Design and Optimization of PID Controller using Various Algorithms for Micro-Robotics System

Ehab Seif Ghith, Farid Adel Aziz Tolba

Abstract


Microparticles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. This paper attempts to provide a thorough comparison between five meta-heuristic search algorithms:  Sparrow Search Algorithm (SSA), Flower Pollination Algorithm (FPA), Slime Mould Algorithm (SMA), Marine Predator Algorithm (MPA), and Multi-Verse Optimizer (MVO). These approaches were used to calculate the PID controller optimal indicators with the application of different functions, including Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). Every method of controlling was presented in a MATLAB Simulink numerical model, and LABVIEW software was used to run the experimental tests. It is observed that the MPA technique achieves the highest values of settling error for both simulation and experimental results among other control approaches, while the SSA approach reduces the settling error by 50% compared to former experiments. The results indicate that SSA is the best method among all approaches and that ISTES is the best choice of PID for optimizing the controlling parameters.

Keywords


Sparrow Search Algorithm; Flower Pollination Algorithm; Slime Mould Algorithm; Marine Predator Algorithm, Multi-Verse Optimizer; Minimally invasive surgery; PID controller.

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References


J. Keuning. Image-based magnetic control of Microparticles, Master's thesis, University of Twente, 2011.

J. D. Keuning, J. de Vriesy, L. Abelmanny, and S. Misra,"Image-based magnetic control of paramagnetic microparticles in water," In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 421-426, 2011.

R. Farag, I. Badawy, F. Magdy, Z. Mahmoud, and M. Sallam, "Real-Time Trajectory Control of Potential Drug Carrier Using Pantograph “Experimental Study”," In International Conference on Advanced Intelligent Systems and Informatics, pp. 305-313, 2020.

J. Karl Astrom and T. Hagglund, PID Controllers: theory, design, and tuning, 2nd ed.: NC: Instrument Society of America, 1995.

M. Tamer, PID Controller Implementation and Tuning, Published by InTech, 2011.

K. Ogata, Modern control engineering, Prentice-Hall, 2010.

T. Hägglund and K. J. Åström. “Revisiting the Ziegler‐Nichols tuning rules for PI control,” Asian J. Control, vol. 4, no. 4, pp. 364-380, 2002.

W. Tan, J. Liu, T. Chen, and H. J. Marquez, “Comparison of some well-known PID tuning formulas,” Comput. Chem. Eng., vol. 39, no. 9, pp. 1416-1423, 2006.

A. Visioli, “Research trends for PID controllers,” Acta Polytech., vol. 52, no. 5, 2012.

D. Valériom, and J. S. Da Costa, “Tuning of fractional PID controllers with Ziegler–Nichols-type rules,” Signal Process, vol. 86, no. 10, pp. 2771-2784, 2006.

D. K. Maghade, and B. M. Patre, “Pole placement by PID controllers to achieve time domain specifications for TITO systems,” Trans. Inst. Meas. Control., vol. 36, no. 4, pp. 506-522, 2014.

Z. Bingul, and O. Karahan, "Comparison of PID and FOPID controllers tuned by PSO and ABC algorithms for unstable and integrating systems with time delay," Optim. Control Appl. Methods, vol. 39, no. 4, 1431-1450, 2018.

D.E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine learning,” 5th edition Pearson Education, 2002.

J. Kennedy, and R. Eberhart, "Particle swarm optimization," Proc. IEEE Int. Conf. on Neural Network, vol. 4, pp.1942 – 1948, 1995.

R. Martínez-Soto, O. Castillo, and L. T. Aguilar, “Type-1and type-2 fuzzy logic controller design using a hybrid PSO-GA optimization method,” Inf. Sci., vol. 285, pp. 35-49, 2014.

M. A. Şen, V. Bakırcıoğlu, and M. Kalyoncu, "Performances comparison of the bees algorithm and genetic algorithm for PID controller tuning," Proceedings of the 5th International Conference on Mechatronics and Control Engineering, pp. 126-130, 2016.

D. T. Pham and M. Castellani, "Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimization Algorithms," Soft Computing - A Fusion of Foundations, Methodologies, and Applications, vol. 18, no. 5, pp. 871-903, 2013.

P. Sharma and R. Gupta, "Tuning of PID controller for a linear BLDC motor using TLBO technique," 2014 International Conference on Computational Intelligence and Communication Networks, pp. 1224-1228, 2014.

D. Manjarres, I. Landa-Torres, S. Gil-Lopez, J. Del Ser, M.N. Bilbao, S. Salcedo-Sanz, and Z. W. Geem, "A survey on applications of the harmony search algorithm," Eng. Appl. Artif. Intell., vol. 26, no. 8, pp. 1818-1831, 2013.

Q. V. Pham, S. Mirjalili, N. Kumar, M. Alazab, and W. J. Hwang, "Whale optimization algorithm with applications to resource allocation in wireless networks," IEEE Trans. Veh. Technol., vol. 69, no. 4, 4285-4297, 2020.

M. Bhuyan, D. C. Das, A. K. Barik, "A comparative analysis of DSM-based autonomous hybrid microgrid using PSO and SCA," 2019 IEEE Region 10 Symposium (TENSYMP), pp. 765-770, 2019.

M. Misaghi, and M. Yaghoobi, "Improved invasive weed optimization algorithm (IWO) based on chaos theory for the optimal design of PID controller," J. Comput. Des. Eng., vol. 6, no. 3, pp. 284-295, 2019.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Adv. Eng. Softw., vol. 69, pp. 46-61, 2014.

S. M. Mirjalili, S. Z. Mirjalili, S. Saremi, and S. Mirjalili, "Sine cosine algorithm: theory, literature review, and application in designing bend photonic crystal waveguides," Nature-inspired, pp. 201-217, 2020.

R. Pradhan, S. K. Majhi, and B. B. Pati, "Design of PID controller for automatic voltage regulator system using Ant Lion Optimizer," World J. Eng., vol. 15, no. 3, pp. 373-387, 2018.

E. S. Ghith, F. A Tolba, and S. A. Hammad, "Real-Time Implementation of Tuning PID Controller Based on Sine Cosine Algorithm for Micro-robotics System," International Conference on Digital Technologies and Applications, pp. 801-811, 2022.

E. S. Ghith and F. A. A. Tolba, "Real-Time Implementation of Tuning PID Controller Based on Whale Optimization Algorithm for Micro-robotics System," 2022 14th International Conference on Computer and Automation Engineering (ICCAE), pp.103-109, 2022.

M. H. M. Ali, M. M. S. Mohamed, N. M. Ahmed, and M. B. A. Zahran"Comparison between P&O and SSO techniques based MPPT algorithm for photovoltaic systems," International Journal of Electrical & Computer Engineering, vol. 12, no. 1, pp. 32-40, 2022.

K. G. Abdulhussein, N. M Yasin, and I. J. Hasan. "Comparison of cascade P-PI controller tuning methods for PMDC motor based on intelligence techniques," International Journal of Electrical & Computer Engineering, vol. 12, no. 1, pp. 1-11, 2022.

P. R. Chinda, and R. D. Rao, "A binary particle swarm optimization approach for power system security enhancement," International Journal of Electrical & Computer Engineering, vol. 12, no. 2, pp. 1929-1936, 2022.

M. K. Alsmadi, M. Alzaqebah, S. Jawarneh, S. Brini, I. Al-Marashdeh, K. Briki, and M. T. Al-Rashdan, "Cuckoo algorithm with great deluge local-search for feature selection problems," International Journal of Electrical & Computer Engineering, vol. 12, no. 4, pp. 4315-4326, 2022.

A. C. O. Reddy, and K. Madhavi, "Manta ray optimized deep contextualized bi-directional long short-term memory based adaptive galactic swarm optimization for complex question answering," International Journal of Electrical & Computer Engineering (2088-8708), vol. 12, no. 4, pp. 3994-4006, 2022.

W. Aribowo, and B. S. Supari, "Optimization of PID parameters for controlling DC motor based on the aquila optimizer algorithm," International Journal of Power Electronics and Drive Systems (IJPEDS), vol. 13, no. 1, pp. 808-2814, 2022.

K. J. Astrom and T. Hagglund, PID Controllers: theory, design, and tuning, 2nd ed.: NC: Instrument Society of America, 1995.

M. M. Eissa, G. S.Virk, A. M. AbdelGhany, E. S. Ghith, "Optimum Induction Motor Speed Control Technique using Genetic Algorithm," American Journal of Intelligent Systems (AJIS), vol. 3, 2013.

M. M. Eissa, G. S.Virk, A. M. AbdelGhany, E. S. Ghith, "Optimum Induction Motor Speed Control Technique using Particle Swarm Optimization," International Journal of Energy Engineering (IJEE) vol. 3, no.2, March 2013, California, USA.

M. Sallam, I. Saif, Z. Saeed, and M. Fanni, "Lyapunov-based control of a teleoperation system in presence of time delay," International Conference on Advanced Intelligent Systems and Informatics, pp. 759-768. Springer, Cham, 2020.

X. Zhang, and D. Yuan."A niche ant colony algorithm for parameter identification of space fractional order diffusion equation," IAENG International Journal of Applied Mathematics, vol. 47, no. 2, pp. 197-208, 2017.

Z. Tian, G. Wang, andY. Ren. "AMOAIA: adaptive multi-objective optimization artificial immune algorithm," IAENG International Journal of Applied Mathematics, vol. 49, no. 1, pp. 1-8, 2019.

J. Wang, "A novel firefly algorithm for portfolio optimization problem,” IAENG International Journal of Applied Mathematics, vol. 49, no. 1, pp. 1-6, 2019.

W. C. Ho, J. M Su, and C. Y. Chang, "Maximal Market Potential of Feeder Bus Route Design Using Particle Swarm Optimization," IAENG International Journal of Applied Mathematics, vol. 49, no. 4, 2019

X. Zhang, H. Huang, and X. Zhang, "Applying Cuckoo Search Algorithm to Solve Fractional Differential Equation Based on Cubic Spline Function," IAENG International Journal of Applied Mathematics, vol. 50, no. 1, pp. 1-12, 2020.

G. Chen, X. Zhao, Y. Xiang, X. Zeng, and H. Long, "Research on Multi-objective Active Power Optimization Simulation of Novel Improved Whale Optimization Algorithm," IAENG International Journal of Applied Mathematics, vol. 51, no. 3, 2021.

G. Chen, Y. Xiao, F. Long, X. Hu, and H. Long, "An Improved Marine Predators Algorithm for Short-term Hydrothermal Scheduling," IAENG International Journal of Applied Mathematics, vol. 51, no. 4, 2021.

C. F Wang, and Y. H. Zhang, "An improved artificial bee colony algorithm for solving optimization problems," IAENG International Journal of Computer Science, vol. 43, no. 3, pp. 336-343, 2016.

G. Chen, S. Qiu, Z. Zhang, and Z. Sun, "Quasi-oppositional cuckoo search algorithm for multi-objective optimal power flow," IAENG International Journal of Computer Science, vol. 45, no. 2, pp. 255-266, 2018.

G. Chen, Z. Lu, Z. Zhang, and Z. Sun, "Research on hybrid modified cuckoo search algorithm for optimal reactive power dispatch problem," IAENG International Journal of Computer Science, vol. 45, no. 2, pp. 328-339, 2018.

B. Tutuko, S. Nurmaini, and P. S. Saparudin, "Route optimization of non-holonomic leader-follower control using dynamic particle swarm optimization," IAENG International Journal of Computer Science, vol. 46, no. 1, pp. 1-11, 2019.

G. Chen, J. Qian, Z. Zhang, and Z. Sun, "Multi-objective improved bat algorithm for optimizing fuel cost, emission and active power loss in power system," IAENG International Journal of Computer Science, vol. 46, no. 1, pp. 118-133, 2019.

S. Li, H. Liu, and Y. Du, "An Optimal Resource Allocation Scheme for Elastic Applications in Multipath Networks via Particle Swarm Optimization," IAENG International Journal of Computer Science, vol. 47, no. 2, 2020.

W. Z. Sun, M. Zhang, J. S. Wang, S. S. Guo, M., Wang, and W. K. Hao, "Binary Particle Swarm Optimization Algorithm Based on Z-shaped Probability Transfer Function to Solve 0-1 Knapsack Problem," IAENG International Journal of Computer Science, vol. 48, no. 2, 2021.

X. Shen, X. Zhang, and Z. Yu, "A New Hybrid Cuckoo Search for the Resources-Constrained Project Scheduling Problem," IAENG International Journal of Computer Science, vol. 48, no. 2, 2021.

A. A. Naim, and N. I. Ghali," New Optimization Algorithm Based on Venus Flytrap Plant," IAENG International Journal of Computer Science, vol. 48, no. 3, 2021.

J. Yang, X. Zhang, S. Fu, and Y. Hu, "A Hybrid Harmony Search Algorithm based on Firefly Algorithm and Boltzmann Machine," IAENG International Journal of Computer Science, vol. 49, no. 1, 2022.

J. Xue, and B. Shen, "A novel swarm intelligence optimization approach: sparrow search algorithm," Syst. Sci. Control Eng., vol. 8, no. 1, pp. 22-34, 2020.

D. Yousri, M. Abd Elaziz, and S. Mirjalili, “Fractional-order calculus-based flower pollination algorithm with local search for global optimization and image segmentation,” Knowl Based Syst., vol. 197, p. 105889, 2020.

S. Li, H. Chen, M.Wang, A. A. Heidari, and S. Mirjalili, “Slime mould algorithm: A new method for stochastic optimization,” Future Generation Computer Systems, vol. 111, pp. 300-323, 2020.

A. Yakout, W. Sabry, and H. M. Hasanien, “Enhancing rotor angle stability of power systems using marine predator algorithm-based cascaded PID control,” Ain Shams Eng. J., vol. 12, no. 2, pp. 1849-1857, 2021.

M. A. Sobhy, A. Y. Abdelaziz, H. M. Hasanien, M. Ezzat, “Marine predators algorithm for load frequency control of modern interconnected power systems including renewable energy sources and energy storage units,” Ain Shams Eng. J., vol. 12, no. 4, pp. 3843-3857, 2021.

A. Singh, and S. Suhag, “Frequency regulation in an AC microgrid interconnected with a thermal system employing multiverse-optimized fractional order-PID controller,” Int. J. Sustain. Energy, vol. 39, no. 3, pp. 250-262, 2020.




DOI: https://doi.org/10.18196/jrc.v3i3.14827

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