Abstract
This control strategy can be quite handful in practical situations where constant rpm is the primary need, especially in case of heavy drilling machines/automobiles being employed in drilling large stone quarry in mines. In such a situation, the drilling needs to be performed at a predetermined speed only and major fluctuations in the speed can lead to undesirable results. To realize the same control strategy in real terms, the physical model of a closed loop HST (Hydrostatic Transmission system) system consisting of a variable displacement pump and a fixed displacement motor has been developed. Conventionally, the rpm of the motor decrease upon loading the pump as load pressure increases. This can severely affect the performance of the overall system as it is assumed that the system works excellently or is designed for certain range of rpm which should be more or less constant. Hence in view of the situation, a feedback control mechanism is applied via means of PID (Proportional, Integral and Derivative) controller which keeps track of the decline in rpm of the motor and through certain control strategy sends a command signal to the pump. Based on the received feedback signal, the inclination of the swash plate of the pump is varied resulting in the increase of the flow rate from the pump which consequently increments the rpm of the motor to match the demand requirements resulting in the constant rpm. Furthermore, an advanced PID controller constructed on the basis of neural network following has the capability of approximating nonlinearities. The devised controller has been found to be quite effective during simulations and has edge in regulating the parameters, better robustness and minimizing nonlinearities, fluctuations and errors. Performance indicators such as correlation coefficient (R), variance and root mean square error (RMSE) are computed for the model under multiple regression analysis.
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References
Aoyama A, Doyle FJ, Venkatasubramanian V (1996) Control-affline neural network approach for non minimum-phase nonlinear process control. J Process Control 6(1):17–26
Çetin Ş, Akkaya AV (2010) Simulation and hybrid fuzzy-PID control for positioning of a hydraulic system. Nonlinear Dyn 61(3):465–476
Chang WD (2013) Nonlinear CSTR control system design using an artificial bee colony algorithm. Simul Model Pract Theory 31:1–9
Chen YM, He YL, Zhou MF (2015) Decentralized PID neural network control for a quad rotor helicopter subjected to wind disturbance. J Central South Univ 22:168–179
Cong S, Liang Y (2009) PID-like neural network nonlinear adaptive control for uncertain multivariable motion control systems. IEEE Trans Industr Electron 56(10):3872–3879
Costopoulus T (1992) Hydraulics and pneumatics. Symeon, Athens
Das J, Mishra SK, Saha R, Mookherjee S, Sanyal D (2017) Nonlinear modeling and PID control through experimental characterization for an electrohydraulic actuation system: system characterization with validation. J Braz Soc Mech Sci Eng 39(4):1177–1187
Elbayomy KM, Zongxia J, Huaqing Z (2008) PID controller optimization by GA and its performances on the electro-hydraulic servo control system. Chin J Aeronaut 21(4):378–384
Gharbi R, Karkoub M, ElKamel A (1995) An artificial neural network for the prediction of immiscible flood performance. Energy Fuels 9(5):894–900
Giacomino A, Abollino O, Malandrino M, Mentasti E (2011) The role of chemometrics in single and sequential extraction assays: a review Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques. Analytica Chimica Acta 688(2):122–139
Guo B, Liu H, Luo Z, Wang F (2009) Adaptive PID controller based on BP neural network. In: International joint conference on artificial intelligence, 2009 (JCAI'09). IEEE, pp 148–150
Harrell FE (2001) Regression modeling strategies: with applications to linear models. Survival Analysis and logistic regression. Springer-Verlag, New York
Hassan MY, Kothapalli G (2012) Interval Type-2 fuzzy position control of electro-hydraulic actuated robotic excavator. Int J Min Sci Technol 22(3):437–445
Ho SJ, Shu LS, Ho SY (2006) Optimizing fuzzy neural networks for tuning PID controllers using an orthogonal simulated annealing algorithm OSA. IEEE Trans Fuzzy Syst 14(3):421–434
Jia CY, Shan XY, Cui YC, Tao BAI, Cui FJ (2013) Modeling and simulation of hydraulic roll bending system based on CMAC neural network and PID coupling control strategy. Int J Iron Steel Res 20(10):17–22
Jung JW, Leu VQ, Do TD, Kim EK, Choi HH (2015) Adaptive PID speed control design for permanent magnet synchronous motor drives. IEEE Trans Power Electron 30(2):900–908
Kaddissi C, Kenne JP, Saad M (2007) Identification and real-time control of an electrohydraulic servo system based on nonlinear backstepping. IEEE/ASME Trans Mechatron 12(1):12–22
Kaliafetis P, Costopoulos T (1995) Modelling and simulation of an axial piston variable displacement pump with pressure control. Mech Mach Theory 30(4):599–661
Kalyoncu M, Haydim M (2009) Mathematical modelling and fuzzy logic-based position control of an electrohydraulic servosystem with internal leakage. Mechatronics 19(6):847–858
Kang J, Meng W, Abraham A, Liu H (2014) An adaptive PID neural network for complex nonlinear system control. Neurocomputing 135:79–85
Karkoub M, Elkamel A (1997) Modeling pressure distribution in a rectangular gas bearing using neural networks. Tribol Int 30(2):139–150
Karkoub MA, Gad OE, Rabie MG (1999) Predicting axial piston pump performance using neural networks. Mech Mach Theory 34(8):1211–1226
Knohl T, Unbehauen H (2000) Adaptive position control of electrohydraulic servo systems using ANN. Mechatronics 10(1):127–143
Kumar V, Gaur P, Mittal AP (2014) ANN based self-tuned PID like adaptive controller design for high performance PMSM position control. Expert Syst Appl 41(17):7995–8002
Li CJ, Lilai Y, Chbat NW (1995) Powell's method applied to learning neural network control of three unknown dynamic systems. Optimal Control Appl Methods 16(4):251–262
Liu Z, Gao Q, Niu H (2014) The research on the position control of the hydraulic cylinder based on the compound algorithm of fuzzy & feed forward-feedback. Sensors Transducers 162(1):314–324
Ogata K (2003) Modern control engineering, 4th edn. Prentice Hall
Panda MN, Zaucha DE, Perez G, Chopra AK (1996) Application of neural networks to modeling fluid contacts in Prudhoe Bay. SPE J 1(3):303–312
Rexroth M (1986) The hydraulic trainer volume 2- proportional and servo valve technology
Skoczowski S, Domek S, Pietrusewicz K, Broel-Plater B (2005) A method for improving the robustness of PID control. IEEE Trans Industr Electron 52(6):1669–1676
Taormina R, Chau KW, Sethi R (2012) Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Eng Appl Artif Intell 25(8):1670–1676
Wu CL, Chau KW, Li YS (2009) Methods to improve neural network performance in daily flows prediction. J Hydrol 372(1):80–93
Zhao Y, Du X, Xia G, Wu L (2015) A novel algorithm for wavelet neural networks with application to enhanced PID controller design. Neuro Computing 158:257–267
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Mishra, S.K., Singh, P.K. (2022). Modeling of a Closed Loop Hydrostatic Transmission System and Its Control Designed for Automotive Applications. In: Kumar, V., Agarwal, A.K., Jena, A., Upadhyay, R.K. (eds) Advances in Engine Tribology. Energy, Environment, and Sustainability. Springer, Singapore. https://doi.org/10.1007/978-981-16-8337-4_11
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