Model Predictive Control: Advances in Sensor Technologies and Applications
A special issue of Sensors (ISSN 1424-8220).
Deadline for manuscript submissions: 30 November 2024 | Viewed by 2335
Special Issue Editor
Interests: soft sensors; Raman spectroscopy; fuzzy model identification; machine learning with big data; predictive control of dynamic systems; sensor fusion; data mining; indoor positioning; autonomous mobile systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The synergy between model predictive control (MPC) and evolving sensor technologies represents a new era of intelligent control. This Special Issue, “Model Predictive Control: Advances in Sensor Technologies and Applications”, explores the multi-faceted relationship between these two fields.
The depth of sensor feedback loops, revealing the crucial role of sensors in MPC, highlights their integral function in feedback control. The fusion of different sensor data provides a broader perspective on MPC and enriches decision-making processes. In the era of data overload, techniques to control inconsistent or unreliable sensor data are becoming increasingly important in MPC. Moreover, the real-time applicability of MPC, when tested via the integration of wireless sensor networks, is both a challenge and a breakthrough. The introduction of soft sensors that can either complement or potentially replace traditional hardware is exciting. Finally, the transformative impact of self-calibrating sensors that redefine the adaptability of MPC is being explored.
This Special Issue aims to shed light on these intersections and foster a deeper understanding of this transformative technology. The authors' insights, research and innovations are invaluable to this discourse.
You may choose our Joint Special Issue in Automation.
Yours sincerely,
Prof. Dr. Simon Tomažič
Guest Editor
Manuscript Submission Information
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Keywords
- model predictive control (MPC)
- neural network control system
- evolving control
- nonlinear control
- advanced process control
- adaptive control
- dynamic matrix control
- intelligent soft sensor
- fuzzy logic control
- self-calibrating sensor
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Model Predictive Control (MPC) of a counter-flow plate heat exchanger in a virtual environment
Authors: Jairo Siza, Jacqueline Llanos, Paola Velazco, Paul Moya, Henry Sumba
Affiliation: Department of Electrical, Electronics and Telecommunications, University of the Armed Forces ESPE, Av. Gral. Rumiñahui s/n, Sangolquí 171103, Ecuador
Abstract: This research proposes advanced model-based control strategies for a Countercurrent Flow Plate Heat Exchanger in a Virtual Environment. First, an immersive 3D virtual environment is designed with visual and auditory effects, where a mathematical model is required that describes the real dynamics of the process, allowing the movement of the fluid in parallel in different directions with hot and cold temperature at the out. Which changes dynamically with the variation of the temperature of the hot and cold input fluids, in addition, it includes interfaces for monitoring the control variables, links that allow communication between the virtual heat exchanger and the control applications. The Plate and Counter-Current Flow heat exchanger, being a multivariable and non-linear process, requires analysis in the design of the controller. In this context, this work proposes and compares two control strategies with the objective of identifying the best performance. The first controller is based on the inverse model of the plant in discrete time, with linear algebra techniques and numerical methods, the second controller applied is an MPC predictive control model, which presents optimal control actions, which minimizes state errors. stationary and aggressive variations of the actuators, respecting temperature restrictions and operating limits of the actuators, incorporating a predictive model of the plant that takes precedence over errors. Both controllers are tested at different set point changes and disturbances, determining that both do not present overshoot and that the MPC controller has a shorter establishment time and lower steady state error.
