Modelling and Optimization of Nonlinear Proportional Integral Controller

Mr. Sharad Chandra Srivastava; Prof. Toran Verma

doi:https://doi.org/10.51976/jfsa.221904

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Modelling and Optimization of Nonlinear Proportional Integral Controller

Sharad Chandra Srivastava, Toran Verma

https://doi.org/10.51976/jfsa.221904

Author Details ( * ) denotes Corresponding author

1. Sharad Chandra Srivastava, Research Scholar, Department of Mechanical Engineering, Sam Higginbottom University of Agriculture and Sciences, Prayagraj, Uttar Pradesh, India (sharad.ucer@gmail.com)

2. * Toran Verma, Professor, CMR Engineering College, Hyderabad, Telengana, India (toranverma.003@gmail.com)

The NPIC (Nonlinear proportional integral Controller) has been studied in this research, and a genetic method has been used to find the smallest possible error. Integral absolute error was used to develop the objective function (IAE)A nonlinear component is included in this controller, making it one of a kind in its mix of proportional and integral control. For non-linear robotic manipulators, this means that the controller may be far more effective than a linear controller, which has historically been difficult to operate. Consequently, this controller offers a nonlinear controller for manipulators. Programming controller parameters to achieve high trajectory tracking has always been a difficult and time-consuming task for engineers, thus developing a system that can handle non-linearity and complexity has become more difficult in the next year. An intelligent controller is needed to meet today's demands It is the goal of this review article to provide an in-depth analysis of different controllers and control approaches as well as optimization strategies.

Keywords

Modelling; Objective Function; Optimization; Absolute Error; Integral Absolute Error

X.S. Yang, & A.H. Gandomi, Bat-algorithm: “a novel approach for global engineering optimization. Engineering Computations,”Vol. 29 No. 5, pp. 464-483,July 2012.
Y. Ohtani, & T. Yoshimura, “Fuzzy control of a manipulator using the concept of sliding mode,”International Journal of Systems Science, Vol 27, Issue 2, May 1996.
A. Hazzab, I.K. Bousserhane, M. Zerbo, & P. Sicard, “Real-time implementation of fuzzy gain scheduling of PI controller for induction motor machine control,” Neural Processing Letters, 24 (2006) 203-215.
Pan, Huihui, et al. "Adaptive tracking control for active suspension systems with non-ideal actuators." Journal of Sound and Vibration 399 (2017): 2-20.
F. Lin. (2007) “Robust control design: An optimal control approach, “John Wiley & Sons Ltd., England.
George Thuruthel, Thomas, et al. "Control strategies for soft robotic manipulators: A survey." Soft robotics 5.2 (2018): 149-163.
Zhang, Shuang, et al. "Adaptive neural control for robotic manipulators with output constraints and uncertainties." IEEE transactions on neural networks and learning systems 29.11 (2018): 5554-5564.
Xiao, Bing, Shen Yin, and OkyayKaynak. "Tracking control of robotic manipulators with uncertain kinematics and dynamics." IEEE Transactions on Industrial Electronics 63.10 (2016): 6439-6449.
Khairudin, Mohammad, Zaharuddin Mohamed, and Abdul Rashid Husain. "Dynamic model and robust control of flexible link robot manipulator." Telkomnika 9.2 (2011): 279.
Rus, Daniela, and Michael T. Tolley. "Design, fabrication and control of soft robots." Nature 521.7553 (2015): 467-475
Good, M. C., L. M. Sweet, and K. L. Strobel. "Dynamic models for control system design of integrated robot and drive systems." (1985): 53-59.
Gravagne, Ian A., Christopher D. Rahn, and Ian D. Walker. "Large deflection dynamics and control for planar continuum robots." IEEE/ASME transactions on mechatronics 8.2 (2003): 299-307.
Xiao, Lin, et al. "Design, verification and robotic application of a novel recurrent neural network for computing dynamic Sylvester equation." Neural networks 105 (2018): 185-196.