Experimentation is crucial in engineering education. This work explores visualized experiments in online laboratories for teaching and learning and also research. Interactive and visualizing features, including theory-guided algorithm implementation, web-based algorithm design, customizable monitoring interface, and three-dimensional (3-D) virtual test rigs are discussed. To illustrate the features and functionalities of the proposed laboratories, three examples, including the first-order system exploration using a circuit-based system with electrical elements, web-based control algorithm design for virtual and remote experimentation, are provided. Using user-designed control algorithms, not only can simulations be conducted, but real-time experiments can also be conducted once the designed control algorithms have been compiled into executable control algorithms. The proposed online laboratory also provides a customizable monitoring interface, with which users can customize their user interface using provided widgets such as the textbox, chart, 3-D, and camera widget. Teachers can use the system for online demonstration in the classroom, students for after-class experimentation, and researchers to verify control strategies.
Laboratories are vital infrastructure for research and education. When conventional laboratories are not available and/or accessible due to different causes, for example, unaffordable purchases and maintenance cost, safety considerations, and crises such as the coronavirus disease 2019 (COVID-19) pandemic, online laboratories can offer alternatives1,2,3. Like conventional laboratories, significant progress such as interactive features4 and customizable experiments5 have been achieved in the online laboratories. Before and during the COVID-19 pandemic, online laboratories are providing experimental services to users throughout the world6,7.
Among online laboratories, remote laboratories can provide users with an experience similar to hands-on experiments with the support of physical test rigs and cameras8. With the advancement of the Internet, communication, computer graphics, and rendering technologies, virtual laboratories also provide alternatives to conventional laboratories1. The effectiveness of remote and virtual laboratories to support research and education has been validated in related literature1,9,10.
Providing visualized experiments is crucial for online laboratories, and visualization in online experimentation has become a trend. Different visualization techniques are achieved in online laboratories, for example, curve charts, two-dimensional (2-D) test rigs, and three-dimensional (3-D) test rigs11. In control education, numerous theories, concepts, and formulas are obscure to comprehend; thus, visualized experiments are vital to enhancing teaching, student learning, and research. The involved visualizing can be concluded into the following three categories: (1) Visualizing theories, concepts, and formulas with web-based algorithm design and implementation, with which simulation and experimentation can be conducted; (2) Visualizing the experimental process with 3-D virtual test rigs; (3) Visualizing control and monitoring using widgets such as a chart and a camera widget.
In this work, three separate visualized examples are provided to enhance teaching and learning and research, which can be accessed via the Networked Control System Laboratory (NCSLab https://www.powersim.whu.edu.cn/react).
1. Example 1: First-order system using circuit-based experimentation protocol
- Access the NCSLab system.
- Open a mainstream web browser and enter the URL https://www.powersim.whu.edu.cn/react.
- Click on the Start Experiment button on the left side of the main page to log in to the system. User name: whutest; password: whutest.
NOTE: This step also suits for other two examples (Example 2 and Example 3).
- Enter the WHULab in the left side sub-laboratory list and choose WHUtypicalLinks for experimentation.
NOTE: Six sub-interfaces are designed and implemented for different purposes to support simulation and real-time experimentation.
- Enter the Algorithm Design sub-interface.
NOTE: The user can choose a public algorithm model designed and shared by other authorized users or create a new model.
- Choose and click on the Create New Model button and enter the web-based algorithm interface. Build a circuit diagram using the provided blocks, as shown in Figure 1.
NOTE: Another operational amplifier (op-amp) (Op-Amp2 in Figure 1) is used to cancel the 180° phase shift. To ensure that the input, the resistors, and the capacitor are tunable, one variable capacitor and two variable resistors in the ELECTRIC ELEMENTS library and four constant blocks from the SOURCES library are selected from the left-side block library panel.
- Double click the corresponding blocks to set parameters as listed in Table 1. Set the X-axis range of the chart to 8 s.
NOTE: A popup window will be triggered after a double click to the block, which includes the descriptions of the block and can be used for setting the parameter. An example of the Resistor (R3) is illustrated in Figure 1.
- Click on the Start Simulation button; the simulation result will be provided in the interface, as included in Figure 1.
NOTE: This step also suits the two other examples with other test rigs. The simulation results can provide information for users to recheck the designed circuit-based system to avoid a wrong circuit. However, a faulty circuit will cause no harm to the users or the system, so the users do not have to worry about the consequences.
- Click on the Start Compilation button. Wait until the designed block diagram is generated into an executable control algorithm that can be downloaded and executed into the remote controller deployed at the test rig side to implement control algorithms.
NOTE: This step also suits the following experiments with other test rigs.
- Conduct real-time experiments using the generated control algorithm. Click on the Request Control button to apply for control of the circuit system.
NOTE: "Request control" is the scheduling mechanism for the system. Once a user is granted the control privilege, the user can conduct experiments with the corresponding test rig. Only one user can occupy the test rig at a time for physical test rigs, and the queue scheduling mechanism has been implemented to schedule other potential users based on the First Come First Served rule11. For virtual test rigs, a massive number of users can be concurrently supported. 500 concurrent user experimentation has been tested effectively. For the circuit-based system, 50 users can access the system at a time.
- Click on the Return button to the Algorithm Design sub-interface. Find the executable control algorithm under the Private Algorithm Models panel.
NOTE: The executable control algorithm can also be found in the My Algorithm panel in the Control Algorithm sub-interface.
- Click on the Conduct An Experiment button to download the designed control algorithm to a remote controller.
- Enter the Configuration sub-interface and click on the Create New Monitor button to configure a monitoring interface, as shown in Figure 2. Four textboxes for parameter tuning and one curve chart for signal monitoring are included.
NOTE: The chart on the right in Figure 2 is the same chart as the one in the left, which was added to demonstrate the data using the Suspend button.
- Link the signals and parameters with the selected widgets.
NOTE: Parameter/ Input, Parameter/ R0, Parameter/ R1, and Parameter/ C for four textboxes, respectively, and Parameter/ Input and Signal/ Output for the curve chart.
- Click on the Start button to start the experiment.
NOTE: This step also suits the following experiments with other test rigs. Users can save the configuration for future use.
- Set the input voltage to 0 V, tune the capacitor C to 5 µF (0.000005 in Figure 2), and then set the input voltage to 1 V; the dynamic process of the output voltage is illustrated in Figure 2.
- Calculate the corresponding parameters K and T.
NOTE: The time constant can be calculated when the output reaches 63.2% of the final value K after t = T, which is 0.63212. From Figure 2, it can be seen that the time duration is 1 s, thus, T = 1, which is consistent with the theory in which, T = R1C = 200000*0.000005 = 1, and K = R1/R0 = 200000 / 200000 = 1 (which equals the final value)12. Thus, the first-order system can be specified as: .
2. Example 2: Interactive and visualized virtual experimentation protocol
- Use the NCSLab system to conduct simulation and real-time experimentation.
- Log in to the NCSLab system. Enter the ProcessControl sub-laboratory and choose the dualTank test rig, and then enter the Algorithm Design sub-interface.
- Design a proportional-integral-derivative (PID) control algorithm using the web interface provided by NCSLab following the steps described in Example 1. Figure 3 is an algorithm example for the dual tank system.
- Double click on the PID controller, and tune the parameters for Proportional (P), Integral (I) and Derivative (D) terms. Set P = 1.12, I = 0.008 and D = 6.6, respectively.
NOTE: The P, I, and D terms should be tuned combined with the simulation result.
- Click on the Start Simulation button; the simulation result will pop up, which is included on the right side of Figure 3.
NOTE: It can be seen that the control performance is good, and the control algorithm is ready for real-time experimentation.
- Generate the executable control algorithm following the previously mentioned steps.
- Download the control algorithm to the remote controller and configure a monitoring interface with four textboxes for Set_point, P, I, and D, respectively.
- Include a chart for monitoring the water level and the corresponding Set_point. Choose a 3-D widget, which can provide all angles of the test rigs and animations of water level connected with the real-time data.
- Click on the Start button; then, the monitoring interface will be activated as shown in Figure 4, which provides a visualized virtual experiment.
- Set the Set_point from 10 cm to 5 cm, and then set I = 0.1 when the height of the water level in the controlled tank reaches and stabilizes at 5 cm. Reset the set-point from 5 cm to 15 cm; it can be seen from Figure 4 that there is an overshoot.
- Tune I from 0.1 to 0.01 and reset the set-point from 15 cm to 25 cm. It can be seen that the overshoot has been eliminated, and the water level can quickly stabilize at the set-point value of 25 cm.
3. Example 3: Research with remote and virtual laboratories protocol
- Conduct a real-time experiment in NCSLab.
- Log into the NCSLab system and choose Fan Speed Control in the Remote Laboratory sub-laboratory.
- Enter the Algorithm Design sub-interface. Drag the blocks to construct the internal model control (IMC) control algorithm diagram, as shown in Figure 5.
NOTE: The F(s) and Gm(s)-1 is designed as shown in Figure 5, in which the designed control algorithm using NCSLab is illustrated to control a fan speed control system in a remote and virtual laboratory mode.
- Generate the executable control algorithm and employ the fan speed control system to verify the designed IMC algorithm.
- Configure a monitoring interface. Link two textboxes with two parameters, namely, the Set_point and lambda (for λ which is the filter time constant) for tuning, and a real-time chart with the Set_point and Speed for monitoring. Select the 3-D model widget of the fan and the camera widget for monitoring.
- Click on the Start button to activate real-time experimentation. Reset the Set_point from 2,000 rpm to 1,500 rpm, and then reset it from 1,500 rpm to 2,500 rpm, the result of which is shown in Figure 6.
NOTE: It can be concluded that when λ = 1 the system can be stabilized to a step reference.
The proposed laboratory system has been used in several different disciples at Wuhan University, such as the Automation, Power and Energy Engineering, Mechanical Engineering, and other universities, such as Henan Agricultural University6.
Teachers/students/researchers are provided with great flexibility to explore the system using different virtual and/or physical test rigs, define their control algorithms, and customize their monitoring interface; thus, users at different levels can benefit from the proposed system. The visualized experiments provided by the proposed approach can potentially enhance understanding theories, concepts, and formulas.
The proposed system can be used for different types of algorithm design (Figure 1 and Figure 3 are two examples) and multi-purposes such as teaching, learning, and research (three protocols can be regarded as three application examples). The first-order system is an example that the system can be applied to typical system analysis using circuit-based diagrams.
Figure 3 and Figure 5 demonstrate that the proposed online laboratory can design simple and complex control algorithms using the designed blocks, verified through simulation and real-time experimentation with 3-D virtual and physical test rigs, respectively, as shown in Figure 4 and Figure 6.
The three examples demonstrate that the proposed interactive and visualized laboratory can achieve the following visualization as aforementioned. (1) Theory, formulas, and schematic diagrams can be visualized through web-based algorithm design and implementation, with which simulation and experimentation can be conducted. (2) With the support of the 3-D virtual test rigs, experimental processes can be visualized in the absence of physical test rigs and cameras deployed at the test rig site. In remote laboratories, the integration of 3-D test rigs can also benefit users, allowing users to view the details of the test rigs from different angles. Combining 3-D virtual test rigs with physical test rigs at the remote side can potentially enhance user experience. (3) Using developed widgets such as a chart, a camera widget, and a textbox, the monitoring, and control during the experimental process can be visualized.
Figure 1: Construction of the first-order system with blocks from the ELECTRICAL ELEMENTS library in NCSLab. The user can drag any block from the left-side block library panel and construct a system by linking the selected blocks properly. Please click here to view a larger version of this figure.
Figure 2: Real-time experiment of the first-order system with the designed control algorithm. The parameters are tunable, and the signals can be monitored with the provided widgets. Please click here to view a larger version of this figure.
Figure 3: Web-based PID control algorithm design and implementation for the dual tank system. The simulation result is included, which shows that the water level of the second tank can be controlled to the set-point value of 10 cm. Please click here to view a larger version of this figure.
Figure 4: Real-time experimentation with the dual tank system. After tuning the integral term from 0.1 to 0.01, the set-point is reset from 15 cm to 25 cm. It can be seen that the overshoot has been eliminated. Please click here to view a larger version of this figure.
Figure 5: IMC control of the fan speed control system. The inverse model of the identified fan model is an improper transfer function (for a proper transfer function, the order of the transfer function numerator must be less than or equal to the order of the denominator), which is constructed with general blocks based on the identified model. To enable a tunable filter, the filter is also built with blocks. The lambda in the figure represents the reciprocal of the λ in Equation 6 and can be tuned easily. Please click here to view a larger version of this figure.
Figure 6: Real-time control and fan speed monitoring using the fan speed control remote laboratory combined with a 3-D virtual fan system. The physical fan system is located at Wuhan University and provides remote laboratory services to users worldwide. Please click here to view a larger version of this figure.
Figure 7: Schematic diagram of the first-order system. The first-order circuit design and implementation in NCSLab are based on this diagram. Please click here to view a larger version of this figure.
Figure 8: 3-D virtual dual tank system in NCSLab. The purpose of the control is to control the water level in the second tank to the set-point value. Please click here to view a larger version of this figure.
Figure 9: Schematic of the internal model control architecture. Gm(s) is the model of the real plant G(s), Gm(s)-1 is the inverse model of Gm(s), F(s) and is the filter. The F(s), Gm(s)-1, and Gm(s) constitute the IMC controller. Please click here to view a larger version of this figure.
Table 1: Parameter configurations for the first-order circuit system. R2 and R3 are used to cancel the phase shift combined with the op-amp.
Supplementary Figure 1: Simulation warning interface when a user fails to ground a circuit. The result will warn the users, which can help them to recheck the designed circuit. Please click here to download this File.
Supplementary Figure 2: Compilation warning interface when a user fails to ground a circuit. The result will warn the users, which can help them to recheck the designed circuit. Please click here to download this File.
Supplementary Figure 3: Simulation result when a user reverses the polarity of the capacitor. A regular capacitor instead of the variable capacitor has been selected to illustrate this example. No warning message is shown, and the result is similar to Supplementary Figure 4. Please click here to download this File.
Supplementary Figure 4: Simulation result when the polarity of the capacitor is correct. A regular capacitor instead of the variable capacitor has been selected to illustrate this example. The simulation result will pop up to help the users to check the circuit. Please click here to download this File.
The presented protocol describes a hybrid online laboratory system that integrates physical test rigs for remote experimentation and 3-D virtual test rigs for virtual experimentation. Several different block libraries are provided for the algorithm design process, such as the electrical elements for circuit-based design. Users from control backgrounds can focus on learning without programming skills. The proper design of a control algorithm that can be applied to a suitable test rig should be considered. It is also challenging to design a controller to guarantee a good control performance (considering control performance index, including overshoot, settling time, and steady error) before applying it to the controlled test rig. Before compiling a control algorithm that can be used for real-time experimentation, simulation should be conducted to address potential issues. Control algorithms can be applied to other different test rigs using the system once they are integrated into the proposed system.
The background and theoretical knowledge regarding the three examples are as follows.
For the first-order system, the principle of the first-order system can be analyzed using circuit theory with the provided circuit in Figure 7. According to circuit theory12, the following two equations can be obtained. From the input side view of the op-amp, the current is
From the output side view of the op-amp, Equation 2 can be obtained
where is the impedance of the RC parallel circuit.
By combining Equation 1 and 2, the transfer function of the system can be calculated as
in which the minus sign (-) indicates a 180° phase shift of the output voltage, which is neglected in the analysis in the following steps.
Denote K = R1/R0, T = R1C, and then the transfer function of the system can be represented as
For the dual tank system, the designed 3-D water tank system is illustrated in Figure 8. The design and implementation of a previous version using Flash have been explored in the work of W. Hu et al. in 201413. The control purpose of this test rig is to control the water level in the second tank to the value of the set point. A PID controller has been used to control the dual tank. Theoretically, the PID can be expressed as14
where Kp, Ki, Kd are the coefficients for P, I, and D terms, respectively.
IMC is simple to tune with good set-point tracking performance and has been widely used to control real-life applications15. The control architecture of IMC is shown in Figure 9, in which G(s) is the real plant and Gm(s) is the model of the plant. Gm(s) is usually obtained through system identification. Gm(s)-1 is the inverse model of Gm(s), and F(s) is the filter. R(s), Y(s), and E(s) are the reference, output, and error, respectively. The F(s), Gm(s)-1, and Gm(s) constitute the IMC controller. A standard default filter F(s)16 is used in this work as Equation 6
where λ is the filter time constant, and order n is selected to ensure a proper or semi-proper IMC compensator (F(s)*Gm(s)-1).
The IMC control algorithm has been designed and applied to control the physical fan speed system through calculation, analysis, and proper design. In this work, G(s) represents a physical fan speed control system, whose model Gm(s) is identified as a second-order system
The order n of the filter F(s) is set to 1. For tuning purposes, the lambda in Figure 5 represents the reciprocal of the λ in Equation 6 and can be easily tuned. The filter is set to be the following
Web-based algorithm design allows users at an advanced level to design more complex algorithms with the support of S-function. However, more advanced control strategies for research and education, such as control strategies for multi-agent systems or networked control strategies with time constraints, are under consideration for further upgrading the proposed laboratory system.
The circuit-based system is based on simulation. One of the advantages of simulation is that the users can conduct their operations freely. They do not have to worry about the consequences since their misoperation will cause no harm to themselves and the system and test rigs, especially in an online experimentation system.
After a circuit-based system is designed, the user is supposed to run a simulation. For some cases, such as failing to ground the circuit, the simulation and compilation results will warn the users, which can help them to recheck the designed circuit (Supplementary Figure 1 and Supplementary Figure 2). For other cases, for example, reversing the capacitor's polarity (Supplementary Figure 3), no warning message will be shown when a user tries to conduct a simulation or compilation, the result of which is similar to that of a correct circuit as shown in Supplementary Figure 4.
Currently, the main limitation of the online experimentation system is that it can primarily be used for users with a control background. The circuit-based system can only be used for simulation with no hardware setups. To cover diverse engineering fields, hardware for circuit systems that can be applied to electrical and electronics engineering can be integrated. More test rigs for other areas should also be considered.
The proposed system can be utilized for teaching, learning, and research for teachers, students, and researchers. Currently, the system has been mainly used in control engineering-related disciplines. The system can potentially be applied to electrical and electronics engineering, industrial electronics, and industrial control.
The authors have nothing to disclose.
This work was supported by the National Natural Science Foundation of China under Grant 62103308, Grant 62173255, Grant 62073247, and Grant 61773144.
|Fan speed control system||/||/||Made by our team|
|https://www.powersim.whu.edu.cn/react||Made by our team|
- De Jong, T., Linn, M. C., Zacharia, Z. C. Physical and virtual laboratories in science and engineering education. Science. 340 (6130), 305-308 (2013).
- Galan, D. et al. Safe experimentation in optical levitation of charged droplets using remote labs. Journal of Visualized Experiments:JoVE. (143), e58699 (2019).
- Heradio, R., de la Torre, L., Dormido, S. Virtual and remote labs in control education: A survey. Annual Reviews in Control. 42, 1-10 (2016).
- Lei, Z. et al. 3-D interactive control laboratory for classroom demonstration and online experimentation in engineering education. IEEE Transactions on Education. 64 (3), 276-282 (2021).
- Galan, D., Chaos, D., De La Torre, L., Aranda-Escolastico, E., Heradio, R. Customized online laboratory experiments: A general tool and its application to the Furuta inverted pendulum. IEEE Control Systems Magazine. 39(5), 75-87 (2019).
- Lei, Z., Zhou, H., Hu, W., Liu, G.-P. Unified and flexible online experimental framework for control engineering education. IEEE Transactions on Industrial Electronics. 69 (1), 835-844 (2022).
- Zaman, M. A., Neustock, L. T., Hesselink, L. iLabs as an online laboratory platform: A case study at Stanford University during the COVID-19 Pandemic. In 2021 IEEE Global Engineering Education Conference (EDUCON). 1615-1623 (2021).
- Gomes, L., Bogosyan, S. Current trends in remote laboratories. IEEE Transactions on Industrial Electronics. 56 (12), 4744-4756 (2009).
- Santana, I., Ferre, M., Izaguirre, E., Aracil, R., Hernandez, L. Remote laboratories for education and research purposes in automatic control systems. IEEE Transactions on Industrial Informatics. 9 (1), 547-556 (2013).
- Maiti, A., Raza, A., Kang, B. H. Teaching embedded systems and internet of things supported by multi-purpose multi-objective remote laboratories. IEEE Transactions on Learning Technologies. 14 (4), 526-539 (2021).
- Lei, Z. et al. Unified 3-D interactive human-centered system for online experimentation: Current deployment and future perspectives. IEEE Transactions on Industrial Informatics. 17 (7), 4777-4787 (2021).
- Love, J. First order systems. Process Automation Handbook: A Guide to Theory and Practice. 571-574 (2007).
- Hu, W., Zhou, H., Liu, Z. W., Zhong, L. Web-based 3D interactive virtual control laboratory based on NCSLab framework. International Journal of Online Engineering. 10 (6), 10-18 (2014).
- Han, J. From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics. 56 (3), 900-906 (2009).
- De Keyser, R., Muresan, C. I. Internal model control: Efficient disturbance rejection for dead-time process models with validation on an active suspension system. In 2020 European Control Conference (ECC). 106-111 (2020).
- Horn, I. G., Arulandu, J. R., Gombas, C. J., VanAntwerp, J. G., Braatz, R. D. Improved filter design in internal model control. Industrial & Engineering Chemistry Research. 35 (10), 3437-3441 (1996).