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# PARAMETERS SETTING OF CONTROLLERS

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This paper presents the performances of Proportional (P), proportional-integral (PI) and proportional- integral-derivative (PID) modes controller to control an automatic water level control system. This project is developed to verify the performance of water level control system using PID control modes. The measurements of water level control syste...

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Context 1
... parameters of each controller modes P, PI and PID are set as in Table 3. ...

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## Citations

... In the few past decades, some researchers have invented the design and the implementation of the liquid level of a coupled-tank system controller such as the Proportional-Integral-Derivative (PID) type controllers [2], the backstepping controller [3,4], the nonlinear constrained predictive algorithms based on the feedback linearization control [5], the second-order sliding mode control [6], Constrained Pole Assignment Control (CPAC) [7,8], and neurofuzzy sliding mode controller (NFSMC) [9]. erefore, industrial process control engineering has immensely benefited from the technology development brought by digital computers and their sophisticated software. ...
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This paper presents an implementation of two radically different control schemes for a state-coupled two-tank liquid-level system. This is due to the purpose of transferring theoretical studies to industrial systems. The proposed schemes to be introduced and compared are the nonsingular terminal sliding mode control (NTSMC) and the backstepping control (BC). The performances of the developed methods are experimentally tested on a particular class of second-order nonlinear systems. The main purpose of the considered control schemes is to achieve a tracking trajectory for a coupled-tank system. It is proved that the designed robust controllers guarantee the stability of the corresponding closed loop systems. The obtained results are verified with the same setup test to ensure a suitable basis for their comparison. During the experiments, we resorted to adding an integrator to the backstepping control so that we improve the results, leading to the appearance of the integrator backstepping control (IBC). To focus on the adequacy and applicability of the suggested control layout, theoretical comparisons as well as experimental results are afforded and debated.
... Then we apply first-order Taylor expansion to the above Eq. (19), yielding ...
... In order to get a, one commonly used method in control theory is to set a = 1 t so that we have y(t) = C (1 − exp(−1)) ≈ 0.632C . By doing this, we can derive a based on the training steps t as KL-divergence reaches 63.2% of its final value C [19] in our experiments. In general, it takes many training steps to reach that goal, so a is a small and positive value. ...
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This paper challenges the common assumption that the weight of $\beta$-VAE should be larger than $1$ in order to effectively disentangle latent factors. We demonstrate that $\beta$-VAE with $\beta \leq 1$ can not only obtain good disentanglement but significantly improve the reconstruction accuracy via dynamic control. The goal of this paper is to deal with the trade-off problem between reconstruction accuracy and disentanglement with unsupervised learning. The existing methods, such as $\beta$-VAE and FactorVAE, assign a large weight in the objective, leading to high reconstruction errors in order to obtain better disentanglement. To overcome this problem, ControlVAE is recently developed to dynamically tune the weight to achieve the trade-off between disentangling and reconstruction using control theory. However, ControlVAE cannot fully decouple disentanglement learning and reconstruction, because it suffers from overshoot problem of the designed controller and does not timely respond to the target KL-divergence at the beginning of model training. In this paper, we propose a novel DynamicVAE that leverages an incremental PI controller, a variant of proportional-integral-derivative controller (PID) controller, and moving average as well as hybrid annealing method to effectively decouple the reconstruction and disentanglement learning. We then theoretically prove the stability of the proposed approach. Evaluation results on benchmark datasets demonstrate that DynamicVAE significantly improves the reconstruction accuracy for the comparable disentanglement compared to the existing methods. More importantly, we discover that our method is able to separate disentanglement learning and reconstruction without introducing any conflict between them.
... The Control system finds numerous applications in the field of process control. Regulating the height and temperature of the liquid in a liquid tank are some of the main problems encountered by these industries [5][6]. These industries find it very hard to regulate and maintain the height and temperature of the liquid. ...
... This paper mainly emphasizes one such problem, the liquid level control [1], [2], [4], [6]. There are numerous methods designed in Control systems particularly to resolve such problems [5]. This paper proposes three methods: the Proportional-Integral-Derivative controller (PID), Tilted-Integral-Derivative controller (TID), and Fractional Order PID controller (FOPID). ...
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At present, process control industries face serious problems related to the liquid level. These industries find it hard to maintain and regulate the liquid level so to attain efficient product quality because the level of the liquid needs to be maintained at a pre-defined rate. Without any interruption, the level of the liquid needs continuous monitoring and control that is only possible by designing a suitable controller or control system. The Proportional-Integral-Derivative (PID), Tilted-Integral-Derivative (TID) and Fractional Order PID (FOPID) controllers are designed based on artificial intelligence techniques to regulate the liquid level. This paper analyses and compares the liquid level control for a process by classical PID, TID and FOPID controllers. The simulation results show far better peak value (PV), settling time (ST), rise time (RT), steady-state error(Ess), Integral of Time Multiplied by Absolute Error (ITAE), Integral of Absolute Error ( IAE) and Integral of Square Error (ISE) with FOPID controller, thus ensuring overall higher efficiency.
... In power generating plants, for continuous and efficient power production there is need to monitor and maintain the boiler water tank level. In many industries, due to non-linearity present, the conventional PID control scheme is not easy to implement [1] [2]. Fuzzy control systems are also long in use for process level control of a plant. ...
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In modern industrial applications, the process liquid level needs to be maintained at a pre-defined level to achieve higher efficiency and final product quality. For uninterrupted electric power generation, the boiler tank water level has to be continuously controlled and monitored. The Proportional-Integral-Derivative (PID) is commonly used in such processes to attain better response. The PID Controllers have lesser development costs, superior features and yields in better system output. In this paper, the PID gain terms are tuned by Particle Swarm Optimization (PSO) algorithm. The PSO has simple concept, implementation is simple. The process liquid level control of a plant by conventional PID and PSO optimized PID controllers has been analyzed and quantitatively compared. The computer simulation results show far better settling time and peak values with PSO optimized PID control, thus ensuring overall higher efficiency.
... PID controllers have been established for decades in control devices with greater use in the industry due to their simplicity, robustness and the existence of multiple methods of tuning their parameters. Nearly 90 percent of today's applications in industries use PID controllers in various technologies, either as stand-alone controllers, as part of a DCS or in a PLC, see [8], [9], [10] and [11]. ...
... Most of these solutions by embedded systems were focused on modeling, analysis, and implementation of simple algorithms, including classical PID and its various modifications [2]. However traditional approach based on PID-type control scheme is not fully applicable when it has to deal with nonlinear features of a tank, especially with multi-loop control tasks [3]. Furthermore, systems become more complex and requirements rise (energy efficiency, reliability, etc.). ...
... Many control systems have been developed and applied in irrigation networks to improve their performance (Litrico and Fromion, 2005;Stringam and Esplin, 2006;Ooi and Weyer, 2008;Isa et al., 2011;Li -fang, 2012;Figueiredo et al., 2013); some of them have succeeded and some have failed. ...
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Poor performance of irrigation networks necessitates their improvement. One of the measures for performance improvement is application of automatic systems such as hydro-mechanical gates. The decision-making process for application of automatic systems is a complicated task. Reasons for this complexity are the wide range of existing technologies to be selected from, interactions between components and economic and social short- and long-term impacts. Development of a suitable model which addresses all aspects of automation is needed. One suitable technique for coping with this is a system dynamic approach. In this research, the dynamic behaviour of irrigation networks using automated control systems is investigated and its conceptual model is developed as a first step of a system dynamic model. A questionnaire based on system dynamic archetypes was designed and completed by irrigation experts from six irrigation networks in Iran. In addition, interview techniques were used to collect data. The conceptual model was developed by a system dynamic approach. The conceptual model includes fixes that fail, limits to growth, eroding goals, success to the successful, and shifting the burden archetypes. The interactions between social, managerial, economic, technical and water resources in the long term, which play a significant role, are well elaborated in the developed conceptual model. Copyright
... A boiler drum level control was done by the self adaptive fuzzy PID controller whose response is better than conventional controller. A sliding mode control was implemented for water tank level control to maintain the level output at given value [18]. ...
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This paper investigates the performance of fuzzy and Particle Swarm Optimization (PSO) based PID controllers for liquid level control of two tank non-interacting system. The main aim is to minimize system performance specifications, such as rise time, settling time, peak time and peak overshoot. Initially a fuzzy controller is applied and then to achieve further improvement in performance PSO is chosen. It is observed that the PSO gives better and satisfactory response than fuzzy controller.
... The most important element of the control system is a controller. Personal computers (PCs) connected to data acquisition (DAQ) cards [8]- [11], compact proportional-integral-derivative (PID) control devices [12], programmable logic controllers (PLCs) [13], [14], microcontrollers (μCs) [6], [15], [16] and digital signal processors (DSPs) [17] are the most commonly used controllers in practically all industrial control applications. These controllers interconnect all parts of the physical and non-physical of a system. ...
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In engineering education, the combination with theoretical education and practical education is an essential problem. The taught knowledge can be quickly forgotten without an experimental application. In addition, the theoretical knowledge’s cannot be easily associated applications by students when they start working in industry. To eliminate these problems, a number of education tools have been developed in engineering education. This article presents a modelling, simulation and practice study of a newly designed liquid level training set developed for the control engineering students to simulate, examine and analyze theoretically and experimentally the controllers widely used in the control of many industrial processes. The newly designed training set combines two control structures, which are a computer-based control and a digital signal processing-based control. The set displays the results related to experiments in real time as well as. These features have made it a suitable laboratory component on which the students can both simulate and test the performance of liquid level control systems by using theoretical different control structures.
... Therefore, it is a requirement application in many areas such as the fluid level in the nuclear tank and the water treatment plant, dry industry [19]. Because of the slight fluctuations in the liquid level control is the most common in PID method [20]. In addition, where PID is not enough for the level control, PID is used in combination with fuzzy logic [21]. ...
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Process control has been one of the application topics employed in the industry for years. Nowadays, many experimental setups are designed and produced for educational purposes to make the students be familiar with industrial process systems. In this study, an original process control experimental setup has been designed and constructed for academic uses. Real industrial components such as high performance pumps and materials are used in the experimental setup so that the quality of the system was increased to get a better system. The system was designed to apply various control techniques such as P, PI, PID and fuzzy logic. A developed PLC, Siemens S7-1200, was employed as a controller of the system. The water is used as a fluid to realize control algorithms. Therefore, the basic process control parameters such as liquid level, flow rate, temperature and pressure in the tank can be controlled at this process experimental setup. The main purpose of the system is to use a higher capacity products and give a good education to the students.