Modeling, Simulation, and Temperature Control of a Thermal Zone with Sliding Modes Strategy

Florez, Frank; Córdoba, Pedro Fernández de; Higón, José Luis; Olivar, Gerard; Taborda, John Alexander

doi:10.3390/math7060503

Cited by 10 publications

(5 citation statements)

References 30 publications

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“…In addition, Equation ( 8) can be extended to include thermal loads by adding the term ±Q, taking Q as the power of the load, positive when the load is a heating load, and negative when the load is a cooling system. This case was studied in previous works and was not analyzed in depth in this paper [40,42].…”

Section: Case 1: a Single Thermal Zone (M = 1)mentioning

confidence: 99%

Design of an Algorithm for Modeling Multiple Thermal Zones Using a Lumped-Parameter Model

et al. 2023

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The generation of mathematical models for the analysis of buildings with multiple thermal zones is a large and complex task. Furthermore, the order and complexity of the dynamical model are increased by the number of included thermal zones. To overcome this problem, this paper presents an algorithm to define the mathematical model automatically, using the geometric and physics parameters as inputs. Additionally, the spatial position of each thermal zone must be recorded in an arrangement called a contact matrix. The algorithm for modeling systems with multiple thermal zones is the main contribution of this work. This algorithm is presented in pseudocode format and as an annex, an implementation in MATLAB software. One of the advantages of this methodology is that it allows us to work with parallelepipeds and not necessarily cubic thermal zones. The algorithm allows us to generate mathematical models with symbolic variables, starting from the knowledge of how many thermal zones compose the system and its geometric organization. This information must be organized in a matrix arrangement called a contact matrix. Different arrays of thermal zones were constructed with wooden boxes to verify the functionality of the models generated with the algorithm. Each case provided information that allowed us to adjust the mathematical models and their simulations, obtaining a range of errors between experimental and simulated temperatures from 2.08 to 5.6, depending on the number of thermal zones studied.

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Section: Case 1: a Single Thermal Zone (M = 1)mentioning

confidence: 99%

Design of an Algorithm for Modeling Multiple Thermal Zones Using a Lumped-Parameter Model

et al. 2023

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“…We have considered a simulator based on the mathematical model, obtained with the technique of Lumped Parameters, which is described in [17]. This model lays on the analogy between thermal and electrical phenomena.…”

Section: Mathematical Model and Tuning Processmentioning

confidence: 99%

“…Resistances R i,in and R i,ex are calculated with the convection and radiation coefficients that are tuned to the specific conditions of the test. For convection with natural ventilation we initially took 60 kJ and the emissivity coefficient of white painted wood was set to 0.9, [17,18]. With these values, we run a fitting algorithm to look for the values of the parameters that provide a best fitting of the solutions respect to the data obtained from the indoor experiments.…”

Section: Mathematical Model and Tuning Processmentioning

confidence: 99%

A system to monitor and model the thermal isolation of coating compounds applied to closed spaces

et al. 2020

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Original scientific paper https://doi.org/10.2298/TSCI190525077M Smart control systems and new technologies are necessary to reduce the energy consumption in buildings while achieving thermal comfort. In this work, we monitor the thermal evolution inside a scale reduced closed space whose exterior and/ or interior wall faces have been painted with a coating solution. Based on the experimental data obtained under different environmental conditions, a simulator was developed and tuned to reproduce the thermodynamic behavior inside the spaces, with a relative error of less than 3.5%. This simulator lets us also estimate energy savings, temperature, and flux behavior under other conditions.

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“…Sliding mode control (SMC) is a nonlinear control approach that drives the state trajectory of the system onto a specified sliding surface and maintains the trajectory on that surface for the subsequent time under system uncertainties and perturbations. However, in conventional SMC design, a priori knowledge of the bounds on system uncertainties must be acquired [23][24][25]. Several SMC-based strategies to control Stewart platforms are proposed and verified by simulations: SMC with perturbation estimation [26], integral SMC [5], continuous higher order SMC [27], and SMC with fuzzy tuning design [28].…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of a Sliding Mode Control for a Stewart Platform Used in Aerospace Inspection Applications

et al. 2020

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The authors introduce a new controller, aimed at industrial domains, that improves the performance and accuracy of positioning systems based on Stewart platforms. More specifically, this paper presents, and validates experimentally, a sliding mode control for precisely positioning a Stewart platform used as a mobile platform in non-destructive inspection (NDI) applications. The NDI application involves exploring the specimen surface of aeronautical coupons at different heights. In order to avoid defocusing and blurred images, the platform must be positioned accurately to keep a uniform distance between the camera and the surface of the specimen. This operation requires the coordinated control of the six electro mechanic actuators (EMAs). The platform trajectory and the EMA lengths can be calculated by means of the forward and inverse kinematics of the Stewart platform. Typically, a proportional integral (PI) control approach is used for this purpose but unfortunately this control scheme is unable to position the platform accurately enough. For this reason, a sliding mode control (SMC) strategy is proposed. The SMC requires: (1) a priori knowledge of the bounds on system uncertainties, and (2) the analysis of the system stability in order to ensure that the strategy executes adequately. The results of this work show a higher performance of the SMC when compared with the PI control strategy: the average absolute error is reduced from 3.45 mm in PI to 0.78 mm in the SMC. Additionally, the duty cycle analysis shows that although PI control demands a smoother actuator response, the power consumption is similar.

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Modeling, Simulation, and Temperature Control of a Thermal Zone with Sliding Modes Strategy

Cited by 10 publications

References 30 publications

Design of an Algorithm for Modeling Multiple Thermal Zones Using a Lumped-Parameter Model

Design of an Algorithm for Modeling Multiple Thermal Zones Using a Lumped-Parameter Model

A system to monitor and model the thermal isolation of coating compounds applied to closed spaces

Experimental Validation of a Sliding Mode Control for a Stewart Platform Used in Aerospace Inspection Applications

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