Toward Dependable Embedded Model Predictive Control

Johansen, Tor Arne

doi:10.1109/jsyst.2014.2368129

Cited by 66 publications

(41 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As argued in [49], for medical embedded control, the highest priority is to maintain reliability and safe functionality (fault tolerance, disturbance rejection) while running at optimal operation levels. Different safety protocols can be implemented in conjunction with the core control strategy, as presented in Figure 2b,c.…”

Section: Control and Safety Layermentioning

confidence: 99%

“…However, issues of stability, performance degradation and increase in storage requirements may arise when switching between controllers, which is not desirable in medical embedded control. An alternative configuration based on the principle of functional redundancy has been suggested in [49,52] and is presented in Figure 2c. This architecture considers two or more control alternatives that run in parallel, and based on pre-specified criteria, the best solution is selected.…”

Section: Control and Safety Layermentioning

confidence: 99%

See 1 more Smart Citation

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

View full text Add to dashboard Cite

Significant increases in processing power, coupled with the miniaturization of processing units operating at low power levels, has motivated the embedding of modern control systems into medical devices. The design of such embedded decision-making strategies for medical applications is driven by multiple crucial factors, such as: (i) guaranteed safety in the presence of exogenous disturbances and unexpected system failures; (ii) constraints on computing resources; (iii) portability and longevity in terms of size and power consumption; and (iv) constraints on manufacturing and maintenance costs. Embedded control systems are especially compelling in the context of modern artificial pancreas systems (AP) used in glucose regulation for patients with type 1 diabetes mellitus (T1DM). Herein, a review of potential embedded control strategies that can be leveraged in a fully-automated and portable AP is presented. Amongst competing controllers, emphasis is provided on model predictive control (MPC), since it has been established as a very promising control strategy for glucose regulation using the AP. Challenges involved in the design, implementation and validation of safety-critical embedded model predictive controllers for the AP application are discussed in detail. Additionally, the computational expenditure inherent to MPC strategies is investigated, and a comparative study of runtime performances and storage requirements among modern quadratic programming solvers is reported for a desktop environment and a prototype hardware platform.

show abstract

Section: Control and Safety Layermentioning

confidence: 99%

Section: Control and Safety Layermentioning

confidence: 99%

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

View full text Add to dashboard Cite

show abstract

“…[34], [16]), computational efficiency [35], and dependability, e.g. [36]. Additionally, azimuth turning has to be implemented ad hoc, as with conventional thrust allocation.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Positioning With Model Predictive Control

Veksler¹,

Johansen

Borrelli

et al. 2016

IEEE Trans. Contr. Syst. Technol.

150

View full text Add to dashboard Cite

Marine vessels with dynamic positioning capability are typically equipped with many enough thrusters to make them overactuated, and with satellite navigation and other sensors to determine their position, heading and velocity. An automatic control system is tasked with coordinating the thrusters to move the vessel in any desired direction and to counteract the environmental forces. The design of this control system is usually separated into several levels. First, a dynamic positioning (DP) control algorithm calculates the total force and moment of force that the thruster system should produce. Then, a thrust allocation (TA) algorithm coordinates the thrusters so that the resultant force they produce matches the request from the DP control algorithm. Unless significant heuristic modifications are made, the DP control algorithm has limited information about the thruster effects such as saturations and limited rate of rotation of variable-direction thrusters, as well as systemic effects such as singular thruster configurations. The control output produced with this control architecture is therefore not always optimal, and may result in a position loss that would not have occurred with a more sophisticated control algorithm. Recent advances in computer hardware and algorithms make it possible to consider model-predictive control algorithm (MPC) that combines positioning control and thrust allocation into a single algorithm, which theoretically should yield a near-optimal controller output. The presented work explores advantages and disadvantages of using model predictive control compared to the traditional algorithms.

show abstract

“…Several activation frequencies (or interval between optimization cycles) for the DRTO routine were evaluated, and an artificial disturbance in the form of a gaussian noise was introduced in the feed rate substrate concentration, as presented by several authors, such as Ochoa et al Johansen, Tenny et al [29,51,52], with a deviation of 10% from the nominal value. It is important to emphasize that a productivity based objective function is appropriately for the control or optimization of a processes in which there is disturbance occurrence, instead of a defined operation point, as it can induce an unsatisfactory performance [29].…”

Section: Drto Of the Fermentative Processmentioning

confidence: 99%

Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques

Freitas

Olivo²,

Andrade³

2017

Energies

View full text Add to dashboard Cite

Due to growing worldwide energy demand, the search for diversification of the energy matrix stands out as an important research topic. Bioethanol represents a notable alternative of renewable and environmental-friendly energy sources extracted from biomass, the bioenergy. Thus, the assurance of optimal growth conditions in the fermenter through operational variables manipulation is cardinal for the maximization of the ethanol production process yield. The current work focuses in the determination of optimal control scheme for the fermenter feed rate and batch end-time, evaluating different parametrization profiles, and comparing evolutionary computation techniques, the genetic algorithm (GA) and differential evolution (DE), using a dynamic real-time optimization (DRTO) approach for the in silico ethanol production optimization. The DRTO was able to optimize the reactor feed rate considering disturbances in the process input. Open-loop tests results obtained for the algorithms were superior to several works presented in the literature. The results indicate that the interaction between the intervals of DRTO cycles and parametrization profile is more significant for the GA, both in terms of ethanol productivity and batch time. In general lines, the present work presents a methodology for control and optimization studies applicable to other bioenergy generation systems.

show abstract

Toward Dependable Embedded Model Predictive Control

Cited by 66 publications

References 53 publications

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Dynamic Positioning With Model Predictive Control

Optimization of Bioethanol In Silico Production Process in a Fed-Batch Bioreactor Using Non-Linear Model Predictive Control and Evolutionary Computation Techniques

Contact Info

Product

Resources

About