Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm

The recently developed reference-command tracking version of model predictive static programming (MPSP) is successfully applied to a single-stage closed grinding mill circuit. MPSP is an innovative optimal control technique that combines the philosophies of model predictive control (MPC) and approximate dynamic programming. The performance of the proposed MPSP control technique, which can be viewed as a `new paradigm' under the nonlinear MPC philosophy, is compared to the performance of a standard nonlinear MPC technique applied to the same plant for the same conditions. Results show that the MPSP control technique is more than capable of tracking the desired set-point in the presence of model-plant mismatch, disturbances and measurement noise. The performance of MPSP and nonlinear MPC compare very well, with definite advantages offered by MPSP. The computational speed of MPSP is increased through a sequence of innovations such as the conversion of the dynamic optimization problem to a low-dimensional static optimization problem, the recursive computation of sensitivity matrices and using a closed form expression to update the control. To alleviate the burden on the optimization procedure in standard MPC, the control horizon is normally restricted. However, in the MPSP technique the control horizon is extended to the prediction horizon with a minor increase in the computational time. Furthermore, the MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for online applications of the nonlinear MPC philosophy to real-world industrial process plants. (C) 2014 Elsevier Ltd. All rights reserved.

Probabilistic robust control : theory and applications

In this work, the development of a probabilistic approach to robust control is motivated by structural control applications in civil engineering. Often in civil structural applications, a system's performance is specified in terms of its reliability. In addition, the model and input uncertainty for the system may be described most appropriately using probabilistic or "soft" bounds on the model and input sets. The probabilistic robust control methodology contrasts with existing H∞/μ robust control methodologies that do not use probability information for the model and input uncertainty sets, yielding only the guaranteed (i.e., "worst-case") system performance, and no information about the system's probable performance which would be of interest to civil engineers.

The design objective for the probabilistic robust controller is to maximize the reliability of the uncertain structure/controller system for a probabilistically-described uncertain excitation. The robust performance is computed for a set of possible models by weighting the conditional performance probability for a particular model by the probability of that model, then integrating over the set of possible models. This integration is accomplished efficiently using an asymptotic approximation. The probable performance can be optimized numerically over the class of allowable controllers to find the optimal controller. Also, if structural response data becomes available from a controlled structure, its probable performance can easily be updated using Bayes's Theorem to update the probability distribution over the set of possible models. An updated optimal controller can then be produced, if desired, by following the original procedure. Thus, the probabilistic framework integrates system identification and robust control in a natural manner.

The probabilistic robust control methodology is applied to two systems in this thesis. The first is a high-fidelity computer model of a benchmark structural control laboratory experiment. For this application, uncertainty in the input model only is considered. The probabilistic control design minimizes the failure probability of the benchmark system while remaining robust with respect to the input model uncertainty. The performance of an optimal low-order controller compares favorably with higher-order controllers for the same benchmark system which are based on other approaches. The second application is to the Caltech Flexible Structure, which is a light-weight aluminum truss structure actuated by three voice coil actuators. A controller is designed to minimize the failure probability for a nominal model of this system. Furthermore, the method for updating the model-based performance calculation given new response data from the system is illustrated.

Plug Force Monitoring for the Control and Optimisation of the Thermoforming Process

Thermoforming processes generally employ sheet temperature monitoring as the primary means of process control. In this paper the development of an alternative system that monitors plug force is described. Tests using a prototype device have shown that the force record over a forming cycle creates a unique map of the process operation. Key process features such as the sheet modulus, sheet sag and the timing of the process stages may be readily observed, and the effects of changes in all of the major processing parameters are easily distinguished. Continuous, cycle-to-cycle tests show that the output is consistent and repeatable over a longer time frame, providing the opportunity for development of an on-line process control system. Further testing of the system is proposed.

Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. (c) 2004 Elsevier Ltd. All rights reserved.

How crucial is back gate misalignment/oversize in double gate MOSFETs for ultra-low-voltage analog/rf applications?

In the present work, by investigating the influence of source/drain (S/D) extension region engineering (also known as gate-underlap architecture) in planar Double Gate (DG) SOI MOSFETs, we offer new design insights to achieve high tolerance to gate misalignment/oversize in nanoscale devices for ultra-low-voltage (ULV) analog/rf applications. Our results show that (i) misaligned gate-underlap devices perform significantly better than DC devices with abrupt source/drain junctions with identical misalignment, (ii) misaligned gate underlap performance (with S/D optimization) exceeds perfectly aligned DG devices with abrupt S/D regions and (iii) 25% back gate misalignment can be tolerated without any significant degradation in cut-off frequency (f(T)) and intrinsic voltage gain (A(VO)). Gate-underlap DG devices designed with spacer-to-straggle ratio lying within the range 2.5 to 3.0 show best tolerance to misaligned/oversize back gate and indeed are better than self-aligned DG MOSFETs with non-underlap (abrupt) S/D regions. Impact of gate length and silicon film thickness scaling is also discussed. These results are very significant as the tolerable limit of misaligned/oversized back gate is considerably extended and the stringent process control requirements to achieve self-alignment can be relaxed for nanoscale planar ULV DG MOSFETs operating in weak-inversion region. The present work provides new opportunities for realizing future ULV analog/rf design with nanoscale gate-underlap DG MOSFETs. (C) 2008 Elsevier Ltd. All rights reserved.

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.

Simplified parameter adaptive control

A simple parameter adaptive controller design methodology is introduced in which steady-state servo tracking properties provide the major control objective. This is achieved without cancellation of process zeros and hence the underlying design can be applied to non-minimum phase systems. As with other self-tuning algorithms, the design (user specified) polynomials of the proposed algorithm define the performance capabilities of the resulting controller. However, with the appropriate definition of these polynomials, the synthesis technique can be shown to admit different adaptive control strategies, e.g. self-tuning PID and self-tuning pole-placement controllers. The algorithm can therefore be thought of as an embodiment of other self-tuning design techniques. The performances of some of the resulting controllers are illustrated using simulation examples and the on-line application to an experimental apparatus.

Novel developments in process optimisation using predictive control

In industrial practice, constrained steady state optimisation and predictive control are separate, albeit closely related functions within the control hierarchy. This paper presents a method which integrates predictive control with on-line optimisation with economic objectives. A receding horizon optimal control problem is formulated using linear state space models. This optimal control problem is very similar to the one presented in many predictive control formulations, but the main difference is that it includes in its formulation a general steady state objective depending on the magnitudes of manipulated and measured output variables. This steady state objective may include the standard quadratic regulatory objective, together with economic objectives which are often linear. Assuming that the system settles to a steady state operating point under receding horizon control, conditions are given for the satisfaction of the necessary optimality conditions of the steady-state optimisation problem. The method is based on adaptive linear state space models, which are obtained by using on-line identification techniques. The use of model adaptation is justified from a theoretical standpoint and its beneficial effects are shown in simulations. The method is tested with simulations of an industrial distillation column and a system of chemical reactors.

Automatic control of food manufacturing processes. 2nd edition

In the ten years since the first edition of this book appeared there have been significant developments in food process engineering, notably in biotechnology and membrane application. Advances have been made in the use of sensors for process control, and the growth of information technology and on-line computer applications continues apace. In addition, plant investment decisions are increasingly determined by quality assurance considerations and have to incorporate a greater emphasis on health and safety issues. The content of this edition has been rearranged to include descriptions of recent developments and to reflect the influence of new technology on the control and operations of automated plant. Original examples have been retained where relevant and these, together with many new illustrations, provide a comprehensive guide to good practice.

Evaluation of fungicide applications to sweet orange at different flowering stages for control of postbloom fruit drop caused by Colletotrichum acutatum

Postbloom fruit drop (PFD), caused by Colletotrichum acutatum, produces blossom blight, fruit abscission and persistent calyces. in groves of Pera-Rio and Natal sweet orange located in Santa Cruz do Rio Pardo and Rincao, São Paulo, Brazil, four experiments were carried out to evaluate the effectiveness of fungicides sprayed alone or as mixtures, at different flowering stages for the control of PFD of citrus. The number of symptomatic flowers, the percentage of fruit set (FS), and the relationship between persistent calyces and total fruit weight per plant were evaluated. The fungicides carbendazim and folpet were sprayed at 0.50 ml and 1.25 ga.i. l(-1) of water, respectively, were superior by all the criteria to the other treatments. Carbendazim and folpet fungicides performed best when they were applied at the green bud through hollow ball stages. Difenoconazole, independent of application timing, was less effective by all criteria used. Application of mancozeb at 1.60 ga.i. l(-1) at the green bud stage followed by application of mancozeb in a tank mix with carbendazim or folpet at 1.0 ml and 1.25 g a.i. l(-1), respectively, during green bud bloom and hollow ball stages were effective for disease control. Carbendazim combined with 0.25% KNO3, reduced the number of persistent calyces and increased fruit production significantly. Applications must be made between green bud and hollow ball stages for best control. Applications only at hollow ball or open flower stages did not provide effective disease control. (C)2007 Elsevier Ltd. All rights reserved.

Double sampling (x)over-bar control chart for a first-order autoregressive moving average process model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A class of multiobjective control problems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A synthetic control chart for monitoring the process mean and variance

Purpose - The aim of this paper is to present a synthetic chart based on the non-central chi-square statistic that is operationally simpler and more effective than the joint X̄ and R chart in detecting assignable cause(s). This chart will assist in identifying which (mean or variance) changed due to the occurrence of the assignable causes. Design/methodology/approach - The approach used is based on the non-central chi-square statistic and the steady-state average run length (ARL) of the developed chart is evaluated using a Markov chain model. Findings - The proposed chart always detects process disturbances faster than the joint X̄ and R charts. The developed chart can monitor the process instead of looking at two charts separately. Originality/value - The most important advantage of using the proposed chart is that practitioners can monitor the process by looking at only one chart instead of looking at two charts separately. © Emerald Group Publishing Limted.

Double sampling X̄ control chart for a first order autoregressive process

In this paper we propose the Double Sampling X̄ control chart for monitoring processes in which the observations follow a first order autoregressive model. We consider sampling intervals that are sufficiently long to meet the rational subgroup concept. The Double Sampling X̄ chart is substantially more efficient than the Shewhart chart and the Variable Sample Size chart. To study the properties of these charts we derived closed-form expressions for the average run length (ARL) taking into account the within-subgroup correlation. Numerical results show that this correlation has a significant impact on the chart properties.

The benefits of soft sensor and multi-rate control for the implementation of wireless networked control systems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)