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Number of results: 9
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Abstract

An electric power steering system (EPS) is a new type of steering system developed after a mechanical hydraulic power system (MHPS) and electric-hydraulic power steering system (EHPS). In order to coordinate and solve the portability and sensitivity of the steering system optimally, taking an induction power steering system as the research object, the control algorithm of induction motor control under the EPS is studied in this paper. In order to eliminate the feed-forward performance degradation caused by the change of feed-forward parameters, an on-line identification algorithm of feed-forward parameters is proposed. It can improve the control performance of online identification among three feed-forward parameters in the T-axle motor, it improves on the robustness of feed-forward control performance, at the same time it also gives simulation and test results. This method can improve the control performance of the three feed-forward parameter online identification of the T-axis motor and improve the robustness of feed-forward control performance. At the same time, simulation and test results are given. The simulation results show that the algorithm can significantly improve the response speed and control accuracy of EPS system control.

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Authors and Affiliations

Zhang Naibiao
Cai Tianfang
Han Xuezheng
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Abstract

This paper presents the optimal PID tuning study to improve the dynamic performance of an automatic voltage regulation (AVR) system. The system under study consists of a synchronous generator whose reference voltage changes in a step function and tries to overcome the transient behavior of its terminal voltage smoothly. To optimally control the performance, different optimization techniques are applied to tune the controller gains to obtain the minimum steady state error (main objective) and better dynamic characteristics (rise time, settling time, max overshoot, etc.). Then the AVR system responses with a PID controller based on different optimization techniques are compared to find out which is the best technique.
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Bibliography

[1] Mahmut Temel Özdemir, Vedat Çelik, Stability analysis of the automatic voltage regulation system with PI controller, Journal of Sakarya University Institute of Science, vol. 21, no. 4, pp. 698–705 (2017).
[2] Challapuram Yaswanth Reddy et al., Laboratory implementation of Automatic Voltage Regulator, Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE), Bangalore, pp. 1–6 (2016).
[3] Saidy M., Huges F.M., A predictive integrated voltage regulator and power system stabilizer, Elsevier proceedings on Electrical Power and Energy Systems, vol. 7, no. 2, pp. 101–111 (1995).
[4] Rakesh Singh Lodhi, Abhishek Saraf, Survey on PID Controller Based Automatic Voltage Regulator, International Journal ofAdvancedResearch in Electrical, Electronics and Instrumentation Engineering, vol. 5, no. 9, pp. 7424–7429 (2016).
[5] Haluk Gozde, Cengiz Taplamacioglu M., Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system, Journal of the Franklin Institute, vol. 348, no. 8, pp. 1927–1946 (2011).
[6] Gaing Z.-L., A particle swarm optimization approach for optimum design of PID controller in AVR system, IEEE Trans. Energy Convers., vol. 19, no. 2, pp. 384–391 (2004).
[7] Elgard O.I., Electric Energy Systems Theory, New York, Mc Graw-Hill (1982).
[8] Mukherjee V., Ghoshal S.P., Intelligent particle swarm optimized fuzzy PID controller for AVR system, Electron. Power Syst. Res., vol. 77, no. 12, pp. 1689–1698 (2007).
[9] Sambariya D.K., Tripti Gupta, Optimal Design of PID Controller for an AVR System Using Flower Pollination Algorithm, Journal of Automation and Control, vol. 6, iss. 1, pp. 1–14 (2018).
[10] dos Santos Coelho L., Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach, Chaos, Solitons and Fractals, vol. 39, no. 4, pp. 1504–1514 (2009).
[11] Qader M.R., Identifying the optimal controller strategy for DC motors, Archives of Electrical Engineering, vol. 68, no. 1, pp. 101–114 (2019).
[12] Eswaramma K., Surya Kalyan G., An Automatic Voltage Regulator AVR System Control using a P-I-DD Controller, Journal of Advance Engineering and Research Development, vol. 4, no. 6, pp. 499–506 (2017).
[13] Aström K.J., Hägglund T., PID Controllers: Theory, Design, and Tuning, Instrument Society of America, USA (1995). [14] Yang X.-S., Flower pollination algorithm for global optimization, Lecture Notes in Computer Science, vol. 7445, pp. 240–249 (2012).
[15] Chiroma H., Shuib N.L.M., Muaz S.A., Abubakar A.I., Ila L.B., Maitama J.Z., A review of the applications of bio-inspired flower pollination algorithm, Procedia Computer Science, vol. 62, pp. 435–441 (2015).
[16] Mihailo Micev, Martin Calasan, Diego Oliva, Fractional Order PID Controller Design for an AVR System Using Chaotic Yellow Saddle Goatfish Algorithm, Mathematics, vol. 8, no. 1182 (2020), DOI: 10.3390/math8071182.
[17] Sahib M.A., A novel optimal PID plus second order derivative controller for AVR system, Engineering Science and Technology, an International Journal, vol. 18, iss. 2, pp. 194–206 (2015).
[18] Abdel-Raouf Osama,Abdel-Baset M., el-Henawy I., A new hybrid flower pollination algorithm for solving constrained global optimization problems, International Journal of Applied Operational Research- An Open Access Journal, vol. 4, no. 2, pp. 1–13 (2014).
[19] Sambariya D.K., Gupta T., Optimal design of PID controller for an AVR system using monarch butterfly optimization, International Conference on Information, Communication, Instrumentation and Control (ICICIC), Indore, India, pp. 1-6 (2017).
[20] Priyambada S., Mohanty P.K., Sahu B.K., Automatic voltage regulator using TLBOalgorithm optimized PID controller, 2014 9th International Conference on Industrial and Information Systems (ICIIS), Gwalior, India, pp. 1–6 (2014).
[21] Niknam Taher, Rasoul Azizipanah-Abarghooee, Narimani Mohammad Rasoul, A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems, Engineering Applications of Artificial Intelligence, vol. 25, pp. 1577–1588 (2012).
[22] Askarzadeh Alireza, Rashedi Esmat, Harmony Search Algorithm. Recent Developments in intelligent Nature-Inspired Computing (2017), DOI: 10.4018/978-1-5225-2322-2.ch001.
[23] Mohanty Pradeep, Sahu Binod, Panda Sidhartha, Kar Sanjeeb, Mishra Nandan, Performance Analysis and Design of Proportional Integral Derivative Controlled Automatic Voltage Regulator System Using Local Unimodal Sampling Optimization Technique, pp. 566–576 (2012), DOI: 10.1007/978-3-642-35380-2_66.


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Authors and Affiliations

Haya Hesham
1
ORCID: ORCID
M. Ezzat
1
Rania A. Swief
1
ORCID: ORCID

  1. Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, 1 Elsarayat St., Abbaseya, 11517 Cairo, Egypt
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Abstract

In this paper, an automatic voltage regulator (AVR) embedded with fractional order PID (FOPID) is employed for the alternator terminal voltage control. A novel meta-heuristic technique, a modified version of grey wolf optimizer (mGWO) is proposed to design and optimize the FOPID AVR system. The parameters of FOPID, namely, proportional gain ( Κ Ρ), the integral gain ( Κ I), the derivative gain ( Κ D), λ and μ have been optimally tuned with the proposed mGWO technique using a novel fitness function. The initial values of the Κ Ρ, Κ I , and Κ D of the FOPID controller are obtained using Ziegler-Nichols (ZN) method, whereas the initial values of λ and μ have been chosen as arbitrary values. The proposed algorithm offers more benefits such as easy implementation, fast convergence characteristics, and excellent computational ability for the optimization of functions with more than three variables. Additionally, the hasty tuning of FOPID controller parameters gives a high-quality result, and the proposed controller also improves the robustness of the system during uncertainties in the parameters. The quality of the simulated result of the proposed controller has been validatedby other state-of-the-art techniques in the literature.
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Authors and Affiliations

Santosh Kumar Verma
1
Ramesh Devarapalli
2
ORCID: ORCID

  1. Department of EIE, Assam Energy Institute, Sivasagar (Centre of RGIPT, Jais), Assam–785697, India
  2. Department of EEE, Lendi Institute of Engineering and Technology, Vizianagaram-535005, India
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Abstract

Tuning rules for PID and PI-PI servo controllers are developed using a pole placement approach with a multiple pole, i.e. a triple one in the case of PID and a quadruple for PI-PI. The controllers involve complex roots in the numerators of the transfer functions. This is not possible in the classical P-PI structure which admits real roots only. The settling time of the servos determined by the multiple time constant is the only design parameter. Nomograms to read out discrete controller settings in terms of the time constant and control cycle are given. As compared to the classical structures, the upper limit on the control cycle is now twice longer in the case of PID, and four times in the case of PI-PI. This implies that the settling times can be shortened by the same ratios. Responses of a PLC-controlled servo confirm the validity of the design.
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Bibliography

  1.  B. Siciliano and O. Khatib, Eds., Springer Handbook of Robotics. Berlin Heidelberg: Springer, 2008.
  2.  G. Ellis, Ed., Control System Design Guide, 4th ed. ButterworthHeinemann, 2012.
  3.  G.W. Younkin, Industrial Servo Control Systems, 2nd ed. New York: Marcel Dekker, 2002.
  4.  S.-M. Yang and K.-W. Lin, “Automatic Control Loop Tuning for Permanent-Magnet AC Servo Motor Drives,” IEEE Trans. Ind. Electron., vol. 63, no. 3, pp. 1499–1506, 2016.
  5.  G.F. Franklin, J.D. Powell, and A.F. Emami-Naeini, Feedback Control of Dynamic Systems, 7th ed. Reading: Addison-Wesley, 2019.
  6.  L. Sciavicco and B. Siciliano, Modelling and Control of Robot Manipulators. London: Springer, 2000.
  7.  T. Tarczewski, M. Skiwski, L.J. Niewiara, and L.M. Grzesiak, “High-performance PMSM servo-drive with constrained state feedback position controller,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 66, pp. 49–58, 2018.
  8.  V. Rao and D. Bernstein, “Naive control of the double integrator,” IEEE Control Syst. Mag., vol. 21, pp. 86–97, 2001.
  9.  P.B. Schmidt and R.D. Lorenz, “Design principles and implementation of acceleration feedback to improve performance of DC drives,” IEEE Trans. Ind. Appl., vol. 28, no. 3, pp. 594–599, 1992.
  10.  T. Żabiński and L. Trybus, “Tuning P-PI and PI-PI controllers for electrical servos,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 58, pp. 51–58, 2010.
  11.  D.E. Seborg, T.F. Edgar, D.A. Mellichamp, and F.J. Doyle, Process Dynamics and Control, 4th ed. New York: Wiley, 2016.
  12.  C. Grimholt and S. Skogestad, “Optimal PI and PID control of first-order plus delay processes and evaluation of the original and improved SIMC rules,” J. Process Control, vol. 70, pp. 36–46, 2018.
  13.  K.J. Åström and T. Hägglund, Advanced PID Control, Research Triangle Park, 2005.
  14.  “Maxima CAS homepage.” [Online]. Available: https://maxima.sourceforge.io/.
  15.  “ESTUN Industrial Technology Europe.” [Online]. Available: https://www.estuneurope.eu/.
  16.  “BECKHOFF New Automation Technology.” [Online]. Available: https://www.beckhoff.com/.
  17. EN 61131-3, Programmable controllers – Part 3: Programming languages (IEC 61131-3:2013), International Standard, CENELEC Std., 2013.
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Authors and Affiliations

Andrzej Bożek
1
ORCID: ORCID
Leszek Trybus
1
ORCID: ORCID

  1. Department of Computer and Control Engineering, Rzeszów University of Technology, W. Pola 2, 35-959 Rzeszów, Poland
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Abstract

PID controllers are crucial for industrial control because of their simple structure and good robustness. In order to further improve the accuracy of PID controllers, this paper proposes an improved sparrow search algorithm (ISSA) to prevent the problem of the algorithm being prone to falling into the local optimum at the late stage of iteration. Based on the standard sparrow search algorithm, the position update formula and the step size control parameter are optimized to help quickly jump out of the local, and to obtain the optimal solution in the whole domain. Finally, to verify the accuracy and stability of the improved algorithm, nine standard test functions are first simulated. Then, the PID parameter optimization tests are finished with the chilled water and battery charging systems, where the lifting load and applying perturbation are carried out. Both the simulation and test results show that ISSA improves the convergence speed and accuracy, and performs better in terms of stability.
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Authors and Affiliations

Mingfeng Zhang
1
Chuntian Xu
1
ORCID: ORCID
Deying Xu
1
Guoqiang Ma
1
Han Han
2
Xu Zong
3

  1. School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan, Liaoning, China
  2. College of Science – Computer Science, University of Arizona, Tucson, Arizona, USA
  3. Angang Steel Co. LTD, Anshan Iron & Steel, Anshan, Liaoning, China
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Abstract

Analytical design of the PID-type controllers for linear plants based on the magnitude optimum criterion usually results in very good control quality and can be applied directly for high-order linear models with dead time, without need of any model reduction. This paper brings an analysis of properties of this tuning method in the case of the PI controller, which shows that it guarantees closed-loop stability and a large stability margin for stable linear plants without zeros, although there are limitations in the case of oscillating plants. In spite of the fact that the magnitude optimum criterion prescribes the closed-loop response only for low frequencies and the stability margin requirements are not explicitly included in the design objective, it reveals that proper open-loop behavior in the middle and high frequency ranges, decisive for the closed-loop stability and robustness, is ensured automatically for the considered class of linear systems if all damping ratios corresponding to poles of the plant transfer function without the dead-time term are sufficiently high.
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Authors and Affiliations

Jan Cvejn
1

  1. University of Pardubice, Faculty of Electrical Engineering and Informatics, Studentska 95, 532 10 Pardubice, Czech Republic
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Abstract

The proportional-integral-derivative (PID) controller is widely used in various industrial applications such as process control, motor drives, magnetic and optical memory, automotive, flight control and instrumentation. PID tuning refers to the generation of PID parameters (Kp, Ki, Kd) to obtain the optimum fitness value for any system. The determination of the PID parameters is essential for any system that relies on it to function in a stable mode. This paper proposes a method in designing a predictive PID controller system using particle swarm optimization (PSO) algorithm for direct current (DC) motor application. Extensive numerical simulations have been done using the Mathwork’s Matlab simulation environment. In order to gain full benefits from the PSO algorithm, the PSO parameters such as inertia weight, iteration number, acceleration constant and particle number need to be carefully adjusted and determined. Therefore, the first investigation of this study is to present a comparative analysis between two important PSO parameters; inertia weight and number of iteration, to assist the predictive PID controller design. Simulation results show that inertia weight of 0.9 and iteration number 100 provide a good fitness achievement with low overshoot and fast rise and settling time. Next, a comparison between the performance of the DC motor with PID-PSO, with PID of gain 1, and without PID were also discussed. From the analysis, it can be concluded that by tuning the PID parameters using PSO method, the best gain in performance may be found. Finally, when comparing between the PID-PSO and its counterpart, the PI-PSO, the PID-PSO controller gives better performance in terms of robustness, low overshoot (0.005%), low minimum rise time (0.2806 seconds) and low settling time (0.4326 seconds).

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Authors and Affiliations

Norhaida Mustafa
Fazida Hanim Hashim
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Abstract

The seawater desalination process is emerging as a substantial source of fresh water by removing salt and minerals from an infinite supply of seawater effectively. The first stage in a desalination plant is the use of chlorine gas to sterilize the microorganisms in the water. During excess chlorine leakage, an alert is activated, employees are relocated away from the site for a specific period, and dampers will be manually opened. This will cause unsafe working conditions and a waste of time. To overcome this problem, this paper proposes a coefficient diagram method based proportional integral derivative (CDM-PID) control strategy for the tune the control parameter with the distributed control system (DCS) interfaced conical tank. During operation, a 10% NaOH solution is injected into the top of the scrubber column using an ethylene-ter-polymer (ETA) designed distributor to ensure that the solution is evenly distributed across the packing surface. The three control strategies are compared to tune the control parameter with the DCS interfaced conical tank. Instead of the sodium hydroxide tank in the chlorine scrubber system, this work presents the pilot plant of DCS interfaced with two conical tank interacting systems with different liquid level heights. Here, the proposed CDM-PID controller is compared with the standard Ziegler-Nichols (ZN)-ultimate cycling method, and the internal model control (IMC) method. The results demonstrated that the proposed CDM-PID approach is superior to existing approaches in terms of low oscillation, settling period, and high robustness.
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Authors and Affiliations

T. Maris Murugan
1
ORCID: ORCID
T.R. Kiruba Shankar
2

  1. Erode Sengunthar Engineering College, Department of Electronics and Instrumentation Engineering, Perundurai, Erode, Tamil Nadu, 638 057, India
  2. KPR Institute of Engineering and Technology, Department of Electronics and Communication Engineering, Coimbatore, Tamil Nadu, 641 407, India
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Abstract

Robots that can comprehend and navigate their surroundings independently on their own are considered intelligent mobile robots (MR). Using a sophisticated set of controllers, artificial intelligence (AI), deep learning (DL), machine learning (ML), sensors, and computation for navigation, MR's can understand and navigate around their environments without even being connected to a cabled source of power. Mobility and intelligence are fundamental drivers of autonomous robots that are intended for their planned operations. They are becoming popular in a variety of fields, including business, industry, healthcare, education, government, agriculture, military operations, and even domestic settings, to optimize everyday activities. We describe different controllers, including proportional integral derivative (PID) controllers, model predictive controllers (MPCs), fuzzy logic controllers (FLCs), and reinforcement learning controllers used in robotics science. The main objective of this article is to demonstrate a comprehensive idea and basic working principle of controllers utilized by mobile robots (MR) for navigation. This work thoroughly investigates several available books and literature to provide a better understanding of the navigation strategies taken by MR. Future research trends and possible challenges to optimizing the MR navigation system are also discussed.
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Authors and Affiliations

Ravi Raj
1
ORCID: ORCID
Andrzej Kos
1

  1. Faculty of Computer Science, Electronics, and Telecommunications, AGH University of Science and Technology, Krakow, Poland

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