Position Control of Electro Hydraulic Actuator (EHA) using an Iterative Learning Control

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http://dx.doi.org/10.7839/ksfc.2014.11.4.001

반복 학습제어를 이용한 전기유압액추에이터의 위치제어 Position Control of Electro Hydraulic Actuator (EHA)

using an Iterative Learning Control

도안녹치남

¹

․우엔민트리

¹

․박형규

¹

․안경관

^1*

D. N. C. Nam, N. M. Tri, H. G. Park and K. K. Ahn

Received: 26 May. 2014, Revised: 12 Aug. 2014, Accepted: 17 Sep. 2014

Key Words：Learning Control(학습제어), Iterative PID Control(반복PID제어), Embedded Computer(임베디드컴퓨터), Electro–Hydraulic Actuator (EHA)

Abstract: This paper presents the development of a compact position generator to be used for industrial purposes based on a pump controlled Electro-Hydraulic Actuator (EHA), which is closed-loop controlled by an embedded based Iterative PID controller. The controller is designed by combining the PID controller and the iterative learning scheme to perform tracking control for periodically desired references. Control algorithm is implemented on an embedded computer (AD 7011-EVA) which makes the implementation and application in industrial environments easier.

* Corresponding author: [email protected], Tel:052-259-2282 1 School of Mechanical and Automotive Engineering, University of Ulsan, Ulsan, Korea

Copyright Ⓒ 2014, KSFC

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://

creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

기호설명

 : external loading force (N)



_

: desired command (mm)

 : position of cylinder (mm)

 : control signal to the DC motor (V)



^*

: control signal modifier (V)



_

 

_

 

_

: control signal modifier

 : adaptation gain

1. Introduction

Hydraulic systems have played an important role in modern industry due to its durability, high power, and reliability. In most of previous

industrial applications, hydraulic actuators have been operated using valve controlled systems.

Recently, the concept of pump-controlled Electro Hydraulic Actuator (EHA) has been introduced and developed as a power-shift system, which shifts from the high speed of electric motor to high force of hydraulic cylinder. Because of its advantages over conventional systems, the EHA has been developed as commercial products [1-4].

y

M

d

P1 D P2

Q1 Q2

QP2 QP1

Qv4 Qv3

Qv2

Qv1 v1 v2

v3 v4

M F

Fig. 1 Structure of the pump-controlled Electro

Hydraulic Actuator (EHA)

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Motor driver Linear encoder

Embedded AD7011-EVA Load

Cell

Weigh

Computer y

M

Fig. 2 Structure of experimental test rig Consequently, EHAs, as position or force

generators, have been employed to a wide range of application fields such as plastic injection-molding, metal forming technology, aeronautics, etc. The EHA configuration is shown in Fig. 1.

For most industrial applications, especially for assembly lines, hydraulic actuators are usually employed as motion generators for accurate tracking of various periodic or non – periodic trajectories. The control problem of pump–

controlled EHA systems faces challenges of high nonlinearities and uncertainties due to the nonlinear dynamics of cylinder and uncertain fluid parameters. However, simple control algorithms with robust performances are usually preferred for use in industrial environments due to reliability features. Hence, it is important to figure out a simple control algorithm, yet can handle performance of EHA system with high accuracy.

In order to implement control algorithms;

embedded based systems are preferred over the computer based system due to low cost, compactness, and high reliability. However, the development and updating for computer based systems are much simpler. Consequently, there are several trends of developing new generations of embedded computer system which can ensure both the characteristics of embedded system and

computer systems for best implementation and performance.

This paper presents the development of a new position generating system for use in industrial environments. Actuating system is based on the commercial EHA manufactured by Bosch Rexroth and a symmetrical double acting hydraulic cylinder.

In this paper, performance of the conventional PID controller is improved with the iterative learning scheme [5-6] to provide accurate tracking performance for desired periodic references. All the control system is implemented on an embedded computer, named as A&D EVA-7011, which runs on linux operating system with a real-time extension (Xenomai). Advantage of this embedded system is that it allows to be programed directly from Matlab/Simulink environment of a host PC.

This makes the development process become more convenient and maintain robust response for the industrial applications.

The remains of this paper is organized as

follows. In section 2, configuration of experimental

system is described. Section 3 presents the control

algorithm and its implementation procedures for

the embedded computer. Next, experimental results

are carried out to evaluate the effectiveness of the

proposed control method in section 4. Finally, some

conclusions are made in section 5.

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2

3 5 6

8 7 1

4

Fig. 3 Experimental apparatus: 1-control system with AD-EVA 7011; 2-Program computer with Simulink; 3-Pressure transducers; 4-Cylinder; 5-Load ceil; 6- Linear encoder; 7-Attached weight;

8-Bosch Rexroth EHA.

2. Experimental test rig

In this paper, the EHA system is manufactured by Bosch Rexroth, which includes a gear pump, supplement valves system, and a symmetrical double acting cylinder. The whole system is driven by a 24V – 20A DC servo motor. In this configuration, the movement of the cylinder is adjusted directly by the speed of the servo motor.

One linear encoder, two pressure transducers and one load cell are installed to the system to measure the cylinder position, the pressure in two chambers of main EHA and the loading force, respectively. The load simulator part is a gravity loading system which can adjust the loading force easily by changing the attached mass. This is a

simple yet efficient method to simulate the variation of working condition for the EHA system.

The developed controller is implemented on a DSP based system, named as AD7011-EVA [7].

This is an embedded computer run on linux OS with a real-time extension (Xenomai), and allow to be programed directly from Matlab/Simulink, environment of a host PC. Some key features of this embedded computer are the tele-programing via Ethernet environment, Visual Touch Screen for observation purposes, 8 channels Analog I/O and 12 channels Digital I/O, and several communication standards.

The compact size of embedded computer and

flexible programing capabilities together with the

compact EHA system bring advantages to the

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Fig. 4 Simulink diagram of the proposed controller industrial application for this new hydraulic control

system. Schematic diagram of the whole pump–

controlled EHA system is shown as Fig. 2, setting parameters for the EHA system are shown in Table 1, while the apparatus is setup as Fig. 3.

Table 1 Setting parameters for the EHA system Components Parameters Specification

Hydraulic Pump

Displacement 0.97[cc/rev]

Rate rotation speed 3000 [rpm]

Relief pressure 100 [bar]

Hydraulic Cylinder

Tube diameter – D 40 [mm]

Rod diameter – d 22 [mm]

Length of stroke 200 [mm]

Hydraulic oil

Effective bulk

modulus 1.5 x10

⁹

[Pa]

Specific gravity 0.87

Linear encoder

Model MHG-TT

Full range 500 [mm]

Resolution 1[]

3. Control system development

In this paper, the EHA system is closed loop control for various periodic desired trajectories.

The control signal is developed based on the combination of the conventional PID controller with an iterative learning mechanism. The control system is built on Matlab/Simulink environment of a personal computer. Then, the algorithm is

compiled into C code and programmed to the AD EVA7011 directly via Ethernet environment. The whole procedure is discussed by two subsections as follows.

3.1 Control algorithm

Given a bounded desired trajectory 



, the objective of this section is to determine the input speed command for the AC servo motor  to control the output position  track as closed as possible to 



.

Define the tracking error as:

(1)

The control signal modifier in this paper is calculated by the conventional PID controller

(2)

where, 



 

_

 

_

are control gains of the modifier.

Employing a modified Iterative Learning Control (ILC) scheme, the control signal of EHA system in the 

^

iteration is defined as follow:

(3)

(5)

here,  is the adaptation gain, 

   

is the last iteration, and the modifier term of control signal



_^

is calculated by equation (2). It is the control signal of the conventional PID controller.

It is noted that the iterative learning based controller is designed only for periodic desired references. The convergence analysis for this controller for a linear time varying model has been done by Ali Madady in (6). Inspired by this result, we follow the procedure and find optimal controller gain for position tracking purpose of the EHA system.

3.2 Implementation to the AD 7011-EVA

The development of the proposed controller is done on Matlab/Simulink environment of a Host PC for eases of modifying or optimizing with various working conditions. Simulink model of the developed controller is carried out as shown in Fig. 4. Then the Simulink diagram is compiled into C code. Here, inputs and outputs of the real-time window targets and the embedded compiler for Matlab/Simulink are provided by A&D Company Ltd[7]. Then the Human Machine Interface (HMI) on AD7011-EVA board is developed and displayed in Fig. 5. Once the controller had been developed, modifications and improvements can be done easily with simple procedures and tele-programming can be done via internet. This is a great advantage AD7011-EVA provides.

Fig. 5 Design of HMI for AD7001-EVA

4. Experiment implementation

4.1 Experimental parameters:

For the designed experimental test rig, the control parameters of the developed iterative PID controller are selected as:

-1.0 -0.5 0.0 0.5 1.0

Pos error [mm]

0 20 40 60 80 100

120 REF

Response

Sin Response [mm]

-2 0 2 4 6

8 P1

P2

Pressure [bar]

0 5 10 15 20

-12 -8 -4 0 4 8 12

Control Signal [V]

Time [s]

Fig. 6 System response for 0.1Hz sinusoidal trajectory, no load condition

0 5 10 15 20

-0.5 0.0 0.5

1.0

^PID ^IPID

Time [s]

Pos error [mm]

0 50 100

REF IPID PID

Sin Response [mm]

Fig. 7 Comparison for 0.1Hz sinusoidal trajectory,

no load condition

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  



_

  

_

  

_

 

  

Meanwhile, parameters of PID controller are obtained easily by choosing the adaptation gain.

-2 -1 0 1 2

Pos error [mm]

0 20 40 60 80

100 ^REF_Response

Sin Response [mm]

0.0 2.5 5.0 7.5

10.0 P1

P2

Pressure [bar]

0 4 8 12

-12 -8 -4 0 4 8 12

Control Signal [V]

Time [s]

Fig. 8 System response for 0.15Hz sinusoidal trajectory, 30 kgf loading force

0 5 10

-1 0 1 2

Time [s]

PID IPID

Pos error [mm]

0 50 100

REF IPID PID

Sin Response [mm]

Fig. 9 Comparison for 0.15Hz sinusoidal trajectory, 30 kgf loading force

4.2 Experimental results

Case study 01: In this case, no loading force is applied, that is    . The desired trajectory is selected as a sinusoidal signal with 50mm of amplitude and 0.1 Hz of frequency:



_

 sin 

Applying the proposed control method, response of the EHA, control input signal, and system states are shown in Fig. 6. As seen in this figure, the proposed controller ensures an excellent tracking performance. After the first iteration, the tracking response is well adapted with the tracking error lies mostly in 0.03mm. Control signal is generally in smooth form implies that the proposed controller acts well for sinusoidal tracking tasks.

A comparison of the proposed controller with the PID controller is shown in Fig. 7. It can be seen that the PID controller provides worst response.

Due to the presence of nonlinearities and uncertainties, the PID controller cannot ensure accurate tracking performance for sinusoidal reference. With the iterative learning mechanism, the proposed controller provides excellent tracking performance after one updating iteration only.

Case study 02: This case of study dealing with tracking task of a 0.15 Hz in frequency and 50mm in amplitude sinusoidal trajectory with a 300N loading condition. Applying same controllers;

response of the EHA, system states, and control input signal are shown in Fig. 8, and Fig. 9, respectively. At shown in these figures, even for a different working condition, the proposed controller with iterative learning mechanism maintains good response.

As shown in Fig.9, the performance of the first

iteration decreases crucially with tracking accuracy

can only reach around 1.0mm. However, with

learning ability, tracking error with the proposed

IPID for the second learning iteration lies mostly

in 0.05 mm and peak of tracking error is just a

little bit higher than one in the first case of study.

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5. Conclusions

In this paper, a new position generating system has been successfully developed and implemented in practical periodic tracking operation. The combination of the compact package of EHA with the small size and reliable embedded computer bring favorable features to the implementation in industrial environments. Meanwhile, the application of iterative learning mechanism into the conventional PID controller provides feasible results in improving control performance with robust and simple algorithm.

In our experiments, the full stroke bandwidth of the proposed position generator is 0.5Hz. For all sinusoidal reference signals below 0.5Hz, the proposed position generator maintains accuracy over 90%. With higher input frequency, the accuracy of generated signal decreases dramatically.

Future works may focus onto two aspects.

Firstly, the proposed controller may be improved by employing some adaptive laws to the iterative gains of the controller. This helps to improve the bandwidth of the proposed position generator.

Another trend is to investigate the robustness of the proposed controller. Since the robustness analyses of the proposed controller was described by Madady in[6], it would be helpful to test the proposed controller with other periodic reference signals or sudden change in loading condition.

Acknowledgement

This work was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry

of Education(No.2013R1A1A2063323) and the Technology Innovation Program (10043810, Development of a 20～40kW smart hybrid powerpack for industry based on intelligent control) funded By the Ministry of Trade, industry