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Sadhana Vol. 31, Part 5, October 2006, pp. 543556. Printed in India Effect of bulk modulus on performance of a hydrostatic transmission control system ALI VOLKAN AKKAYA Yildiz Technical University, Mechanical Engineering Department, 34349, Besiktas, Istanbul, Turkey e-mail: aakkayayildiz.edu.tr MS received 9 September 2005; revised 20 February 2006 Abstract. In this paper, we examine the performance of PID (proportional integral derivative) and fuzzy controllers on the angular velocity of a hydrostatic transmission system by means of Matlab-Simulink. A very novel aspect is that it includes the analysis of the effect of bulk modulus on system control. Simulation results demonstrates that bulk modulus should be considered as a variable parameter to obtain a more realistic model. Additionally, a PID controller is insufficient in presence of variable bulk modulus, whereas a fuzzy controller provides robust angular velocity control. Keywords. Hydrostatic transmission; bulk modulus; PID (proportional integral derivative); fuzzy controller. 1. Introduction Hydrostatic transmission (HST) systems are widely recognized as an excellent means of power transmission when variable output velocity is required in engineering applications, especially in field of manufacturing, automation and heavy duty vehicles. They offer fast response, maintain precise velocity under varying loads and allow improved energy efficiency and power variability (Dasgupta 2000; Kugi et al 2000). A basic hydrostatic transmission is an entire hydraulic system. Generally, it contains a variable-displacement pump driven by an induction motor, a fixed or variable displacement motor, and all required controls in one simple package. By regulating the displacement of the pump and/or motor, a continuously variable velocity can be achieved (Wu et al 2004). Manufacturers and researchers continue to improve the performance and reduce the cost of hydrostatic systems. Especially, modelling and control studies of hydrostatic transmission systems have attracted considerable attention in recent decades. Some studies on this topic can be found in the literature (Huhtala 1996; Manring Dasgupta 2000; Kugi et al 2000; Dasgupta et al 2005). Various rotational velocity control algorithms for hydrostatic sys- tems are developed and applied by Lennevi Watton 1989). Due to temperature variations and air entrapment, the bulk modulus may vary during the operation of the hydraulic sys- tems (Eryilmaz Tan Prasetiawan 2001). In particular, the bulk modulus ought to be regarded as a source of significant nonlinearity for this type of controller. Thus, the controller has to be very robust to account for such wide variation. Use of knowledge-based systems in process control is increasing, especially in the fields of fuzzy control (Tanaka 1996). Unlike classical control methods, the fuzzy controller is designed with linguistic terms to cope with the nonlineari- ties. Therefore, this control method is also applied to judge its capacity to reduce the adverse effect of variable bulk modulus. 3.1 PID control The structure of the PID control algorithm used for the angular velocity control of HST system is given in (17) and (18) below. Ziegler-Nichols method is implemented for tuning control parameters, such as proportional gain (K p ), derivative time constant ( d ) and integral time constant ( i ) (Ogata 1990). After fine adjustments, the optimal control parameters are Effect of bulk modulus on performance of a transmission control system 549 Figure 3. Simulink model of HST system for PID control. determined for the reference angular velocity. Figure 3 shows the Simulink model of the PID-controlled HST system. uv(t) = K p e(t) + K p d de(t) dt + K p i integraldisplay e(t) dt, (17) e(t) = r . (18) 3.2 Fuzzy control Fuzzy logic has come a long way since it was first presented to technical society, when Zadeh (1965) first published his seminal work. Since then, the subject has been the focus of many independent research investigations. The attention currently being paid to fuzzy logic is most likely the result of present popular consumer products employing fuzzy logic. The advantages of this method are its applicability to nonlinear systems, simplicity, good performance and robust character. These days, this method is being applied to engineer- ing control systems such as robot control, flight control, motor control and power systems successfully. In fuzzy control, linguistic descriptions of human expertise in controlling a process are represented as fuzzy rules or relations. This knowledge base is used by an inference mecha- nism, in conjunction with some knowledge of the states of the process in order to determine control actions. Unlike the conventional controller, there are three procedures involved in the implementation of a fuzzy controller: fuzzification of inputs, and fuzzy inference based on the knowledge and the defuzzification of the rule-based control signal. The structure of the fuzzy controller is seen in figure 4. An applied fuzzy controller needs two input signals. These signals are error (e) and deriva- tive of error (de) respectively. The usual overlapped triangular fuzzy membership functions are used for two input signals (e, de/dt) and the output signal (u). Figure 5 shows the struc- ture of the membership functions of input and output signals. Input signals are transformed at intervals of 1, 1 by scaling factors which are Ge and Gde. In the fuzzification process, all input signals are expressed as linguistic values which are: NB negative big, NM negative medium, NS-negative small, ZE-zero, PS-positive small, PM-positive medium, PB-positive big. After input signals are converted to fuzzy linguistic variables, these variables are sent to the inference mechanism to create output signals. 550 Ali Volkan Akkaya Figure 4. Structure of a fuzzy controller. The inference process consists of forty nine rules driven by the linguistic values of the input signals. These fuzzy rules written as a rule base are shown given in table 1. The rule base is developed by heuristics with error in motor angular velocity and derivation of error in this velocity. For instance, one of the possible rules is: IF e = PS and de = NB THAN u = NM. This rule can be explained as in the following: If the error is small, angular velocity of hydraulic motor is around the reference velocity. Significantly big negative value of derivation of error shows that the motor velocity is rapidly approaching the reference position. Consequently, controller output should be negative middle to prevent overshoot and to create a brake effect. As a rule-inference method, the Mamdani Method is selected because of its general acceptance (Tanaka 1996). Defuzzification transforms the control linguistic variables into the exact control output. In defuzzification, the method of centre of gravity is implemented (Tanaka 1996), as u = n summationdisplay i=1 W i B i / n summationdisplay i=1 W i (19) Figure 5. Triangular fuzzy member- ship functions, (a)e input signal, (b) de input signal, (c) u output signals. Effect of bulk modulus on performance of a transmission control system 551 Table 1. Rule base for fuzzy control. dee NB NM NS ZE PS PM PB NB NB NB NB NM NM NS ZE NM NB NB NM NS NS ZE PS NS NB NM NS NS ZE PS PM ZE NM NS NS ZE PS PS PM PS NM NS ZE PS PS PM PB PM NS ZE PS PS PM PB PB PB ZE PS PM PM PB PB PB where, u is the output signal of the fuzzy controller, W i is the degree of the firing of the ith rule, B i is the centroid of the consequent fuzzy subset of ith rule. Real values of control output signal (uv) are determined by the scaling factor of Guv. As a result, the fuzzy controller built-in Fuzzy Logic Toolbox of the Matlab program has been added to the Simulink model of hydrostatic transmission system for simulation analysis (figure 6). 4. Simulation results and discussion The validity of the influence of bulk modulus dynamics on HST control system has been tested in computer simulations. In order to carry out simulation, some physical and simulation parameters corresponding to HST system are taken from work of McCloy B i centroid of the consequent fuzzy subset of ith rule; HST hydrostatic transmission; I m inertia of hydraulic motor shaft Nms 2 ; k p pump coefficient m 3 / s; k m hydraulic motor coefficient m 3 ; k mt motor torque coefficient m 3 ; k v slope coefficient of static characteristic of relief valve m 5 /Ns; M B moments resulted from friction force Nm; M I moments resulted from load inertia Nm; M m hydraulic motor torque Nm; M o moments resulted from machine operation Nm; P system pressure Pa; P v opening pressure value of valve Pa; Q c flow rate deal with compressibility m 3 /s; Q m flow rate used in hydraulic motor m 3 /s; Q p flow rate of pump m 3 /s; Q v passing flow rate through relief valve m 3 /s; uv output signal of fuzzy controller; V fluid volume subjected to pressure effect m 3 ; W i degree of firing of ith fuzzy rule; displacement angle of swashplate ; bulk modulus Pa; mm mechanical efficiency of hydraulic motor ; vp pump volumetric efficiency ; vm volumetric efficiency of motor ; angular velocity of motor 1/s; Delta1P m pressure drop in hydraulic motor Pa. 556 Ali Volkan Akkaya References Dasgupta K 2000 Analysis of a hydrostatic transmission system using low speed high torque motor. 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