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At least 19 recordsLinked to original sources
PubMed2004

Well-conditioned model predictive control.

Model-based predictive control is an advanced control strategy that uses a move suppression factor or constrained optimization methods for achieving satisfactory closed-loop dynamic responses of complex systems. While these approaches are suitable for many processes, they are formulated on the selection of certain parameters that are ambiguous and also computationally demanding which makes them less suited for tight control of fast processes. In this paper, a new dynamic matrix control (DMC) algorithm is proposed that reduces inherent ill-conditioning by allowing the process prediction time step to exceed the control time step. The main feature, that stands in contrast with current DMC approaches, is that the original open-loop data are used to evaluate a "shifting factor" m in the controller matrix where m replaces the move suppression coefficient. The new control algorithm is practically demonstrated on a fast reacting process with better control being realized in comparison with DMC using move suppression. The algorithm also gives improved closed-loop responses for control simulations on a multivariable nonlinear process having variable dead-time, and on other models found in the literature. The shifting factor m is generic and can be effectively applied for any control horizon.

Journal Article↗
PubMed2006

Multicentric, randomized, controlled trial to evaluate blood glucose control by the model predictive control algorithm versus routine glucose management protocols in intensive care unit patients.

OBJECTIVE: To evaluate a fully automated algorithm for the establishment of tight glycemic control in critically ill patients and to compare the results with different routine glucose management protocols of three intensive care units (ICUs) across Europe (Graz, Prague, and London). RESEARCH DESIGN AND METHODS: Sixty patients undergoing cardiac surgery (age 67 +/- 9 years, BMI 27.7 +/- 4.9 kg/m2, 17 women) with postsurgery blood glucose levels >120 mg/dl (6.7 mmol/l) were investigated in three different ICUs (20 per center). Patients were randomized to either blood glucose management (target range 80-110 mg/dl [4.4-6.1 mmol/l]) by the fully automated model predictive control (MPC) algorithm (n = 30, 10 per center) or implemented routine glucose management protocols (n = 30, 10 per center). In all patients, arterial glucose was measured hourly to describe the glucose profile until the end of the ICU stay but for a maximum period of 48 h. RESULTS: Compared with routine protocols, MPC treatment resulted in a significantly higher percentage of time within the target glycemic range (% median [min-max]: 52 [17-92] vs. 19 [0-71]) over 0-24 h (P < 0.01). Improved glycemic control with MPC treatment was confirmed in patients remaining in the ICU for 48 h (0-24 h: 50 [17-71] vs. 21 [4-67], P < 0.05, and 24-48 h: 65 [38-96] vs. 25 [8-79], P < 0.05, for MPC [n = 16] vs. routine protocol [n = 13], respectively). Two hypoglycemic events (<54 mg/dl [3.0 mmol/l]) were observed with routine protocol treatment. No hypoglycemic event occurred with MPC. CONCLUSIONS: The data suggest that the MPC algorithm is safe and effective in controlling glycemia in critically ill postsurgery patients.

Aged↗
PubMed2004

Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes.

A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.

Blood Glucose↗
PubMed2002

Model-predictive control of hyperthermia treatments.

A model-predictive controller (MPC) of the thermal dose in hyperthermia cancer treatments has been developed and evaluated using simulations with one-point and one-dimensional models of a tumor. The developed controller is the first effort in: 1) the application of feedback control to pulsed, high-temperature hyperthermia treatments; 2) the direct control of the treatment thermal dose rather than the treatment temperatures; and 3) the application of MPC to hyperthermia treatments. Simulations were performed with different blood flow rates in the tumor and constraints on temperatures in normal tissues. The results demonstrate that 1) thermal dose can be controlled in the presence of plant-model mismatch and 2) constraints on the maximum allowable temperatures in normal tissue and/or the pulsed power magnitude can be directly incorporated into MPC and met while delivering the desired thermal dose to the tumor. For relatively high blood flow rates and low transducer surface intensities--factors that limit the range of temperature variations in the tumor, the linear MPC, obtained by piece-wise linearization of the dose-temperature relationship, provides an adequate performance. For large temperature variations, the development of nonlinear MPC is necessary.

Computer Simulation↗
PubMed2005

Modeling a multivariable reactor and on-line model predictive control.

A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown.

Algorithms↗
PubMed2005

Model predictive control as a tool for improving the process operation of MSW combustion plants.

In this paper a feasibility study is presented on the application of the advanced control strategy called model predictive control (MPC) as a tool for obtaining improved process operation performance for municipal solid waste (MSW) combustion plants. The paper starts with a discussion of the operational objectives and control of such plants, from which a motivation follows for applying MPC to them. This is followed by a discussion on the basic idea behind this advanced control strategy. After that, an MPC-based combustion control system is proposed aimed at tackling a typical MSW combustion control problem and, using this proposed control system, an assessment is made of the improvement in performance that an MPC-based MSW combustion control system can provide in comparison to conventional MSW combustion control systems. This assessment is based on simulations using an experimentally obtained process and disturbance model of a real-life large-scale MSW combustion plant.

Computer Simulation↗
PubMed2005

A model predictive control based scheduling method for HIV therapy.

Recently developed models of the interaction of the human immune system and the human immunodeficiency virus (HIV) suggest the possibility of using interruptions of highly active anti-retroviral therapy (HAART) to simulate a therapeutic vaccine and induce cytotoxic lymphocyte (CTL) mediated control of HIV infection. We have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules for this purpose. This method provides a clinically implementable framework for calculating interruption schedules that are robust to errors due to measurement and patient variations. In this paper, we discuss the medical motivation for this work, introduce the MPC-based method, show simulation results, and discuss future work necessary to implement the method.

AIDS Vaccines↗
PubMed2003

Experimental studies on multiple-model predictive control for automated regulation of hemodynamic variables.

A model-based control methodology was developed for automated regulation of mean arterial pressure and cardiac output in critical care subjects using inotropic and vasoactive drugs. The control algorithm used a multiple-model adaptive approach in a model predictive control framework to account for variability and explicitly handle drug rate constraints. The controller was experimentally evaluated on canines that were pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output. The controller performed better as compared to experiments on manual regulation of the hemodynamic variables. After the model bank was determined, mean arterial pressure was held within +/- 5 mm Hg 88.9% of the time with a standard deviation of 3.9 mm Hg. The cardiac output was held within +/- 1 l/min 96.1% of the time with a standard deviation of 0.5 l/min. The manual runs maintain mean arterial pressure only 82.3% of the time with a standard deviation of 5 mm Hg, and cardiac output 92.2% of the time with a standard deviation of 0.6 l/min.

Algorithms↗
PubMed2002

Robustness of MetaNet graph models: predicting control of urea production in humans.

Urea production in human liver was described by a MetaNet graph, a flowchart-like representation of metabolic pathways that includes parameters for the kinetic constants of the constituent enzymes. Formal operations on the graph facilitate the identification of ligand-binding equilibria that participate in feedback regulation in the network of biochemical reactions. The state of the biochemical network is specified by the concentrations of the intermediates. At any particular time, the influence of an identified locus of regulation is proportional to the respective fractional saturation of the corresponding binding site. Enzymes that make or consume the feedback chemicals share in the control of the strength of the feedback signal in proportion to their fractional saturation. This model predicts control of urea production by the processes that deliver amino groups to the urea cycle enzymes more than by the cycle enzymes themselves. Mitochondrial membrane transport processes are important for transmission of information through the network, but irreversible enzymes and processes far from equilibrium control the strength of the feedback signal. Systematic variation of the parameter values by amounts comparable to the expected variability of their measured values indicated a high probability of invariance in the identities of the predicted control points. The properties of the model are consistent with those of error-tolerant scale-free networks. These results demonstrate the robustness of a MetaNet model's predictions with respect to uncertainties in the values of its parameters.

Computer Simulation↗
PubMed1997

Model Predictive Impedance Control: A Model for Joint Movement.

Impedance control has been suggested as the strategy employed by the central nervous system to control human postures and movements. A realization of this strategy is presented that uses a model predictive control algorithm as a higher motor controller. External disturbances are explicitly included in the model. The combination of 3 key factors-joint impedance control, model predictive controller, and external disturbance input-forms the basis for the generality of this model. The model was applied to 3 different types of joint movements: a tracking movement with an unpredicted disturbance, a rhythmic movement, and an unstable biped model of human walking. Computer simulation results showed excellent performance of the model in all 3 cases for optimal values of active joint impedances and an exact match between the musculoskeletal system and the model internal to the model predictive controller. The controller was also able to maintain acceptable performance in the presence of a 25% mismatch between the musculoskeletal system and its internal model.

equilibrium-point hypothesis↗
PubMed2006

Robust and nominal stability conditions for a simplified model predictive controller.

A new condition is derived that guarantees robust stability for a set of stable, linear time-invariant plants controlled by using a simplified model predictive control algorithm (SMPC). Discrete single-input-single-output control systems are considered in this paper. Uncertainty is treated in the time domain by considering the stabilization of a set of pulse response functions. The method presented is suitable for stabilizing a set of plants that are not necessarily related. Central to this method is a bounding function, which is a function of the model and controller parameters. The bounding function is designed to have a larger magnitude than all of the pulse response functions in the set of plants to be stabilized. Using this method, it was found that the bounding function is monotonically decreasing when a first-order plus dead-time model is used to design the controller. This allows the coincidence point used in SMPC to be employed directly as a tuning "knob" for robustness, and also simplifies the analysis for dead-time uncertainty. In addition, a comparison of two nominal stability conditions is provided.

Journal Article↗
PubMed2005

Modeling, identification and nonlinear model predictive control of type I diabetic patient.

Patients with type I diabetes nearly always need therapy with insulin. The most desirable treatment would be to mimic the operation of a normal pancreas. In this work a patient affected with this pathology is modeled and identified with a neural network, and a control strategy known as Nonlinear Model Predictive Control is evaluated as an approach to command an insulin pump using the subcutaneous route. A method for dealing with the problems related with the multiple insulin injections simulation and a multilayer neural network identification of the patient model is presented. The controller performance of the proposed strategy is tested under charge and reference disturbances (setpoint). Simulating an initial blood glucose concentration of 250 mg/dl a stable value of 97.0 mg/dl was reached, with a minimum level of 76.1 mg/dl. The results of a simulated 50 g oral glucose tolerance test show a maximum glucose concentration of 142.6 mg/dl with an undershoot of 76.0 mg/dl. According to the simulation results, stable close-loop control is achieved and physiological levels are reached with reasonable delays, avoiding the undesirable low glucose levels. Further studies are needed in order to deal with noise and robustness aspects, issues which are out of the scope of this work.

Blood Glucose↗
PubMed2003

Model predictive control with amplitude and rate actuator saturation.

In this work we show that the anti-wind-up-bumpless-transfer controller emerges from the structure of model predictive control (MPC) with quadratic objective and input constrains. The key to establish that relationship is the application of optimality conditions to the equivalent optimal control problem. The proposed framework employs a model of physical constraints as part of the controller architecture to ensure that the commands sent to the actuator do not exceed their specific limits and the internal states of the controller are well updated. Numerical examples are presented for illustrating the proposed control design methodology.

Journal Article↗
PubMed2005

Model predictive control of discrete-time hybrid systems with discrete inputs.

This paper proposes and discusses a model predictive control approach to hybrid systems with discrete inputs only. The algorithm, which takes into account a model of a hybrid system, described as a mixed logical dynamical system, is based on a performance-driven reachability analysis. The algorithm abstracts the behavior of the hybrid system by building a "tree of evolution." The nodes of the tree represent the reachable states of a process, and the branches connect two nodes if a transition exists between the corresponding states. A cost-function value is associated with each node, and based on this value the exploration of the tree is driven. As soon as the exploration of the tree is finished, the corresponding input is applied to the system and the procedure is repeated.

Journal Article↗
PubMed2005

Real time optimal guidance of low-thrust spacecraft: an application of nonlinear model predictive control.

Real time optimal guidance is considered for a class of low thrust spacecraft. In particular, nonlinear model predictive control (NMPC) is utilized for computing the optimal control actions required to transfer a spacecraft from a low Earth orbit to a mission orbit. The NMPC methodology presented is able to cope with unmodeled disturbances. The dynamics of the transfer are modeled using a set of modified equinoctial elements because they do not exhibit singularities for zero inclination and zero eccentricity. The idea behind NMPC is the repeated solution of optimal control problems; at each time step, a new control action is computed. The optimal control problem is solved using a direct method-fully discretizing the equations of motion. The large scale nonlinear program resulting from the discretization procedure is solved using IPOPT--a primal-dual interior point algorithm. Stability and robustness characteristics of the NMPC algorithm are reviewed. A numerical example is presented that encourages further development of the proposed methodology: the transfer from low-Earth orbit to a molniya orbit.

Journal Article↗
PubMed2002

Model predictive controller for biodegradable polyhydroxyalkanoate production in fed-batch culture.

The aim of this study is to develop a model predictive controller (MPC) accompanied with a metabolic reaction model controller for controlling ethanol and n-pentanol concentrations and the mole fraction of monomer units in the production of poly(beta-hydroxybutyrate-co-beta-hydroxyvalerate), P(HB-co-HV), a biodegradable copolyester. The controller consists of two parts: one is for alcohol concentration control and the other one is for mole fraction control, and is based on the concept of metabolic flux distribution control. For control of alcohol concentration, conventional proportional and integral (PI) controller and feedforward/feedback controller did not function sufficiently because the large sampling interval of the biosensor led to a severe overshoot of concentration. A single-input and single-output (SISO) MPC is constructed for control of ethanol concentration in the growth phase, whereas a multi-input and multi-output (MIMO) MPC is constructed for control of both alcohol concentrations in the production phase. Specific ethanol consumption rate was estimated by the MPC using the past time series data of ethanol concentration. By means of simulations and experiments, the weighting parameters of the noise filters in the MPC were well adjusted. Ethanol and n-pentanol concentrations were well controlled by the MPC, compared with PI controller and feedforward/feedback controller. As a result, P(HB-co-HV) production was maximized with a given value of mole fraction of 3HV units at the end of cultivation.

Biodegradation, Environmental↗