Matlab System Identification Toolbox Research Articles

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.

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.

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.

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).

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.

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.

Vibration control of laminated composite cantilever beam operating in elevated thermal environments using fuzzy logic controller

In the present study, vibration control of laminated composite cantilever beam operating in the elevated thermal environment is achieved using combined experimental and numerical techniques. The impact hammer test is performed on the glass-epoxy cantilever beam at different temperatures. Experimentally recorded impact hammer force signals and piezoelectric accelerometer time-domain signals are processed through a system identification toolbox in MATLAB to obtain transfer functions of the plant models. A robust fuzzy logic controller is developed to accomplish the effective vibration control of a cantilever composite beam operating at different temperatures. The fuzzy logic controller with two inputs and one output is designed using the 20 if-then rules. The results are presented in both frequency and time domain, keeping the vibration attenuation of the fundamental frequency as the point of interest. The results indicate the proposed fuzzy logic control strategy can attenuate the vibrations of a cantilever composite beam for a wide temperature range.

Simultaneous Positive and Negative Pressure Control Using Disturbance Observer Compensating Coupled Disturbance Dynamics

This letter introduces a simultaneous positive and negative pressure controller for a pneumatic pump. For precise position or force control of the pneumatic soft actuator, the pressure from the source has to be regulated. When the positive and negative pressures are regulated, coupled dynamics caused by the pressure change affect the controller performance. In other words, changes in the positive or negative pressure concurrently act as disturbances in the opposite pressure controller. Therefore, a disturbance observer is designed to compensate for the coupled pressure dynamics. A nominal model of the system is obtained based on the output-error model used in the MATLAB System Identification Toolbox. A simulation study is also performed to design a controller when the demand for the positive and negative pressures varies. Finally, a verification of the effectiveness of the controller is done through a series of experiments. In the step response test, the maximum pressure error of the positive and negative pressure was reduced from 0.159 to 0.152 kPa and from 0.3297 to 0.2334 kPa with the disturbance observer. Where positive and negative pressures change at the same time, the root mean square of the positive and negative pressure error decreased from 0.9701 to 0.4457 kPa and from 0.9848 to 0.4634 kPa applying the proposed pressure controller.

Conformable fractional order controller design and optimization for sensorless control of induction motor

PurposeIn recent years, use of sensorless control methods for electrical motor-based variable speed drive systems has been increasing rapidly to compensate the increasing costs in industrial systems. Also, use of induction motors is popular for a long time to decrease the cost of these industrial systems. This study aims to design an effective controller to improve the sensorless speed control performance of induction motor. To achieve this, a conformable fractional order proportional integral (CFOPI) controller is designed.Design/methodology/approachThe 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. (2014). CFOPI controller coefficients are optimized using particle swarm optimization method on the created small signal-based simulation model of the system to minimize the integral time absolute error. To prove the success of the proposed method, a traditional fractional order proportional integral (TFOPI) controller is tested under the same experimental system with the CFOPI controller.FindingsTFOPI and CFOPI controllers are tested with the optimum parameters. Reference and actual speed trends are obtained for both methods. In induction motor start-up test, settling-times are measured as 8.73 and 8.44 s and steady-state oscillations are 2.66% and 0% (almost) for TFOPI and CFOPI controllers, respectively. In variable referenced speed tracking test, CFOPI performs well at all speed levels, while TFOPI fails to reach the reference speed at most speed levels.Practical implicationsProposed CFOPI control method can be easily implemented in industrial systems, thanks to its simple algorithm. digital signal peripheral interface controller (dsPIC) based driver circuit with designed CFOPI controller used in this study can be applied directly to industrial systems such as elevators, conveyors, cranes and drills. Moreover, it can improve the performance of induction motor-based variable speed drive systems.Originality/valueThe proposed method provides robust performance for induction motor used in control systems. Additionally, it does this by using less complex algorithm written on the processors according to the traditional fractional order controllers.

A SimPLE Tool for UAV Remote Pilot Training and Testing

With the ever-growing potential of using UAVs in various fields there is a need for proper training of their remote pilots. This paper presents a newly developed tool for the training and testing of UAV remote pilots, based on the measurement of their responses in controlling a simulated UAV in 2D space. The evaluation system consists of a conventional RC controller and a PC running Python scripts for the measurement and preprocessing of data and the MATLAB System Identification Toolbox for the calculation of the parameters based on the human model. Based on the initial testing results, which were carried out with an experienced pilot and a beginner, four key parameters were identified, that would be used for the evaluation of the performance of UAV remote pilots. Furthermore, the developed system could be used for training purposes to learn the basic principles and dynamics of UAVs in a safe environment.

Investigation of 2DOF PID Controller for Physio-Therapeutic Application for Elbow Rehabilitation

The aim of this work is to evaluate the output of a two-degree of freedom (DOF) proportional integral derivative (PID) controller for controlling elbow flexion and extension on an upper limb rehabilitation robot of an existing model. Since the usage of upper limb rehabilitation is increasing dramatically because of human impairment, 2DOF has been proposed in this work as a suitable controller. The 2DOF PID controller offers set-point-weight features and, hence, is fast in removing disturbance from the system and ensuring system stability. Importantly, as the system parameters are unknown in this work, the black-box model approach has been taken into consideration, using the MATLAB System identification toolbox to estimate a model. The best-fitted estimated model is then coupled with the proposed controller in the MATLAB/Simulink environment that, upon successful simulation works, leads, finally, to the hardware implementation. Three different amplitudes of sinusoidal current signals, such as 0.3 amps, 0.2 amps, and 0.1 amps, are applied for hardware measurements. Considering patients’ physical conditions. In this work, the 2DOF controller offers a fast transient response, settling time, negligible tracking error and 0% overshoot and undershoot.

Dynamic Modeling and Validation of PEM Fuel Cell via System Identification Approach

The key issues with non-renewable energy resources are the harmful pollutants they produce. They also pose serious threat to human life and lead to severe atmospheric devastation. Hence, they become non-viable for future generations. With the innovation of fuel cell technology, these difficulties are wiped out. Fuel Cell technology is considered as the most efficient and environmental friendly type of energy production. Among the different types of fuel cells, Proton-Exchange Membrane Fuel Cell (PEMFC) is the most distributed type and are widespread in the market, because of its lower operating temperature, reliable performance to frequent load changes, higher efficiency and good power density. This work primarily focuses on the dynamic modeling and simulation of PEMFC. A voltage model for PEMFC is developed based on experimental data. Estimation of the system model is done by using system identification toolbox in MATLAB. Validation of the estimated model is performed by comparing the estimated model response with first principle nonlinear PEMFC model and with a different real time data.

Modeling of doubly fed induction generator based wind energy conversion system and speed controller

In this paper, modeling of doubly fed induction generator (DFIG) based wind energy conversion system (WECS) and generator speed controller are presented. The System Identification Toolbox of MatLab is used to develop the linear model of the WECS by considering the wind speed as input and speed of the generator as output. Two models, namely Auto Regressive with eXogenous Input (ARX) and Auto-Regressive Moving Average with eXogenous Input (ARMAX), are estimated. We used the ARX221 model structure with the best fit of 84.31%, Final Prediction Error (FPE) of 0.0433 and Mean Square Error (MSE) of 0.0432. The Ziegler-Nichols (Z-N) method and the fuzzy logic technique are employed in the proportional integral derivative (PID) controller design to control the speed of the generator. The classical Z-N PID used for the responses of the system is observed to be insufficient for both uniform and variable inputs, hence, a better response has been obtained by applying the fuzzy logic-based PID controller. The present study proves that the fuzzy logic based control enhances the speed regulation of generator in the WECS by overcoming the effect of varying wind speed.

A new Graphical User Interface for the CONTSID toolbox for Matlab

Abstract The main purpose of this contribution is to describe the new features of the latest version 7.4 of the CONtinuous-Time System IDentification (CONTSID) toolbox for Matlab. The main addition is a new Graphical User Interface (GUI), which allows the user in a friendly and easy way to perform data analysis, model parameter estimation as well as model validation. The recent additions for MISO time-delay transfer function model identification are also briefly introduced.

Experimental Simulation and Verification of Position Servo Control of Mechanical Rodless Cylinder

In order to improve the position control accuracy of rodless cylinder, the valve control cylinder system based on pneumatic proportional servo is studied deeply. According to the working principle of the mechanical rodless cylinder control system, under the condition of uniform speed, the driving voltage of the proportional valve is changed to measure multiple sets of friction force and corresponding velocity data. Analyzed the physical structure of each component in pneumatic system, established the mathematical model of pneumatic system, and introduced MATLAB system identification toolbox to identify the parameters of the transfer function. and the experiment verifies its correctness.

Transfer function identification of a liquid flow-line pressure control system from simple relay auto-tuning

Accurate modeling of liquid flow systems is necessary for the effective operation of the plant under different conditions. The plant dynamics may change with time, and due to this, the operation may not be satisfactory. In this article, relay autotuning of a liquid flow-line is proposed. Well-known describing function approximation is utilized for the estimation of gain of relay used in a feedback loop to excite the plant to be identified. The expressions are derived for the identification of a class of overdamped second-order plus dead-time and first-order plus dead-time plant dynamics. The identification method tested on different standard systems shows that the proposed method has good identification accuracy. Therefore, the proposed identification method is applied to a liquid flow line pressure control system. The experimental results show that an accurate dynamic model is extracted using the proposed method with a minimal set of data from an obtained limit cycle oscillation. The measured quantities of the limit cycle are utilized in the derived explicit expressions for the identification of unknown variables of the dynamic plant model. A distributed process control system is utilized here as a standard industrial platform to design virtual relay and conduct the proposed autotuning. The efficiency of the modeling method is demonstrated through the comparison of Nyquist and relay response plots with the transfer functions obtained from the MATLAB system identification toolbox.

Model predictive control of vinyl chloride monomer process by Aspen Plus Dynamics and MATLAB/Simulink co-simulation approach

Characteristics of a vinyl chloride monomer (VCM) process is complex and nonlinear due to interactions between units in a reaction-separation network, multiple process streams, and multiple control loops involved. A fluctuation of the thermal cracking unit could result in a difficulty in maintaining downstream units at the setpoints. In this work, an approach to develop a model predictive control (MPC) for the VCM process by deploying a co-simulation between MATLAB/Simulink and Aspen Plus Dynamics is presented. The co-simulation provides more capability and comfortability to design the MPC controller, and evaluate controllability. The VCM process consisting of thermal cracking, quench and distillation is modelled by Aspen Plus Dynamics. The MPC is developed by integrating the concept of plant-wide control and subsystem partitioning. To reduce the burden of mathematical modeling, the MATLAB system identification toolbox is used to develop a multivariable linear model for the MPC controller by reconciling dynamics data from the VCM plant model. Performances of the developed MPC is evaluated under the regulatory test of the EDC feed disturbance. By comparison with the multiple single-input-single-output proportional-integral controllers through the efficiency indexes—an integral squared error, an overshoot and a settling time. Simulation results supported that the MPC controller outperforms proportional-integral controllers.