Model predictive control using Dynamic Integrated System Optimisation and Parameter Estimation (DISOPE)

Minimally invasive surgery (MIS) during cardiovascular interventions reduces trauma and enables the treatment of high-risk patients who were initially denied surgery. However, restricted access, reduced visibility and control of the instrument at the treatment locations limits the performance and capabilities of such interventions during MIS. Therefore, the demand for technology such as steerable sheaths or catheters that assist the clinician during the procedure is increasing. In this study, we present and evaluate a robotically actuated delivery sheath (RADS) capable of autonomously and accurately compensating for beating heart motions by using a model-predictive control (MPC) strategy. We develop kinematic models and present online ultrasound segmentation of the RADS that are integrated with the MPC strategy. As a case study, we use pre-operative ultrasound images from a patient to extract motion profiles of the aortic heart valve (AHV). This allows the MPC strategy to anticipate for AHV motions. Further, mechanical hysteresis in the steering mechanism is compensated for in order to improve tip positioning accuracy. The novel integrated system is capable of controlling the articulating tip of the RADS to assist the clinician during cardiovascular surgery. Experiments demonstrate that the RADS follows the AHV motion with a mean positioning error of 1.68 mm. The presented modelling, imaging and control framework could be adapted and applied to a range of continuum-style robots and catheters for various cardiovascular interventions.

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Biogas production management systems with model predictive control of anaerobic digestion processes

Bioprocess and Biosystems Engineering ◽

10.1007/s00449-020-02404-7 ◽

2020 ◽

Vol 43 (12) ◽

pp. 2189-2200

Author(s):

Kazuto Yoshida ◽

Naoto Shimizu

Keyword(s):

Parameter Estimation ◽

Anaerobic Digestion ◽

Model Predictive Control ◽

Prediction Model ◽

Predictive Control ◽

Management System ◽

Production Management ◽

Biogas Production ◽

Production Management System ◽

Estimation System

Abstract We developed a biogas production management system to control biogas production by determining the feedstock inputs to the anaerobic digestion process according to fluctuations of the renewable energy supply. The developed system consists of three functions: a prediction model for the anaerobic digestion processes, a parameter-estimation system, and a feedstock-determination controller. A prediction model for the anaerobic digestion processes in a state-space representation was constructed for the input–output relationship of biogas generation from organic compounds and the state of methane fermentation. A parameter-estimation system that estimated the parameters included in the prediction model from actual operating process data was built based on adaptive identification theory. The feedstock-determination controller was established based on model predictive control as a method to control biogas production. From the results of the identification experiment, the least square estimator of the parameters converged as the training data increased, and a reliable parameter was given in 1 week. From the results of the numerical simulation and the control experiment, it was confirmed that the biogas production management system developed in this study had a high prediction accuracy and control performance.

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