axis of the TRMS rig. 2-impulse, 3-impulse and 4-impulse sequence input shapers and Iow-pass and band-stop digital filters are developed to shape the command signals such that the resonance modes are not overly excited. The effectiveness of this concept is then demonstrated in both simulation and real-time experimental environments in terms of level of vibration reduction using power spectral density profiles of the system response. Combinations of intelligent and conventional techniques are commonly used the control of complex dynamic systems. Such hybrid schemes have proved to be efficient and can overcome the deficiencies of conventional and intelligent controllers alone. The current study is confined to the development of two forms of hybrid control schemes that combine fuzzy control and conventional PID compensator for input tracking performance. The two hybrid control strategies comprising conventional PO control plus PlO compensator and PO-type fuzzy control plus PlO compensator are developed and implemented for set-point tracking control of the vertical movement of the TRMS rig. It is observed that the hybrid control schemes are superior to other feedback control strategies namely, PlO compensator, pure PO-type and PI-type fuzzy controllers in terms of time domain system behaviour. This research also witnesses investigations into the development of an augmented feedforward and feedback control scheme (AFFCS) for the control of rigid body motion and vibration suppression of the TRMS. The main goal of this framework is to satisfy performance objectives in terms of robust command tracking, fast system response and minimum residual vibration. The developed control strategies have been designed and implemented within both simulation and real-time environments of the TRMS rig. The employed control strategies are shown to demonstrate acceptable performances. The obtained results show that much improved tracking is achieved on positive and negative cycles of the reference signal, as compared to that without any control action. The system performance with the feedback controller is significantly improved when the feedforward control component is added. This leads to the conclusion that augmenting feedback control with feedforward method can lead to more practical and accurate control of flexible systems such as the TRMS

    On adaptive control and particle filtering in the automatic administration of medicinal drugs

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    Automatic feedback methodologies for the administration of medicinal drugs offer undisputed potential benefits in terms of cost reduction and improved clinical outcomes. However, despite several decades of research, the ultimate safety of many--it would be fair to say most--closed-loop drug delivery approaches remains under question and manual methods based on clinicians' expertise are still dominant in clinical practice. Key challenges to the design of control systems for these applications include uncertainty in pharmacological models, as well as intra- and interpatient variability in the response to drug administration. Pharmacological systems may feature nonlinearities, time delays, time-varying parameters and non-Gaussian stochastic processes. This dissertation investigates a novel multi-controller adaptive control strategy capable of delivering safe control for closed-loop drug delivery applications without impairing clinicians' ability to make an expert assessment of a clinical situation. Our new feedback control approach, which we have named Robust Adaptive Control with Particle Filtering (RAC-PF), estimates a patient's individual response characteristic in real-time through particle filtering and uses the Bayesian inference result to select the most suitable controller for closed-loop operation from a bank of candidate controllers designed using the robust methodology of mu-synthesis. The work is presented as four distinct pieces of research. We first apply the existing approach of Robust Multiple-Model Adaptive Control (RMMAC), which features robust controllers and Kalman filter estimators, to the case-study of administration of the vasodepressor drug sodium nitroprusside and examine benefits and drawbacks. We then consider particle filtering as an alternative to Kalman filter-based methods for the real-time estimation of pharmacological dose-response, and apply this to the nonlinear pharmacokinetic-pharmacodynamic model of the anaesthetic drug propofol. We ultimately combine particle filters and robust controllers to create RAC-PF, and test our novel approach first in a proof-of-concept design and finally in the case of sodium nitroprusside. The results presented in the dissertation are based on computational studies, including extensive Monte-Carlo simulation campaigns. Our findings of improved parameter estimates from noisy observations support the use of particle filtering as a viable tool for real-time Bayesian inference in pharmacological system identification. The potential of the RAC-PF approach as an extension of RMMAC for closed-loop control of a broader class of systems is also clearly highlighted, with the proposed new approach delivering safe control of acute hypertension through sodium nitroprusside infusion when applied to a very general population response model. All approaches presented are generalisable and may be readily adapted to other drug delivery instances

    Applications of MATLAB in Science and Engineering

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    The book consists of 24 chapters illustrating a wide range of areas where MATLAB tools are applied. These areas include mathematics, physics, chemistry and chemical engineering, mechanical engineering, biological (molecular biology) and medical sciences, communication and control systems, digital signal, image and video processing, system modeling and simulation. Many interesting problems have been included throughout the book, and its contents will be beneficial for students and professionals in wide areas of interest

    Systems and control : 21th Benelux meeting, 2002, March 19-21, Veldhoven, The Netherlands

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    Book of abstract

    Activity Report: Automatic Control 2011

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    On Approximation of Linear Network Systems

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    On Approximation of Linear Network Systems

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