System Identification Toolbox Research Articles

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.

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

This study presents a model predictive control of a 4 × 3 Multiple-Input Multiple-Output (MIMO) biomass control system that uses radial basis function (RBF) as an activation function to enhance the control performance. The biomass boiler model is developed from the system identification box in MATLAB by collecting measured input and output data from the 31.5 MW Wonji sugar mill and analyzing the data using black box modeling approach. The major goal of this study is to improve the MIMO control performance of a biomass boiler by utilizing Radial Basis Function Neural Network (RBFNN) to increase model accuracy which ultimately enhances the overall control system. Biomass boilers have four inputs and three outputs. The inputs are airflow 1, airflow 2, water flow and stocker speed (the speed of the motor which feeds bagasse or sugarcane pulp) and outputs are temperature, pressure and drum level. The RBFNN model was programmed using MATLAB R2021a software with system identification toolbox. Shorter settling times of 0.286 s, 1.873 s, and 0.637 s, as well as tolerable overshoots of 0.505%, 1.5%, and 15.698% for temperature, pressure, and level respectively, were achieved with the suggested model predictive controller using RBFNN model. However, when utilizing the identical controller and state space model, the settling times are 2.318 s, 5.461 s, and 6.147 s, with overshoots of 4.737%, 8.152%, and 38.194% for the three variables respectively. The suggested method was compared to the model predictive control (MPC) biomass boiler state space model, which can be considered as a model without RBFNN. The simulation results indicate that the RBFNN-based MPC performed better than the state space model-based MPC. This is because, the state space model is always constant, in contrast to the neural network's active monitoring of boiler dynamics.

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

This paper introduces fault type identification, fault section selection, and fault phase selectivity using a Karen Bell Transformation matrix in earthed distribution grids coupled with Doubly Fed Induction Generator (DFIG). Rogowski Coil’s (RC) model is estimated using a system identification toolbox to obtain the precise terminal voltage. This model is calculated and verified using experimental results for the RC’s primary input current and output voltage. Then, the estimated voltage relies on the current signals in the measuring point. The transient slope of the ground and aerial modes voltages is extracted during the fault to classify the fault type, discriminate the fault section and identify the faulted phase in every cycle. Further, earth faults are detected using ground and phase faults using aerial modes. The relative polarity of the front slopes at the terminals of each section created by the fault voltage is extracted to perform faulty section discrimination. The value of aerial modes is used to identify the defective phase selectivity. The overall protection selectivity function’s integration procedure with modified autonomous control is proposed for earthed distribution systems. The Transient Selectivity Function (TSF) was presented in unearthed and compensated grids in the literature. After estimating the RC voltage output at each agent, the discrete Fourier transform (DFT) phasor extraction is done. Different standard fault cases and system conditions are investigated to evaluate the proposed scheme’s behavior. Simulation results are designed using the ATP-EMTP program. The simulation results obtained validate the effectiveness of the scheme proposed in this work.

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.

Hourly identification and simulation of the TGE S.A. Day-Ahead Market system

Abstract The paper presents selected research results concerning the identification and simulation of the TGE S.A. Day-Ahead Market (DAM) system of the day for electricity delivered and sold, listed for the following hours: 5:01-6:00, 11:01-12:00, 17:01-18:00 and 23:01-24:00 in 2019, which were obtained in the MATLAB and Simulink environment using the System Identification Toolbox. As a result of identification, four respective discrete parametric arx models were obtained, which were then subject to quality assessment. Then, a simulation model was built in the Simulink environment, which was used for simulation tests and for assessing the sensitivity of the model created using the data from 2019 as the basis and the data from 2020 for verification. The obtained results confirm the correctness of both the performed discrete parametric identification and the possibility of testing the quality of the model and its sensitivity with the use of the DAM system model in the MATLAB and Simulink environment.

Research on DC Electronic Load System Based on PSO-PID Algorithm

At present, the control part of the DC electronic load is mainly based on analog control, that is, the control loop is controlled by an operational amplifier. Therefore, it is easy to cause slow response speed and overshoot in the process of current rise, and the improvement of response speed is of great significance to the test link. Therefore, this paper studies the digital control of DC electronic load. The DC electronic load control system has the characteristics of nonlinearity, and it is difficult to complete the theoretical modeling. Therefore, the MATLAB system identification toolbox is used to obtain the mathematical model. The traditional PID tuning method has a long time and poor effect. This paper adopts a parameter self-tuning PID controller based on particle swarm optimization algorithm. Through MATLAB simulation, the traditional PID control and particle swarm optimization PID control are simulated and compared. The results show that the digital controller has faster response speed and smaller overshoot than the conventional controller, and the system performance is significantly improved.

System Identification and Control of Automatic Car Pedal Pressing System

This paper mainly explores the system identification and control of an automatic car pedal pressing system. Specifically, the system identification was achieved using an artificial neural network, with the help of MATLAB’s System Identification Toolbox. The proportional-integral-derivative (PID) controller and fuzzy logic controller were designed, and normalized with membership functions. These functions were scaled with a gain as a scaling factor. The controller gains were tuned by a metaheuristic algorithm named particle swarm optimization (PSO). On this basis, the two controllers were compared with a number of performance indices, including integral squared error (ISE), integral absolute error (IAE), integral time absolute error (ITAE), and mean squared error (MSE). The car pedal pressing performance was measured at different speed levels for each controller.

Dynamic Study and PI Control of Milk Cooling Process

The background of this research is to understand the operation process, which is the main goal of developing the process model. This model is often used for operator training, process design, safety system analysis, or control system design. The dynamic model of the milk cooling process from 36ËšC to 4ËšC using chilled water available at 2ËšC was performed. Chilled water was maintained at a constant temperature by using a refrigerant unit. The process being investigated was a Packo brand milk cooling tank belonging to KUD SAE Pujon (Malang - Indonesia). A fundamental heat balance method was used to derive the model, leading to a first-order transfer function process. For a 2-hr cooling process, the gain and time constant values are 1.00 and 42.3548 mins, respectively. Heat balance was then extended to continuous processes so that its transfer function could also be obtained. This study simulated and investigated the behavior of batch and continuous processes. Process Identification via input-output data was also introduced for continuous process. The process model obtained from the system identification toolbox was very useful in control, such as for determining tuning parameters via the Ziegler-Nichol method for Proportional-Integral control. However, a small delay was required to be introduced to the system as the first order process without time Ziegler Nichol method cannot be implemented. Further research may include other system identification methods, such as ARX, ARMAX, Output-Error, Box Jenkins etc., or implementing advanced process control for milk cooling.

Research on model identification and compound control of vibration mirror swing scan system

According to the principle of system identification, the least squares algorithm and MATLAB system identification toolbox is used to identify the model of the test bench of the vibration mirror swing scan system with the step signal and the vibration mirror swing-sweep angular displacement as the input and output quantities. Taking the identification model as the object, a sliding mode variable structures composite controller of fuzzy switching gain adjustment is designed. In the controller, according to the actual conditions achieved by the sliding mode, the switching gain is effectively estimated, so as to eliminate the interference of uncertain factors and high-frequency vibration. At the same time, the sliding mode function and sliding mode controller of the system needs to be designed. The switching hyperplane of the system is designed using the fuzzy switching method and the desired dynamic characteristics of the system, to ensure the smooth transition of the system state from outside the hyperplane to the convergence of the switching hyperplane. The simulation results show that: the new composite control method eliminates the shortcomings of PD series control, improves the response speed and tracking accuracy of the vibration mirror swing scan system, and improves the overall performance of the system. And it has a strong ability to inhibit the adverse effects caused by changes in the parameters of the controlled object.

Auto tuned PID and neural network predictive controller for a flow loop pilot plant

This paper presents an advanced control strategy that uses the neural network (NN) predictive controller to govern the dynamics of a Flow loop pilot plant. The set point tracking using NN Predictive controller and conventional PID strategies are investigated. The control objective is to keep the outlet flow of the Single Input Single Output Flow loop at a reference value. An orifice meter as a flow measuring device and Variable Frequency Drive as a final control element is selected to collect Input, Output data. System Identification toolbox in MATLAB is used to construct the model of a flow loop from measured input–output data. Various models are estimated, validated and realized that the transfer function model gives the best fit. This model is used to build the PID and NN Predictive controller. NN predictive controller parameters are set, by the designer's expertise. The performance of a NN predictive controller is then compared with the conventional auto-tuned PID controller. Experimental results are presented, which emphasizes the superiority and effectiveness of the NN predictive controller.

Self Adaptive Fuzzy Controller for Supplementary Oxygen Supply to the Respiratory Distress Patients

In this paper, respiratory irregularities of humans are investigated, and a self Adaptive Fuzzy Logic-based Proportional plus Derivative Controller (AFLPDC) is designed to provide a controlled supply of oxygen to the patient, suffering from breathlessness. The Fuzzy Logic-based Proportional plus Derivative Controller (FLPDC) is tuned, by implementing an adaptive scheme, which provides desired control action according to the process parameter variations. The electrical equivalent model of the respiratory system is developed, and the model is validated with the help of a Spirometer and MATLAB System Identification Toolbox. To build up this model, the respiratory tract is well thought out into four sections: the nasal cavity, trachea, bronchi, and finally, the alveolar sacs. Each of these sections is represented as a series of combinations of Resistance, Inductance, and Compliance. The control capability of the proposed AFLPDC is investigated on the derived respiratory model. The supply of the desired level of supplemental oxygen by AFLPDC to the patients suffering from the diseases like Bronchitis and Emphysema during respiratory dysfunction is observed in the MATLAB SIMULINK environment without human intervention. The response revealed by AFLPDC is compared with the responses of the conventional PD controller and the Fuzzy Logic-based PD controller (FLPDC).

Kalman Filter and PID Application on Underwater Vehicles

Unmanned underwater vehicles (ROV/AUV) are autonomous or remotely controlled robotic systems that can move underwater at any desired angle. Unmanned underwater vehicles; It is used in areas such as underwater image taking, ship maintenance and repair, coast guard, examination of shipwrecks, underwater cleaning. In this study, the software design of the balance control of underwater vehicles was carried out using the PID algorithm. For the PID algorithm trial, a two-motor test setup with an IMU sensor was prepared. After the data from the sensor were recorded in MATLAB using the Kalman filter, the transfer function of the system was obtained using the System Identification Toolbox. With the obtained transfer function, the stable operation of the system is provided in real time. As a result of the research on software and hardware integration, microcontroller ARM-based STM32 was used.

Conformable Fractional Order Controller Design and Implementation for Per-Phase Voltage Regulation of Three-Phase SEIG Under Unbalanced Load

Self-excited induction generators (SEIGs) usage is an effective way to generate electrical power in off-grid locations. This study aims to performance improvement for voltage regulation of the SEIG in wind turbine systems. To achieve this, a fixed-capacitor with thyristor controlled-reactor based per-phase voltage controller is designed with a conformable fractional order proportional integral (CFOPI) method. The SEIG system is modeled based on small signal analysis by using the input-output data, experimentally. To do this, system identification toolbox of Matlab is used. The proposed controller is established on conformable fractional integral approach proposed by Khalil et al. in 2014. Proposed CFOPI controller parameters Kp , Ki and γ are optimized using particle swarm optimization (PSO) according to minimization of integral time absolute error (ITAE). To validate the success of proposed scheme, CFOPI, traditional fractional order PI (TFOPI) and integer order PI (IOPI) controllers are tested under same experimental setup and results are compared. Three experiments, after voltage build-up process, under balanced and unbalanced load are done for each controller. Experimental results demonstrate that proposed voltage regulator improves the performance of SEIG systems. Moreover, robust voltage regulation is ensured thanks to independent control loops created for each phase.

DETERMINATION OF STIFFNESS AND DAMPING PARAMETERS FROM A VEHICLE MODEL IN A FRONTAL IMPACT INTO THE RIGID BARRIER USING THE MATLAB

The main goal of this paper is to create a computer crash model (up to the level of the double mass-spring-damper model, which is described by a system of two differential equations and where the stiffness and damping coefficients - physical parameters are calculated using the Matlab function Y = solve (eqns) - Equations and systems solver) using measured real data obtained from a frontal impact of a vehicle into a rigid barrier. The vehicle crash modelling process can be performed in two ways. One of them, which is also applied in this article, is based on the System Identification Toolbox, which contains MATLAB® functions, Simulink® blocks and a special application for compiling models of dynamic systems from measured I / O data.

Deep Neural Network System Identification for Servomechanism System

This paper presents a systematic technique for designing the input signal to identify the one-stage servomechanism system. Sources of nonlinearities such as friction and backlash consider an obstacle to obtaining an accurate model. Also, most such systems suffer from a lack of system parameters data. So, this study establishes a model using the black-box modeling approach; simulations are performed based on real-time data collected by LabVIEW software and processed using MATLAB System Identification toolbox. The input signal for the servomechanism system driver is a pseudo-random binary sequence that considers violent excitation in the frequency interval and the output signal is the corresponding stage speed measured by rotary encoder. The candidate models were obtained using linear least squares, nonlinear least squares, and Deep Neural Network (DNN). The validation results proved that the identified model based on DNN has the smallest mean square errors compared to other candidate models.