Position Control of Hydraulic Servo System Using PID Controller Tuned by Some Evolutionary Techniques

Position Control of Hydraulic Servo System Using PID Controller Tuned by Some Evolutionary Techniques

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This paper presents Particle Swarm Optimization (PSO) algorithm, Adaptive Weighted PSO (AWPSO) algorithm and Genetic Algorithm (GA) for determining the optimal Proportional-Integral-Derivative (PID) controller’s parameters of a position control for a typical Hydraulic Servo System (HSS). The performance indices, which have been used in the optimization, are Integral Absolute Error (IAE), Integral Square Error (ISE) and Integral Time Absolute Error (ITAE). The proposed controller is implemented on a simulation model of a real time hydraulic system to know the best method for tuning the controller. The PSO method yields better tuning in terms of settling time, maximum overshoot and undershoot. Compared to GA and AWPSO results, the PSO method has been noticed to be more efficient and robust in improving the step response of a position control for hydraulic systems. A mathematical model of a hydraulic servo-system is presented. This model includes the most relevant dynamic and non-linear effects. The model describes the behavior of a REXROTH servo valve and includes the nonlinearities of friction forces, valve dynamics, oil compressibility and load influence.
Hydraulic Servo Systems (HSS) play an important role in industrial field because they can produce high torque and large forces with high speeds. The hydraulic servo system applications include, manipulators, material test machines, fatigue testing, paper machines, ships, injection molding machines, robotics, and aircraft field. The dynamics of hydraulic systems are highly nonlinear due to directional change of valve opening, friction, etc. (Sohl and Bobrow, 1999). In hydraulic control systems, one of the main purposes of control is to achieve a desired and satisfactory response f...

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...g,” Computers and Chemical Engineering 26.6: 903-908
Saad, M. S., Jamaluddin, H., and Darus, I. Z. M., 2012, “Implementation of PID controller tuning using differential evolution and genetic algorithms,” International Journal of Innovative Computing Information And Control, 8(11), 7761-7779.
Sirouspour, Mohammad R., and S. E. Salcudean, 2000, “On the nonlinear control of hydraulic servo-systems,” InProceedings of the IEEE International Conference on Robotics and Automation ICRA'00. Vol. 2.
Sivanandam, S. N., Visalakshi, P., and Bhuvaneswari, A., 2007. Multiprocessor Scheduling Using Hybrid Particle Swarm Optimization with Dynamically Varying Inertia. IJCSA, 4(3), pp. 95-106.
Sohl, Garett A., and James E. Bobrow, (1999), “Experiments and simulations on the nonlinear control of a hydraulic servo system,” IEEE Transactions onControl Systems Technology, 7(2), pp. 238-247

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