253 research outputs found
Process control of a laboratory combustor using neural networks
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
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New Stable Inverses of Linear Discrete Time Systems and Application to Iterative Learning Control
Digital control needs discrete time models, but conversion from continuous time, fed by a zero order hold, to discrete time introduces sampling zeros which are outside the unit circle, i.e. non-minimum phase (NMP) zeros, in the majority of the systems. Also, some systems are already NMP in continuous time. In both cases, the inverse problem to find the input required to maintain a desired output tracking, produces an unstable causal control action. The control action will grow exponentially every time step, and the error between time steps also grows exponentially. This prevents many control approaches from making use of inverse models.
The problem statement for the existing stable inverse theorem is presented in this work, and it aims at finding a bounded nominal state-input trajectory by solving a two-point boundary value problem obtained by decomposing the internal dynamics of the system. This results in the causal part specified from the minus infinity time; and its non-causal part from the positive infinity time. By solving for the nominal bounded internal dynamics, the exact output tracking is achieved in the original finite time interval.
The new stable inverses concepts presented and developed here address this instability problem in a different way based on the modified versions of problem states, and in a way that is more practical for implementation. The statements of how the different inverse problems are posed is presented, as well as the calculation and implementation. In order to produce zero tracking error at the addressed time steps, two modified statements are given as the initial delete and the skip step. The development presented here involves: (1) The detection of the signature of instability in both the nonhomogeneous difference equation and matrix form for finite time problems. (2) Create a new factorization of the system separating maximum part from minimum part in matrix form as analogous to transfer function format, and more generally, modeling the behavior of finite time zeros and poles. (3) Produce bounded stable inverse solutions evolving from the minimum Euclidean norm satisfying different optimization objective functions, to the solution having no projection on transient solutions terms excited by initial conditions.
Iterative Learning Control (ILC) iterates with a real world control system repeatedly performing the same task. It adjusts the control action based on error history from the previous iteration, aiming to converge to zero tracking error. ILC has been widely used in various applications due to its high precision in trajectory tracking, e.g. semiconductor manufacturing sensors that repeatedly perform scanning maneuvers. Designing effective feedback controllers for non-minimum phase (NMP) systems can be challenging. Applying Iterative Learning Control (ILC) to NMP systems is particularly problematic. Incorporating the initial delete stable inverse thinkg into ILC, the control action obtained in the limit as the iterations tend to infinity, is a function of the tracking error produced by the command in the initial run. It is shown here that this dependence is very small, so that one can reasonably use any initial run. By picking an initial input that goes to zero approaching the final time step, the influence becomes particularly small. And by simply commanding zero in the first run, the resulting converged control minimizes the Euclidean norm of the underdetermined control history. Three main classes of ILC laws are examined, and it is shown that all ILC laws converge to the identical control history, as the converged result is not a function of the ILC law. All of these conclusions apply to ILC that aims to track a given finite time trajectory, and also apply to ILC that in addition aims to cancel the effect of a disturbance that repeats each run.
Having these stable inverses opens up opportunities for many control design approaches. (1) ILC was the original motivation of the new stable inverses. Besides the scenario using the initial delete above, consider ILC to perform local learning in a trajectory, by using a quadratic cost control in general, but phasing into the skip step stable inverse for some portion of the trajectory that needs high precision tracking. (2) One step ahead control uses a model to compute the control action at the current time step to produce the output desired at the next time step. Before it can be useful, it must be phased in to honor actuator saturation limits, and being a true inverse it requires that the system have a stable inverse. One could generalize this to p-step ahead control, updating the control action every p steps instead of every one step. It determines how small p can be to give a stable implementation using skip step, and it can be quite small. So it only requires knowledge of future desired control for a few steps. (3) Note that the statement in (2) can be reformulated as Linear Model Predictive Control that updates every p steps instead of every step. This offers the ability to converge to zero tracking error at every time step of the skip step inverse, instead of the usual aim to converge to a quadratic cost solution. (4) Indirect discrete time adaptive control combines one step ahead control with the projection algorithm to perform real time identification updates. It has limited applications, because it requires a stable inverse
Dynamic modelling and control of a flexible manoeuvring system.
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
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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
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
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
Book of abstract
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