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PubMed2002

Model predictive control helps to regulate slow processes--robust barrel temperature control.

Slow temperature control is a challenging control problem. The problem becomes even more challenging when multiple zones are involved, such as in barrel temperature control for extruders. Often, strict closed-loop performance requirements (such as fast startup with no overshoot and maintaining tight temperature control during production) are given for such applications. When characteristics of the system are examined, it becomes clear that a commonly used proportional plus integral plus derivative (PID) controller cannot meet such performance specifications for this kind of system. The system either will overshoot or not maintain the temperature within the specified range during the production run. In order to achieve the required performance, a control strategy that utilizes techniques such as model predictive control, autotuning, and multiple parameter PID is formulated. This control strategy proves to be very effective in achieving the desired specifications, and is very robust.

Computer Simulation↗

PubMed2004

Solution of low-dimensional constrained model predictive control problems.

Large benefits are possible by utilizing the solution of the constrained optimization problem involved in model predictive control. For a special case of these problems, the solution can be obtained relatively easily from its relationship with the unconstrained optimum. In this paper, a visualization of the relationship between the constrained and unconstrained optimum is presented. Based upon this relationship, a method for finding the constrained optimum is proposed that is suitable for low-dimensional control systems. A comparison with a linear programming formulation on 2 x 2 and 3 x 3 problems shows that the computational effort can be 10-35 times lower. For such processes, the proposed approach may allow one to avail the benefits of optimization by using the small process control systems already present in many plants.

Journal Article↗

PubMed2000

Application of model-predictive control based on artificial neural networks to optimize the fed-batch process for riboflavin production.

The fed-batch process for commercial production of riboflavin (vitamin B2) was optimized on-line using model-predictive control based on artificial neural networks (ANNs). The information required for process models was extracted from both historical data and heuristic rules. After each cultivation the process model was readapted off-line to include the most recent process data. The control signal (feed rate), however, was optimized on-line at each sampling interval. An optimizer simulated variations in the control signal and assessed the forecasted model outputs according to an objective function. The optimum feed profile for increasing the product yield (YB2/S) and the amount of riboflavin at the time of harvesting was adjusted continuously and applied to the process. In contrast to the control by set-point profiles, the novel ANN-control is able to react on-line to variations in the process and also to incorporate the new process information continuously. As a result, both the total amount of riboflavin produced and the product yield increased systematically by more than 10% and the reproducibility of seven subsequently optimized batches was enhanced.

Algorithms↗

PubMedDate not supplied

Cybernetic model predictive control of a continuous bioreactor with cell recycle.

The control of poly-beta-hydroxybutyrate (PHB) productivity in a continuous bioreactor with cell recycle is studied by simulation. A cybernetic model of PHB synthesis in Alcaligenes eutrophus is developed. Model parameters are identified using experimental data, and simulation results are presented. The model is interfaced to a multirate model predictive control (MPC) algorithm. PHB productivity and concentration are controlled by manipulating dilution rate and recycle ratio. Unmeasured time varying disturbances are imposed to study regulatory control performance, including unreachable setpoints. With proper controller tuning, the nonlinear MPC algorithm can track productivity and concentration setpoints despite a change in the sign of PHB productivity gain with respect to dilution rate. It is shown that the nonlinear MPC algorithm is able to track the maximum achievable productivity for unreachable setpoints under significant process/model mismatch. The impact of model uncertainty upon controller performance is explored. The multirate MPC algorithm is tested using three controllers employing models that vary in complexity of regulation. It is shown that controller performance deteriorates as a function of decreasing biological complexity.

Algorithms↗

PubMed2002

Grouped-neural network modeling for model predictive control.

A group of feed-forward neural networks (NNs), each providing the prediction of an individual process output at a future step, is used as the dynamic prediction model for the model-based predictive control (MPC) scheme in the proposed work. These NNs are parallel (independent) rather than cascaded--they are trained and implemented in parallel. Therefore, the complexity and effort in the training stage is decreased and compounded error propagation is eliminated from the prediction. A new strategy of compensating for the process-model mismatch under this grouped-NN model structure is also developed. Effectiveness of the scheme as a general nonlinear MPC is demonstrated by simulation results.

Algorithms↗

PubMed2000

Integration of advanced process control and full-scale dynamic simulation

In the process control industry, multivariable model predictive controller and dynamic simulation for operator training are usually available in separate packages. It is very difficult for the operators and plant engineers to find good tools for them to get trained in multivariable advanced process control. This paper presents a system, which integrates the advanced process control and full-scale dynamic simulation. The advanced process control uses multivariable model predictive control techniques. The model used in the predictive control algorithms is generated from the dynamic simulated process. The advanced process controller can control the simulated plant directly, or through a DCS system to control the simulated plant. The combined system provides an excellent environment for training operators in process operation with multivariable advanced process control. The same environment is also very useful for engineers in designing and tuning the advanced process controllers, and in testing communication between the advanced process controller and the DCS systems, or the other type of process control systems.

Journal Article↗

PubMed1992

Multiple-model adaptive predictive control of mean arterial pressure and cardiac output.

A multiple-model adaptive predictive controller has been designed to simultaneously regulate mean arterial pressure and cardiac output in congestive heart failure subjects by adjusting the infusion rates of nitroprusside and dopamine. The algorithm is based on the multiple-model adaptive controller and utilizes model predictive controllers to provide reliable control in each model subspace. A total of 36 linear small-signal models were needed to span the entire space of anticipated responses. To reduce computation time, only the six models with the highest probabilities were used in the control calculations. The controller was evaluated on laboratory animals that were either surgically or pharmacologically altered to exhibit symptoms of congestive heart failure. During trials, the controller performance was robust with respect to excessive switching between models and nonconvergence to a single dominant model. A comparison is also made with a previous multiple-drug controller design.

Animals↗

PubMedDate not supplied

How to control blood glucose under continuous glucose challenge.

A newly improved method for controlling blood glucose was compared with the standard model predictive controller under continuous glucose infusion. Continuous intravenous glucose infusion at rates of 50 or 100 mg/kg/hour was conducted on pancreatectomized dogs. An improved blood glucose control method using a combination of the proportional controller in the initial stage and the model predictive controller in the later stage was compared with the simple model predictive controller. The parameters of the controller were determined by identifying individual responses to the infused insulin during the first 60 minutes. The parameters of the proportional controller were changed at 60, 90, and 120 minutes to reflect the response to the infused insulin. The simple model predictive controller was able to reach the target level in the usual manner under the low infusion rate of glucose. However, under glucose infusion rates of 100 mg/kg/hour and more, it was difficult to reach the target level within 8 hours. In contrast, the improved system could reach the target level within 5 to 8 hours even under continuous glucose challenge. Addition of the modified proportional controller to the model predictive controller can stabilize the blood glucose control even under continuous glucose infusion.

Animals↗

PubMed2003

Minimization of carbon losses in pilot-scale outdoor photobioreactors by model-based predictive control.

The optimization of carbon use in pilot-scale outdoor tubular photobioreactors is investigated in this study. The behavior of a 0.20-m(3) tubular photobioreactor was studied, with and without algae, by steady-state and pulse dynamic-response analysis experiments. A model of the system was obtained and implemented in a programmable control unit and was used to control the reactor under normal production conditions. Results showed that, using and on-off control, the mean daily CO(2) flow in the reactor was 0.86 g min(-1), 19.7% of this being lost. By using a predictive control algorithm the mean daily CO(2) flow was reduced to 0.74 g min(-1), with losses being reduced to 15.6%. In this case, pH tracking was not adequate, especially at the beginning and end of the daylight period, because the variation in solar irradiance was not considered. Taking solar irradiance into account resulted in better performance, with mean daily CO(2) flow reduced to 0.70 g min(-1), and carbon losses reduced to 5.5%. pH tracking was improved and valve actuation was reduced. Improvement of pH control reduced pH gradients in the culture, which increased the photosynthesis rate and biomass productivity of the system. Biomass productivity increased from 1.28 to 1.48 g L(-1) day-(1) when on-off control was replaced by model-based predictive control plus solar irradiance effect mode. Implementation of this methodology in outdoor photobioreactors can increase productivity by 15% and reduce the cost of producing biomass by >6%. Clearly, application of effective control techniques, such as model-based predictive control (MPC), must be considered when developing these processes.

Algorithms↗

PubMed2006

Closed-loop control of mean arterial blood pressure during surgery with alfentanil: clinical evaluation of a novel model-based predictive controller.

BACKGROUND: In contrast to hypnosis, there is no surrogate parameter for analgesia in anesthetized patients. Opioids are titrated to suppress blood pressure response to noxious stimulation. The authors evaluated a novel model predictive controller for closed-loop administration of alfentanil using mean arterial blood pressure and predicted plasma alfentanil concentration (Cp Alf) as input parameters. METHODS: The authors studied 13 healthy patients scheduled to undergo minor lumbar and cervical spine surgery. After induction with propofol, alfentanil, and mivacurium and tracheal intubation, isoflurane was titrated to maintain the Bispectral Index at 55 (+/- 5), and the alfentanil administration was switched from manual to closed-loop control. The controller adjusted the alfentanil infusion rate to maintain the mean arterial blood pressure near the set-point (70 mmHg) while minimizing the Cp Alf toward the set-point plasma alfentanil concentration (Cp Alfref) (100 ng/ml). RESULTS: Two patients were excluded because of loss of arterial pressure signal and protocol violation. The alfentanil infusion was closed-loop controlled for a mean (SD) of 98.9 (1.5)% of presurgery time and 95.5 (4.3)% of surgery time. The mean (SD) end-tidal isoflurane concentrations were 0.78 (0.1) and 0.86 (0.1) vol%, the Cp Alf values were 122 (35) and 181 (58) ng/ml, and the Bispectral Index values were 51 (9) and 52 (4) before surgery and during surgery, respectively. The mean (SD) absolute deviations of mean arterial blood pressure were 7.6 (2.6) and 10.0 (4.2) mmHg (P = 0.262), and the median performance error, median absolute performance error, and wobble were 4.2 (6.2) and 8.8 (9.4)% (P = 0.002), 7.9 (3.8) and 11.8 (6.3)% (P = 0.129), and 14.5 (8.4) and 5.7 (1.2)% (P = 0.002) before surgery and during surgery, respectively. A post hoc simulation showed that the Cp Alfref decreased the predicted Cp Alf compared with mean arterial blood pressure alone. CONCLUSION: The authors' controller has a similar set-point precision as previous hypnotic controllers and provides adequate alfentanil dosing during surgery. It may help to standardize opioid dosing in research and may be a further step toward a multiple input-multiple output controller.

Adult↗

PubMed2005

A simplified predictive control algorithm for disturbance rejection.

Model predictive control (MPC) offers several advantages for control of chemical processes. However, the standard MPC may do a poor job in suppressing the effects of certain disturbances. This shortcoming is mainly due to the assumption that disturbances remain constant over the prediction horizon. In this paper, a simple disturbance predictor (SDP) is developed to provide predictions of the unmodeled deterministic disturbances for a simplified MPC algorithm. The prediction is developed by curve fitting of the past information. A tuning parameter is employed to handle a variety of disturbance dynamics and a procedure is presented to find an optimum value of the tuning parameter online. A comparison is made with the commonly used disturbance prediction on three example problems. The results show that an improved regulatory performance and zero offset can be achieved under both regular and ramp output disturbances by using the proposed disturbance predictor.

Journal Article↗

PubMedDate not supplied

Control of a nonsquare drug infusion system: A simulation study.

A model predictive control strategy was developed and tested on a nonlinear canine circulatory model for the regulation of hemodynamic variables under critical care conditions. Different patient conditions such as congestive heart failure, post-operative hypertension, and sepsis shock were studied in closed-loop simulations. The model predictive controller, which uses a different linear model depending on the patient condition, allowed constraints to be explicitly enforced. The controller was initially tuned on the basis of a linear plant model, then tested on the nonlinear physiological model; the simulations demonstrated the ability to handle constraints, such as drug dosage specifications, commonly desired by critical care physicians.

Animals↗

PubMed1999

Synchronizing chaos in an experimental chaotic pendulum using methods from linear control theory.

Linear feedback control, specifically model predictive control (MPC), was used successfully to synchronize an experimental chaotic pendulum both on unstable periodic and aperiodic orbits. MPC enables tuning of the controller to give an optimal controller performance. That is, both the fluctuations around the target trajectory and the necessary control actions are minimized using a least-squares solution of the linearized problem. It is thus shown that linear control methods can be applied to experimental chaotic systems, as long as an adequate model is available that can be linearized along the desired trajectory. This model is used as an observer, i.e., it is synchronized with the experimental pendulum to estimate the state of the experimental pendulum. In contrast with other chaos control procedures like the map-based Ott, Grebogi, and York method [Phys. Rev. Lett. 64, 1196 (1990)], the continuous type feedback control proposed by Pyragas [Phys. Lett. A 170, 421 (1992)], or the feedback control method recently proposed by Brown and Rulkov [Chaos 7 (3), 395 (1997)], the procedure outlined in this paper automatically results in a choice for the feedback gains that gives optimum performance, i.e., minimum fluctuations around the desired trajectory using minimum control actions.

Journal Article↗

PubMed2007

A mathematical model for predicting controlled release of bioactive agents from composite fiber structures.

A mathematical model for predicting bioactive agent release profiles from core/shell fiber structures was developed and studied. These new composite fibers, which combine good mechanical properties with desired protein release profiles, are designed for use in tissue regeneration and other biomedical applications. These fibers are composed of an inner dense polymeric core surrounded by a porous bioresorbable shell, which encapsulates the bioactive agent molecules. The model is based on Fick's second law of diffusion, and on two major assumptions: (a) first-order degradation kinetics of the porous shell, and (b) a nonconstant diffusion coefficient for the bioactive agent, which increases with time because of degradation of the host polymer. Three factors are evaluated and included in this model: a porosity factor, a tortuosity factor, and a polymer concentration factor. Our study indicates that the model correlates well with in vitro release results, exhibiting a mean error of less than 2.2% for most studied cases. In this study, the model was used for predicting protein release profiles from fibers with shells of various initial molecular weights and for predicting the release of proteins with various molecular weights. This new model exhibits a potential for simulating fibrous systems for a wide variety of biomedical applications.

Biocompatible Materials↗

PubMed2000

Simultaneous regulation of hemodynamic and anesthetic states: a simulation study.

A model predictive control strategy to simultaneously regulate hemodynamic and anesthetic variables in critical care patients is presented. A nonlinear canine circulatory model, which has been used to study the effect of inotropic and vasoactive drugs on hemodynamic variables, has been extended to include propofol pharmacokinetics and pharmacodynamics. Propofol blood concentration is used as a measure for depth of anesthesia. The simulation model is used to design and test the control strategy. The optimization-based model predictive control strategy assures that constraints imposed on the drug infusion rates are met. The physician always remains "in the loop" and serves as the "primary controller" by making propofol blood concentration setpoint changes based on observations about anesthetic depth. Results are shown for three simulated cases: (i) congestive heart failure, (ii) postcoronary artery bypass, and (iii) acute changes in hemodynamic variables.

Anesthesia↗

PubMed1977

Implementation of a model for census prediction and control.

A model is described that predicts hospital census and computes, for each day, the number of elective admissions that will maximize the census over the short run, subject to constraints on the probability of overflow. Where a computer is available the model provides detailed predictions of census in units as small as 10 beds; used with manual computation the model allows production of tables of the recommended numbers of elective admissions to the hospital as a whole. The model has been tested in five hospitals and is part of the admissions system in two of them; implementation is described, and the results obtained are discussed.

Computers↗

PubMed1999

Nonlinear model-based predictive control of non-depolarizing muscle relaxants using neural networks.

Neuromuscular blockade can be relatively easily measured in the clinical setting. Consequently, closed-loop control can be exercised by measuring the neuromuscular activity, calculating the dose of drug necessary to achieve a predefined degree of neuromuscular blockade and finally directing an infusion pump. Recently introduced short-acting blocking agents like mivacurium provide benefits for the clinical routine due to a small onset time and half life. In order to provide a stable blockade for different groups of patients a fast and highly adaptable control unit is needed. Furthermore its development should not imply costly investigations for determining a pharmacological model. The fulfilling of these requirements yield a self-adapting model-based predictive control system. The application of artificial neural networks allows an appropriate adjustment of specific parameters without the knowledge of inner pharmacodynamic processes. In a clinical study the EMG module within a Datex AS/3 monitor was used to measure the blockade and a Grasepy 3500 infusion pump for i.v. administration of mivacurium to 35 patients (ASA I-III). The performance of the novel system (mean of the T1 error: -0.32 +/- 1.7) compares favourably with closed-loop controllers demonstrated in the past. These promising results and the easy adaption to other blocking agents encourage to apply this technology even for delivering hypnotic drugs.

Electromyography↗

PubMed2006

Predictive directional compensator for systems with input constraints.

Nonlinearity caused by actuator constraint plays a destructive role in the overall performance of a control system. A model predictive controller can handle the problem by implementing a constrained optimization algorithm. Due to the iterative nature of the solution, however, this requires high computation power. In the present work we propose a new method to approach the problem by separating the constraint handling from the predictive control job. The input constraint effects are dealt with in a newly defined component called a predictive directional compensator, which works based on the directionality and predictive concepts. Through implementation of the proposed method, the computational requirement is greatly reduced with the least degradation of the closed-loop performance. Meanwhile, a new characteristic matrix has been defined by which directionality of SISO as well as nonminimum phase systems can be determined.

Algorithms↗