### PID optimal control to reduce energy consumption in DC-drive system

#### Abstract

The control system that is widely used in industry is PID (Proportional Integral Derivative). Almost 90% of industries still use PID control systems because of its simplicity, applicability, and reliability. However, the weakness of PID is that it takes a long time to tune. PID control with good performance and low energy consumption can be achieved using GA tuning with the appropriate objective function. The contribution of this paper is to propose the implementation of LQR control in the form of PID with GA tuning using LQR objective function. The proposed algorithm was implemented both in the simulation and hardware which is a mini conveyor with a DC motor. The result shows that the proposed algorithm is better in both IAE and energy consumption compared with other PID tuning, Ziegler – Nichols, and GA with IAE objective function. Compared with PID ZN, it has IAE and energy reduction by 2.76 % and 16.07 % respectively. Although its performance is lower than the LQR, it has other advantages that only use fewer sensors. The other advantage of the proposed method is, PID is more familiar using. Therefore, it easy to be implemented in the existing system without a lot of changes.

#### References

A. Faramarzi and K. Sabahi, “Recurrent Fuzzy Neural Network for DC- motor control,” in 2011 Fifth International Conference on Genetic and Evolutionary Computing, 2011, pp. 93–96.

U. K. Bansal and R. Narvey, “Speed Control of DC Motor Using Fuzzy PID Controller,” Adv. Electron. Electr. Eng., vol. 3, no. 9, pp. 1209–1220, 2013.

C. A. Kumar, B. R. Harijan, M. K. Kumar, and M. Bharathi, “BLDC Motor Speed Control using Fuzzy Logic PID Controller and Comparing It With PI Controller,” Int. J. Eng. Adv. Technol., vol. 9, no. 2, pp. 4917–4922, 2019.

A. Hughes and B. Drury, Electric Motor and Drives: Fundamental, Types and Applications, 5th ed. Elsevier, 2019.

J. De Oliveira, A. Nied, M. H. F. Santos, and R. P. Dias, “Study on the Energy Efficiency of Soft Starting of an Induction Motor with Torque Control,” in Advasces in Motor Torque Control, Intech, 2011, pp. 33–46.

S. M. Rakhtala and E. S. Roudbari, “Application of PEM Fuel Cell for Stand-alone Based on a Fuzzy PID Control,” Bull. Electr. Eng. Informatics, vol. 5, no. 1, pp. 45–61, 2016.

K. H. Ang, G. Chong, S. Member, and Y. Li, “PID Control System Analysis , Design , and Technology,” IEEE Trans. Control Syst. Technol., vol. 13, no. 4, pp. 559–576, 2005.

A. Balestrino, A. Caiti, V. Calabró, E. Crisostomi, and A. Landi, “From Basic to Advanced PI Controllers : A Complexity vs . Performance Comparison,” in Advance in PID Control, Intech, 2011, pp. 87–100.

A. Y. Jaen-cuellar, R. de J. Romero-Troncoso, L. Morales-velazquez, and R. A. Osornio-rios, “PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems,” Int. J. Adv. Robot. Syst., vol. 10, pp. 1–14, 2013.

J. M. Herrero, X. Blasco, M. Martinez, and J. V Salcedo, “Optimal PID Tuning with Genetic Algorithm for Non-Linear Process Models,” in 15th Trienial World Congress, 2002, pp. 31–36.

A. A. M. Zahir, S. S. N. Alhady, W. A. F. W. Othman, and M. F. Ahmad, “Genetic Algorithm Optimization of PID Controller for Brushed DC Motor,” Intell. Manuf. Mechatronics, pp. 427–437, 2018.

C. Lin, S. Member, H. Jan, and N. Shieh, “GA-Based Multiobjective PID Control for a Linear Brushless DC Motor,” IEEE/ASME Trans. Mechatronics, vol. 8, no. 1, pp. 56–65, 2003.

B. Nagaraj and N. Murugananth, “A Comparative Study of PID Controller Thning Using GA, EP, PSO and ACO,” in 2010 Int. Conference on Communication Control and Computing Technologies, 2010, pp. 305–313.

U. Ansari, S. Alam, and S. M. un N. Jafri, “Modeling and Control of Three Phase BLDC Motor using PID with Genetic Algorithm,” in 2013 UKSim 13th Interntional Conference on Modelling and Simulation, 2011, pp. 189–194.

V. Vishal, V. Kumar, K. P. S. Rana, and P. Mishra, “Comparative Study of Some Optimization Techniques Applied to DC Motor Control,” in 2014 IEEE International Advance Computing Conference (IACC), 2014, pp. 1342–1347.

M. A. Ibrahim, A. K. Mahmood, and N. S. Sultan, “Optimal PID controller of a brushless DC motor using genetic algorithm,” Int. J. Power Electron. Drive Syst., vol. 10, no. 2, pp. 822–830, 2019.

A. Mirzal, S. Yoshii, and M. Furukawa, “PID Parameters Optimization by Using Genetic Algorithm,” ISTECS J., 2012.

S. K. Suman and V. K. Giri, “Genetic Algorithms Techniques Based Optimal PID Tuning For Speed Control of DC Motor,” Am. J. Eng. Technol. Manag., vol. 1, no. 4, pp. 59–64, 2016.

H. Zhang, Y. Cai, and Y. Chen, “Parameter Optimization of PID Controllers Based on Genetic Algorithm,” in 2010 International Conference on E-Healt Networking, Digital Ecosystem and Technologies, 2010, pp. 47–49.

S. L. Brunton and J. N. Kutz, Data-Driven Science and Engineering, 1st ed. Cambridge University Press, 2019.

H. Maghfiroh, O. Wahyunggoro, A. I. Cahyadi, and S. Praptodiyono, “PID-Hybrid Tuning to Improve Control Performance in Speed Control of DC Motor Base on PLC,” in 3rd ICA, 2013, no. August 2014, pp. 233–238.

A. Abdulameer, M. Sulaiman, M. S. M. Aras, and D. Saleem, “GUI Based Control System Analysis Using PID Controller for Education,” Indones. J. Electr. Eng. Comput. Sci., vol. 3, no. 1, pp. 91–101, 2016.

G. Lin and G. Liu, “Tuning PID Controller Using Adaptive Genetic Algorithms,” in 5th International Conference on Computer Science and Education, 2010, pp. 519–523.

L. Fan and E. M. Joo, “Design for Auto-tuning PID Controller Based on Genetic Algorithms,” in IEEE Conference on Industrial Electronics and Applications, 2009, pp. 1924–1928.

M. Gani, S. Islam, and M. A. Ullah, “Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm,” SN Appl. Sci., vol. 1, no. 8, pp. 1–8, 2019.

L. B. Prasad, B. Tyagi, and H. O. Gupta, “Optimal Control of Nonlinear Inverted Pendulum Dynamical System with Disturbance Input using PID Controller and LQR,” in 2011 IEEE Int.Con. on Control System, Computing and Engineering, 2011, pp. 540–545.

K. Ogata, Modern Control Engineering, 5th ed. New Jersey: Prentice Hall, 2010.

DOI: http://doi.org/10.11591/ijpeds.v11.i4.pp%25p

### Refbacks

- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.