System Identification Toolbox Research Articles

A Transfer Function Modelling Using System Identification for Air-cooling Photovoltaic System

Mathematical modelling is essential in comprehending, optimising, and administering air-cooling photovoltaic (PV) systems by considering factors like temperature, irradiance, and module characteristics. Mathematical modelling allows for informed decision-making, optimisation, and risk management in designing, operating, and maintaining air-cooling PV systems. This study creates mathematical models to identify an air-cooling PV system and anticipate the PV module’s output performance based on the solar irradiance received. The air-cooling PV system is modelled using the system identification toolbox, which relies on experiment data. The modelling process utilises a black-box approach, eliminating the necessity for internal parameter knowledge. The transfer function estimation method was selected as the best non-linear model due to its superior fit percentage. Prior to the installation of the air-cooling system, the data-driven analysis produced a continuous-time transfer function with an accuracy of 90% for the PV module model, whereas the air-cooling PV system model obtained an accuracy of 94.3%. The validity of the acquired models was assessed using Simulink by employing multiple levels of PSH. The model exhibits a failure rate of less than 10% in predicting inequality. The validation results for the PV module model were 90.1% and 90.8% for high, moderate, and low PSH, respectively. Similarly, the air-cooling PV system model got validation results of 91.7%, 93.2%, and 91.5%, while the mean output voltage increased by 10.8%, 17.5%, and 15.3%. Consequently, a continuous-time transfer function model is created, which will be utilised for developing and tuning controllers in future research.

Empirical pharmacodynamic model of phenylephrine and intrathecal bupivacaine for mean arterial pressure prediction in obstetric patients presenting for elective cesarean delivery under spinal anesthesia.

Cesarean delivery under spinal anesthesia may be complicated by hypotension in up to 80% of the patients. The response to standard-of-care prophylactic phenylephrine infusion varies, and there is little guidance on achieving optimal blood pressure control. In this work, we developed a data-driven pharmacodynamic relationship between intravenous phenylephrine, intrathecal bupivacaine, and maternal mean arterial pressure (MAP) in patients presenting for cesarean delivery. In this single-center cohort study, secondary use data were available for normotensive patients presenting for cesarean delivery. Intraoperative MAP, intrathecal bupivacaine, and intravenous phenylephrine doses were recorded prospectively every minute. The recorded data were used to identify and confirm a time series (Autoregressive with Exogenous Input (ARX)) model for predicting the MAP using MATLAB 2021a System Identification Toolbox and the Prediction Error Method. An independent model validation was conducted using a second dataset collected after the model fitting stage. Model identification was performed on 172 patients, using 70% for model fitting and 30% for testing. The final ARX model, which takes the past three data points to make predictions, performed 48.9% better than a mean constant model for one-minute ahead MAP predictions with a root mean square error (RMSE) of 3.6 ± 1.3mmHg. Similar performance was observed on independent validation using a second dataset (N = 84), yielding an RMSE of 4.2 ± 1.6mmHg for one-minute ahead MAP predictions. Our ARX model showed good performance at up to a three-minute prediction horizon and could be used for future decision support applications to guide phenylephrine dose titration.

Obtaining the transfer function using MATLAB’s System Identification and PID controller for a temperature plant of an USP-type reactor

The MATLAB System Identification Toolbox optimizes experimental developments and accurately models physical systems using experimental data. Valued for their simplicity and robustness, PID controllers are crucial in industries such as food, pharmaceutical, and chemical. USP reactors are key in the synthesis of nanomaterials and coating of substrates with thin films, as well as in ultrasonic spraying and atomization. In this process, the substrate temperature is critical to ensure material.

Workpiece temperature control in friction stir welding of Inconel 718 through integrated numerical analysis and process control

Friction stir welding (FSW) offers significant advantages over fusion welding, particularly for high-strength alloys like Inconel 718. However, achieving optimal surface quality in Inconel 718 FSW remains challenging due to its sensitivity to temperature fluctuations during welding. This study integrates finite element simulations, statistical analysis, and advanced control methodologies to enhance weld surface quality through adequate thermal management. High-fidelity simulations of the FSW process were conducted using a validated 3D transient COMSOL Multiphysics model, producing a comprehensive dataset correlating process parameters (rotational speed, axial force, and welding speed) with workpiece temperature. This dataset facilitated statistical analysis and parameter optimization through Analysis of variance (ANOVA) method, leading to a deeper understanding of process variables. A nonlinear state-space system model was subsequently developed using experimental data and the system identification toolbox in Matlab, incorporating domain-specific insights. This model was rigorously validated with an independent dataset to ensure predictive accuracy. Utilizing the validated model, tailored control strategies, including proportional-integral-derivative (PID) and model predictive control (MPC) in both single and multivariable configurations, were designed and evaluated. These control strategies excelled in maintaining welding temperatures within optimal ranges, demonstrating robustness in response times and disturbance handling. This precision in thermal management is poised to significantly refine the FSW process, enhancing both surface integrity and microstructural uniformity. The strategic implementation of these controls is anticipated to substantially improve the quality and consistency of welding outcomes.

Development of a PID-Controlled Refrigeration System for Reduced Power Consumption

Refrigerator systems are used for food preservation as well as other applications. However, the cost of running the system is high due to rising fuel and electricity prices. Traditionally, these systems are controlled by On/Off controllers. This study proposes the use of a proportional-integral-derivative (PID) controller algorithm to reduce costs for domestic and industrial refrigeration without negatively affecting the system performance. To accomplish this, a physical model was developed, comprising a domestic refrigerator, microcontroller, and MATLAB computer software for analysis. A mathematical model of second-order lead and second-order lag transfer function was also developed for a typical refrigerator system. The physical model was connected, and open-loop temperature-time response data was collected for system modeling. In addition, Data were collected from industries namely Fan Milk Industry and Benue State University Teaching Hospital Mortuary for a robust system analysis. All data sets were imported into MATLAB's system identification toolbox to estimate model parameters. The ultimate gains, frequency, and period were determined for each feedback closed-loop model, allowing the application of Ziegler-Nichols and Tyreus-Luyben PID tuning settings. The closed-loop models were then simulated in MATLAB to evaluate system performance. Simulation results showed that the Tyreus-Luyben model performed better, and offered better temperature response, less undershoot, and faster settling time than the Zeigler-Nichols method. Both PID models outperformed the traditional On/Off controller, with energy consumption reduced to less than one-third of the conventional method. The study concludes that PID controllers are a better alternative to On/Off systems when properly tuned.

Obtaining Vibration Models of a Lattice Structured Cantilever Beam Constructed with Different Types of Materials

Lattice structures provide lightweight engineering components with optimal distribution. Lattice structures have been used in many engineering applications in recent years. In literature, studies including vibration models of lattice structured beams are not common. In this study, cantilever beams with lattice structures were modeled using three types of materials in the Ansys program. 20 kN force was applied to each beam and the displacements that occurred at the free end side of the beams were recorded. By using input and output data sets belonging to the vibration of the beams and the System Identification Toolbox of MATLAB, discrete-time transfer function models, discrete-time state space models, and nonlinear ARX models were obtained. The results for the success of the obtained vibration models of the cantilever beams were compared and discussed using tables and figures. It can be said that the lattice Ti6Al4V beam is superior to the measured and model output compared to other materials.

INVSID 1.0: An Inverse System Identification Toolbox for MATLAB

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process from theoretical derivation to toolbox creation of developing the toolbox is demonstrated. Afterwards, numerical examples are illustrated to describe how the toolbox can be used to solve inverse identification problems. Simulation results demonstrate the effectiveness of the toolbox.

Visualization and Control System for a Wastewater Laboratory Plant with Biological Treatment

This paper presents a novel real-time control and electronic instrumentation system (RTC\andEIS) for a prototype plant that uses activated sludge for wastewater treatment. The main contribution in this research was oriented to work on an experimental design with small dimensions and low cost. This design was carried out using two continuous flow storage tanks in which an identification process was carried out with the MATLAB system identification toolbox to know a mathematical model. The mathematical representation obtained is presented as a continuous and a discrete transfer function. To evaluate the performance of the temperature control and instrumentation system, we implemented a simulation of the closed-loop control system and then implemented it using a DAQ, LabVIEW software, electronic devices and the wastewater treatment plant prototype to compare both results. Finally, the secondary treatment effluent was analyzed in a certified water laboratory to have a detailed analysis compared to the initial influent.

Design and Implementation of PID Controller for the Cooling Tower's pH Regulation Based on Particle Swarm Optimization PSO Algorithm

The PH regulation of cooling tower plant in southern fertilizers company (SCF) in Iraq is important for industry pipes protection and process continuity. According to the Mitsubishi standard, the PH of cooling water must be around (7.1 to 7.8). The deviation in PH parameter affects the pipes, such as corrosion and scale. Acidic water causes pipes to corrode, and alkaline water causes pipes to scale. The sulfuric acid solution is used for PH neutralization. The problem is that the sulfuric acid is pumped manually in the cooling tower plant every two or three hours for PH regulation. The manual operation of the sulfuric acid pump makes deviations in the PH parameter. It is very difficult to control the PH manually. To solve this problem, a PID controller for PH regulation was used. The reason for using the PID controller is that the PH response is irregular through the neutralization process. The methodology is to calculate the transfer function of the PH loop using the system identification toolbox of MATLAB, to design and implement a PID controller, to optimize the PID controller response using particle swarm optimization PSO algorithm, and to make a comparison among several tuning methods such as Ziegler Nichols (ZN) tuning method, MATLAB tuner method, and PSO algorithm tuning method. The results showed that the PSO-based PID controller tuning gives a better overshoot, less rise time, and an endurable settling time than the other tuning methods. Hence, the PH response became according to the target range. The experimental results showed that the PH regulation improved using the PSO-based PID controller tuning.

Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox

This paper aims to design an accurate angular position control for DC motors using a proportional integral derivative (PID) controller. The estimated DC motor parameters have been calculated using the parameter estimator toolbox in MATLAB, Arduino Mega 2560 and speed sensor to build an accurate model in MATLAB. The optimized PID coefficients are found for the DC motor model using the firefly algorithm (FA), which aims to make the actual angle match the desired value without overshooting and oscillations. The PIC16F877A microcontroller was used to implement the code based on optimized PID coefficients found in MATLAB/Simulink to generate the suitable pulse width modulation (PWM) output. In this work, step input was tested to analyze the characteristics of the system response in terms of rise time, settling time and overshoot. It was found that the controller output response curve which is produced from FA-based-PID reached the desired position without overshoot and any oscillations. The findings established that closed-loop control of any system using a system identification toolbox and optimized PID technique can be applied in real applications using low-cost controllers and sensors such as PIC16F877A microcontroller and analog rotary position sensor, respectively.

Modeling, Control and Optimization of a Small Scale CHP System in Island operating Mode based on Fuzzy logic controller

This paper presents the modeling, control scheme and optimization of a small scale combined heat and power (SSCHP) in island operating mode. A thermo dynamic model is carried out for SSCHP engine using Carnot machine method, while an optimization technique is formulated to identify CHP engine parameters. In addition, a dynamic model is built based on a system identification toolbox for SSCHP engine to study the dynamic behavior of the SSCHP during operating mode. In island mode, the Fuzzy logic controller (FLC) is used to regulate the inputs of SSCHP, and to match electric load demand. The complete system was represented and simulated in Matlab/Simulink.

Accuracy assessment of identification models and their influence on control systems

The work investigates step-response system identification algorithms of the plant of the third and higher orders. A transfer function with a time delay that has multiple poles of the Nth order is considered as a general identification model. Also, certain cases of approximation are included: aperiodic first-order plus time delay (FOPTD) and Nth order transfer function without time delay. Provided an algorithm of finding optimal values of model parameters that minimize the integral criterion of identification error. They are presented the comparative results of the identification of the 7th order test model for two criteria: Integral Square Error (ISE) and Integral Absolute Error (IAE). Additionally, an area method of the identification algorithm was implemented and used. For comparison, the identification of the test model was made by using the process() function from the System Identification Toolbox / MATLAB library. As a result, 11 identification models were obtained. For each model, the coefficients of the PI controller were calculated with a predetermined optimality criterion. As a benchmark, the optimal values of the coefficients of the PI controller for the test model were found. When the obtained identification models were used for the design of the PI controller, similar values and a slight deterioration of the integral quality criterion were discovered. The best results have been received by using the developed method and ISE as a criterion for optimization. The best structure of the identification model is a transfer function with a time delay which has multiple poles of the Nth order. A good result was shown by the transfer function with multiple poles of the Nth order without time delay. The advantage of this model structure is a fast algorithm for searching needed parameters. Also, an effective result was obtained by the searchless method of areas. The advantage of the process function is the ability to work with different types of input and output signals. At the same time, it showed worse results when it was used in step-response system identification.

Comparison Study between Open-Loop and Closed-Loop Identification for Industrial Hydraulics Actuator System

In control applications involving motion control, precise control of the Industrial Hydraulics Actuator (IHA) is necessary to accurately assess the position of the actuator rod. Non-recursive identification is used to model the system with an open-loop and closed-loop approach for this paper. The grey box approach is used to estimate the continuous model and parameter estimation for the system. The System Identification toolbox in MATLAB is used for the model estimation. The process starts by obtaining the input-output data from the experimental work. The input-output data validation outcome reveals a best-fit percentage for open-loop 88.91% and closed-loop 95.43%, as well as a small Root Mean Square Error (RMSE). This indicates a successful validation between the estimated model and the actual system due to a high degree of agreement. The open-loop and closed-loop identification have proven successful as there is no noteworthy disparity in the outcomes. The closed-loop system can be applied to unstable systems and any potential system.

Multiple-input multiple-output Radial Basis Function Neural Network modeling and model predictive control of a biomass boiler

Multiple-input multiple-output Radial Basis Function Neural Network modeling and model predictive control of a biomass boiler

Comparative study of the efficiency, effectiveness and robustness of the TGE S.A. Day-Ahead Market model and system

The article contains selected results of comparative research on the quality of the parametric model, corrected in selected situations with the use of ANN and the Day-Ahead Market system of TGE S.A. carried out in MATLAB and Simulink. The System Identification Toolbox library was used for identification tests and Simulink for simulation and comparative tests. The comparative studies used suchmeasures of model and system quality as: efficiency, effectiveness and robustness. Their waveforms as well as their average values and absolute errors and relative errors between the identification model or the identification-neural model and the system were obtained. The results of general tests were shown for the hours: 6:00, 12:00, 18:00 and 24:00 in 2019, and the detailed tests for 6:00. The sensitivity of the waveforms obtained in terms of model quality and the Day-Ahead Market system was also tested, depending on the assumed values of such parameters as e.g. electricity volume or volume-weighted average price of electricity.

Development of CAVLAB—A Control-Oriented MATLAB Based Simulator for an Underground Coal Gasification Process

The Cavity Simulation Model (CAVSIM) is a 3D, parameterisable simulator of the Underground Coal Gasification Process (UCG) that serves as a benchmark for UCG prediction. Despite yielding accurate outputs, CAVSIM has some limitations, which chiefly include inadequate graphical capabilities to visualise cavity geometry and gas production, time-ineffectiveness in terms of parametrisation, i.e., it involves editing, compiling multiple files and checking for errors, and lack of tools to synthesise a controller. Therefore, to compensate for these shortcomings, the services of third-party software, such as MATLAB, must be procured. CAVSIM was integrated with MATLAB to utilise its functionalities and toolboxes such as System Identification, Neural Network, and Optimization Toolbox etc. The integration was accomplished by designing C-mex files, and furthermore, the simulation results in both environments exhibit the same behaviour, demonstrating successful integration. Consequently, CAVSIM has also acquired a controllable structure, wherein parametrisation is now a single-click process; this is demonstrated by a case study outlining the implementation of Model Predictive Control (MPC) on a UCG plant. Moreover, the performance metrics, i.e., Mean Average Error (MAE) and Root Mean Square Error (RMSE) of 0.13, 0.23 for syngas heating value, and 0.012, 0.02 for flowrate quantitatively establishes the efficacy of CAVLAB in designing MPC for the UCG system. The novelty of this work lies in making the software package open-source with the aim of streamlining the research of multiple aspects of the UCG process.

Data-Driven Modelling of Soil Moisture Dynamics for Smart Irrigation Scheduling

In the face of increasing water scarcity and uncertainties of climate change, improving crop water use efficiency and productivity, while minimizing negative environmental impacts, is becoming crucial to meet the surging global food demand. Smart irrigation has a potential of improving water use efficiency in precision agriculture especially when efficient irrigation control strategies are adopted. Conventionally, irrigation systems rely on heuristic methods to schedule irrigation which either leads to over-irrigation or under-irrigation. This influences the crop physiological characteristics as well as the water use efficiency. To tackle this menace, model-based irrigation management has been overemphasized. A closed-loop irrigation control strategy relies on a mathematical model of the system for irrigation scheduling decisions. In this study, a data-driven approach was used to learn soil moisture dynamics from a drip irrigated tomato in an open field agricultural system. A total number of 9674 data samples were collected using an ATMOS41 weather station, TERROS 12 soil moisture sensor and a YFS-201 flow sensor for crop evapotranspiration and precipitation, soil moisture and irrigation volumes respectively. Data driven modelling was then performed using the system identification toolbox in a MATLAB environment. The model formulation was a multi-input single-output (MISO) system with reference evapotranspiration, irrigation and rainfall as inputs and soil moisture as the output. Different model structures including transfer functions, state space models, polynomial models and ARX models were evaluated. Model performance was evaluated using the mean square error (MSE), final prediction error (FPE) and estimated fit of the model approaches. Simulation results indicate that the soil moisture dynamics model provides a satisfactory approximation of the process dynamics with a state space model giving an estimated fit of 97.04 %, MSE and FPE of 1.74×10−7 and 1.75×10−7 respectively. This model will be used to design a model predictive controller for smart irrigation scheduling in open field environmental conditions.

Rogowski coil-based autonomous fault management strategy using Karen Bell Transformation for earthed active distribution networks

Rogowski coil-based autonomous fault management strategy using Karen Bell Transformation for earthed active distribution networks

Focus on software, data acquisition, and instrumentation

Focus on software, data acquisition, and instrumentation

An Advanced Fractional Order Method for Temperature Control

Temperature control in buildings has been a highly studied area of research and interest since it affects the comfort of occupants. Commonly, temperature systems like centralized air conditioning or heating systems work with a fixed set point locally set at the thermostat, but users turn on or turn off the system when they feel it is too hot or too cold. This configuration is clearly not optimal in terms of energy consumption or even thermal comfort for users. Model predictive control (MPC) has been widely used for temperature control systems. In MPC design, the objective function involves the selection of constant weighting factors. In this study, a fractional-order objective function is implemented, so the weighting factors are time-varying. Furthermore, we compared the performance and disturbance rejection of MPC and Fractional-order MPC (FOMPC) controllers. To this end, we have chosen a building model from an EnergyPlus repository. The weather data needed for the EnergyPlus calculations has been obtained as a licensed file from the ASHRAE Handbook. Furthermore, we acquired a mathematical model by employing the Matlab system identification toolbox with the data obtained from the building model simulation in EnergyPlus. Next, we designed several FOMPC controllers, including the classical MPC controllers. Subsequently, we ran co-simulations in Matlab for the FOMPC controllers and EnergyPlus for the building model. Finally, through numerical analysis of several performance indexes, the FOMPC controller showed its superiority against the classical MPC in both reference tracking and disturbance rejection scenarios.