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Results for “model predictive control”

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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↗

PubMed2004

Adaptive predictive control of arterial blood pressure based on a neural network during acute hypotension.

In acute hypotension, an automated drug infusion system to control mean arterial blood pressure (MAP) has not been previously studied, though many investigations have examined the use of vasodilating drugs to control MAP in postoperative hypertension. Therefore, we examined an automated control of MAP during acute hypotension using a neural network (NN) approach. A proportional-integral-derivative (PID) control, an adaptive predictive control using a NN (APC(NN)), a combined control of APC(NN) and PID (APC(NN-PID)), a fuzzy control, and a model predictive control were tested in computer simulation based on the MAP response to norepinephrine (NE) of 25 microg ml(-1). In six anesthetized rabbits, using the NE of 25 microg ml(-1), the PID control, APC(NN), and APC(NN-PID) prevented severe hypotension compared to an uncontrolled condition. Under PID control, four of the six animals showed MAP oscillation. Using NE of 50 microg ml(-1), the rabbits recovered from acute hypotension for all systems tested but showed sustained MAP oscillation during PID control. In conclusion, utilization of a NN for adaptive predictive control systems could facilitate the development of an automated drug infusion apparatus because it provides robust control even when acute or large perturbations and inter-individual differences in the sensitivity to therapeutic agents occur.

Acute Disease↗

PubMed1998

Neural predictive controller for insulin delivery using the subcutaneous route.

A neural predictive controller for closed-loop control of glucose using subcutaneous (s.c.) tissue glucose measurement and s.c. infusion of monomeric insulin analogs was developed and evaluated in a simulation study. The proposed control strategy is based on off-line system identification using neural networks (NN's) and nonlinear model predictive controller design. The system identification framework combines the concept of nonlinear autoregressive model with exogenous inputs (NARX) system representation, regularization approach for constructing radial basis function NN's, and validation methods for nonlinear systems. Numerical studies on system identification and closed-loop control of glucose were carried out using a comprehensive model of glucose regulation and a pharmacokinetic model for the absorption of monomeric insulin analogs from the s.c. depot. The system identification procedure enabled construction of a parsimonious network from the simulated data, and consequently, design of a controller using multiple-step-ahead predictions of the previously identified model. According to the simulation results, stable control is achievable in the presence of large noise levels, for unknown or variable time delays as well as for slow time variations of the controlled process. However, the control limitations due to the s.c. insulin administration makes additional action from the patient at meal time necessary.

Algorithms↗

PubMed2006

Modeling, identification, and validation of models for predictive ammonia control in a wastewater treatment plant--a case study.

The aim of this work is to develop the ammonia models that could be used for model predictive control (MPC) of nitrification process in a wastewater treatment plant. First, a reduced nonlinear model is presented, which is based on expression for nitrification reaction rate in activated sludge model No. 1 and modified for attached biomass processes, while second, a linear black-box model is shown. The data used for model identification were collected during several weeks of experiments on a real plant so that good identification data were obtained. The designed models were validated based on open loop simulations and predictions. Validation results show that the reduced nonlinear model performs better compared to the linear model, however, both models show relatively large errors compared to the real plant data. Hence, a closed loop simulation study was performed to see the differences between the performance of model predictive controller using previously estimated linear and nonlinear models and a standard proportional integral (PI) controller. From the simulation study results it was seen that in spite of relatively large model errors the MPC algorithms give better results in terms of ammonia removal compared to the PI controller, while MPC with the nonlinear model shows additional improvements over the MPC with the linear model.

Ammonia↗

PubMed2003

An alternative approach to synthesizing bipedal walking.

Based on mechanical analysis, three gait descriptors are found which should be controlled to generate cyclic gait of a seven-link humanoid biped in the sagittal plane: (i) step length, (ii) step time, and (iii) the velocity of the center of mass (CoM) at push off. Two of these three gait descriptors can be chosen independently, since the CoM moves almost ballistically during the swing phase. These gait descriptors are formulated as end-point conditions and are regulated by a model predictive controller. In addition, continuous controls at the trunk and knees are implemented to maintain the trunk upright and to ensure weight bearing. The model predictive controller is realized by quadratic dynamic matrix control, which offers the possibility of including constraints that are exposed by the environment and the biped itself. Specifying step length and CoM velocity at push off, the controller generates a symmetric and stable gait. The proposed control scheme serves as a general-purpose solution for the generation of a bipedal gait. The proposed model contains fewer parameters than other models, and they are all directly related to determinants of bipedal gait: step length, trunk orientation, step time, walking velocity, and weight bearing. The proposed control objectives and the model of humanoid bipedal walking have potential applications in robotics and rehabilitation engineering.

Gait↗

PubMed2006

Continuous glucose monitoring and closed-loop systems.

BACKGROUND: The last two decades have witnessed unprecedented technological progress in the development of continuous glucose sensors, resulting in the first generation of commercial glucose monitors. This has fuelled the development of prototypes of a closed-loop system based on the combination of a continuous monitor, a control algorithm, and an insulin pump. METHOD: A review of electromechanical closed-loop approaches is presented. This is followed by a review of existing prototypes and associated glucose sensors. A literature review was undertaken from 1960 to 2004. RESULTS: Two main approaches exist. The extracorporeal s.c.-s.c. approach employs subcutaneous glucose monitoring and subcutaneous insulin delivery. The implantable i.v.-i.p. approach adopts intravenous sampling and intraperitoneal insulin delivery. Feasibility of both solutions has been demonstrated in small-scale laboratory studies using either the classical proportional-integral-derivative controller or a model predictive controller. Performance in the home setting has yet to be demonstrated. CONCLUSIONS: The glucose monitor remains the main limiting factor in the development of a commercially viable closed-loop system, as presently available monitors fail to demonstrate satisfactory characteristics in terms of reliability and/or accuracy. Regulatory issues are the second limiting factor. Closed-loop systems are likely to be used first by health-care professionals in controlled environments such as intensive care units.

Administration, Cutaneous↗

PubMed2005

Cascade generalized predictive control strategy for boiler drum level.

This paper proposes a cascade model predictive control scheme for boiler drum level control. By employing generalized predictive control structures for both inner and outer loops, measured and unmeasured disturbances can be effectively rejected, and drum level at constant load is maintained. In addition, nonminimum phase characteristic and system constraints in both loops can be handled effectively by generalized predictive control algorithms. Simulation results are provided to show that cascade generalized predictive control results in better performance than that of well tuned cascade proportional integral differential controllers. The algorithm has also been implemented to control a 75-MW boiler plant, and the results show an improvement over conventional control schemes.

Journal Article↗

PubMed1983

A predictive value model for quality control: effects of the prevalence of errors on the performance of control procedures.

A predictive value model has been developed to describe the usefulness of results from quality control tests or procedures. The model shows that the critical parameters are the probability for false rejection, probability for error detection, and prevalence or frequency of occurrence of analytical errors. When prevalence is low, control procedures should have a low probability for false rejection. When prevalence is high, control procedures should have a high probability for error detection. The predictive value model for a quality control (QC) test is analogous to the predictive value model for a diagnostic test, thus suggesting new strategies for optimizing the performance of QC tests.

Diagnostic Errors↗

PubMed2005

Online optimization of fuzzy-PID control of a thermal process.

A constrained optimization of a simple fuzzy-PID (PID-proportional integral derivative) system is designed for the online improvement of PID control performance during productive control runs. The cost function design yields a desirable balance between rise time, setpoint overshoot, and settling time to the setpoint. The constraints determined by simulation yield control performance no worse than the existing control performance during online optimization. The optimized fuzzy-PID system is compared to a similarly optimized PID controller and a benchmark model predictive controller.

Journal Article↗

PubMed2006

Predictive models of control strategies involved in containing indoor airborne infections.

Recently developed control measure modeling approaches for containing airborne infections, including engineering controls with respiratory protection and public health interventions, are readily amenable to an integrated-scale analysis. Here we show that such models can be derived from an integrated-scale analysis generated from three different types of functional relationship: Wells-Riley mathematical model, competing-risks model, and Von Foerster equation, both of the key epidemiological determinants involved and of the functional connections between them. We examine mathematically the impact of engineering control measures such as enhanced air exchange and air filtration rates with personal masking combined with public health interventions such as vaccination, isolation, and contact tracing in containing the spread of indoor airborne infections including influenza, chickenpox, measles, and severe acute respiratory syndrome (SARS). If enhanced engineering controls could reduce the basic reproductive number (R0) below 1.60 for chickenpox and 3 for measles, our simulations show that in such a prepared response with public health interventions would have a high probability of containing the indoor airborne infections. Combinations of engineering control measures and public health interventions could moderately contain influenza strains with an R0 as high as 4. Our analysis indicates that effective isolation of symptomatic patients with low-efficacy contact tracing is sufficient to control a SARS outbreak. We suggest that a valuable added dimension to public health inventions could be provided by systematically quantifying transmissibility and proportion of asymptomatic infection of indoor airborne infection. Practical Implications We have developed a flexible mathematical model that can help determine the best intervention strategies for containing indoor airborne infections. The approach presented here is scalable and can be extended to include additional control efficacies. If a newly emergent airborne infection should appear, the model could be quickly calibrated to data and intervention options at the early stage of the outbreak. Data could be provided from the field to estimate value of R0, the serial interval between cases, the distributions of the latent, incubation, and infectious periods, case fatality rates, and secondary spread within important mixing groups. The combination of enhanced engineering control measures and assigned effective public health interventions would have a high probability for containing airborne infection.

Air Pollution, Indoor↗

PubMed2001

Nonlinear mill control.

A mill is a mechanical device that grinds mined or processed material into small particles. The process is known to display significant deadtime, and, more notably, severe nonlinear behavior. Over the past 25 years attempts at continuous mill control have met varying degrees of failure, mainly due to model mismatch caused by changes in the mill process gains. This paper describes an on-line control application on a closed-circuit cement mill that uses nonlinear model predictive control technology. The nonlinear gains for the control model are calculated on-line from a neural network model of the process.

Algorithms↗

PubMed2006

SISO extended predictive control-formulation and the basic algorithm.

A new predictive controller is developed that represents a significant change from conventional model predictive control. The method termed extended predictive control (EPC) uses one tuning parameter, the condition number of the system matrix to provide an easy-to-follow tuning procedure. EPC drastically improves the system matrix conditionality resulting in faster closed-loop response without oscillatory transients. The control performance of EPC is compared with the original move suppressed and recently derived shifted predictive controllers, with improved results.

Algorithms↗

PubMedDate not supplied

Identification and control of dissolved oxygen in hybridoma cell culture in a shear sensitive environment.

The productivity of mammalian cells can be enhanced by facilitating adequate oxygen transfer into the cultivation medium. However, current methods of controlling dissolved oxygen (DO) fail to account for alterations in medium composition during the course of the fermentation. These changes, which directly affect gas solubility and overall mass transfer coefficient, may be significant and deteriorate controller's performance in the long run. In this paper, the applications of Generalized Predictive Controllers (GPC) to DO control were investigated in a shear sensitive environment and compared to PID and Model Predictive Controllers (MPC). Input and output data for system identification were initially generated by varying the composition of oxygen fed into the bioreactor from 0 to 0.21 mol % while keeping the total inlet gas flow rate at 8.75 vvm. The process was identified using an AutoRegressive model with eXogeneous inputs (ARX) model and tested on different data sets. The model parameters were then correlated with the overall mass transfer coefficients. In simulation tests, the output of the PID controller switched from minimum to maximum values while more continuous control signals were obtained with the MPC and GPC controllers. When tested in a cell-free medium, all three controllers were able to track setpoint changes with some chattering observed in the control signals. The GPC outperformed the MPC and PID controllers when applied to the cultivation of hybridoma cells.

Animals↗

PubMed2005

A multi-group model of Schistosoma japonicum transmission dynamics and control: model calibration and control prediction.

Previously we formulated a quantitative model to characterize site-specific schistosomiasis transmission. In this paper, we present a procedure to calibrate the model to data collected in endemic villages of south-western Sichuan, China, with the objective of reducing parametric uncertainty to allow the model to describe local transmission with relative confidence. A Bayesian approach using local epidemiological data and expert opinion is employed to calibrate the model. Results indicate that, after calibration, the output uncertainty is reduced substantially. The calibrated model is then used for prediction of the effects of different intervention options. Simulations reflect a bimodal transmission in both human (early summer and early fall) and snail (late summer and late fall) infections in this area, for which there is some field evidence. Also shown in the simulations are relatively high reinfection rates following chemotherapy in these endemic villages. These results suggest that a sustainable control strategy is essential in reducing transmission, and that transmission can be reduced by chemotherapy, focal snail (e.g. snail clusters) control, and egg control. Our work demonstrates the feasibility of characterizing site-specific schistosomiasis transmission using a mathematical model and a calibration approach that integrates diverse field data, and the use of the calibrated model to design control strategies.

Animals↗

PubMed1999

A model-based algorithm for blood glucose control in type I diabetic patients.

A model-based predictive control algorithm is developed to maintain normoglycemia in the Type I diabetic patient using a closed-loop insulin infusion pump. Utilizing compartmental modeling techniques, a fundamental model of the diabetic patient is constructed. The resulting nineteenth-order nonlinear pharmacokinetic-pharmacodynamic representation is used in controller synthesis. Linear identification of an input-output model from noisy patient data is performed by filtering the impulse-response coefficients via projection onto the Laguerre basis. A linear model predictive controller is developed using the identified step response model. Controller performance for unmeasured disturbance rejection (50 g oral glucose tolerance test) is examined. Glucose setpoint tracking performance is improved by designing a second controller which substitutes a more detailed internal model including state-estimation and a Kalman filter for the input-output representation. The state-estimating controller maintains glucose within 15 mg/dl of the setpoint in the presence of measurement noise. Under noise-free conditions, the model-based predictive controller using state estimation outperforms an internal model controller from literature (49.4% reduction in undershoot and 45.7% reduction in settling time). These results demonstrate the potential use of predictive algorithms for blood glucose control in an insulin infusion pump.

Algorithms↗

PubMed2000

PID gain scheduling using fuzzy logic.

A simple, yet robust and stable alternative to proportional, integral, derivative (PID) gain scheduling is developed using fuzzy logic. This fuzzy gain scheduling allows simple online duplication of PID control and the online improvement of PID control performance. The method is demonstrated with a physical model where PID control performance is improved to levels comparable to model predictive control. The fuzzy formulation is uniquely characterized by; (i) one fuzzy input variable involving the PID manipulated variable, (ii) two parameters to be tuned, while previously tuned PID parameters are retained, and (iii) a gain scheduling differential equation which relates the fuzzy and conventional PID manipulated variables and enables fuzzy gain scheduling.

Journal Article↗

PubMed2005

Real-time nonlinear feedback control of pattern formation in (bio)chemical reaction-diffusion processes: a model study.

Theoretical and experimental studies related to manipulation of pattern formation in self-organizing reaction-diffusion processes by appropriate control stimuli become increasingly important both in chemical engineering and cellular biochemistry. In a model study, we demonstrate here exemplarily the application of an efficient nonlinear model predictive control (NMPC) algorithm to real-time optimal feedback control of pattern formation in a bacterial chemotaxis system modeled by nonlinear partial differential equations. The corresponding drift-diffusion model type is representative for many (bio)chemical systems involving nonlinear reaction dynamics and nonlinear diffusion. We show how the computed optimal feedback control strategy exploits the system inherent physical property of wave propagation to achieve desired control aims. We discuss various applications of our approach to optimal control of spatiotemporal dynamics.

Bacteria↗

PubMed2006

Predictive controller evaluation including non-stationary high frequency noise and outliers for batch solid substrate fermentation bioreactors.

Optimum operation and automatic control of large-scale solid substrate fermentation (SSF) bioreactors is difficult. Though advanced control algorithms can handle most challenges encountered properly, for real-time SSF processes such controllers are expensive and time consuming to design and tune. With these considerations, advanced control algorithm tests using realistic simulations appear more appropriate. We used a phenomenological process model of an SSF pilot bioreactor, coupled with a realistic noise model, to test linear model predictive controllers. We focused on the effect noise has on the performance of the control algorithms, and how to enhance performance using a combination of low-pass (Butterworth) and outlier shaving (Hampel) filters. In simulations undertaken directly with the phenomenological model it was relatively straightforward to achieve good control performance. Nevertheless, control degraded sharply when the output of the phenomenological model was contaminated with noise using our realistic noise model, even with proper signal filtering.

Artifacts↗