Adaptive Servo-Control Concept for the Non-Interacting Control of Electrohydraulic Systems

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O + P »Ölhydraulik und Pneumatik« 45 (2001) Nr. 9

Adaptive servo-control concept for the non-interacting control of electrohydraulic systems HEIKO BAUM

Introduction The requirements to be met by servo-control structures for fluid systems are becoming increasing stringent as a result of their growing complexity. The majority of servo-control structures are still based on classical closed-loop control concepts with PIDT1 controllers, which means that there is an increasing number of interacting parameters to be taken into consideration when tuning the controller. Aiming to simplify the parameterisation work involved in the design and commissioning of servo-control systems, a tool has now been developed within the framework of a DFG-sponsored project at the IFAS, that is capable of processing the requisite closed-loop control concepts in a practical manner. At the same time, it releases the commissioning engineer from the need to acquire an in-depth knowledge of complicated servo-control systems.

The design and planning measures to be implemented for the configuration of a hydraulic system have already been presented in the O&P article entitled "CAE tool for the simplified commissioning of electrohydraulic systems with multivariable control systems" (Baum /BAU00/). That article also discussed the theoretical aspects of load sensing, presented a suitable test rig and described the tools and controller structures required to isolate the servo-control system, as well as integrating these into the DSHplus simulation program /FLU01/.

The tool is tested on an adaptive servo-control concept for the aforementioned load-sensing system in this article, by way of an example. Beginning with the field of activity for the system, as defined in the tender and performance specifications, the controller parameters that will be needed later are already calculated automatically by means of simulation during the configuration stage. In addition to this, by interfacing the control hardware to the simulation model, the servo-control concept can be tested and pre-parameterised before the real drive is built. The final commissioning of the load-sensing system and a comparison between the simulated results and the measured values clearly illustrate the potential offered by this tool.

Presenting the servo-control concept It is theoretically possible to design both hydro-mechanical and electrohydraulic load-sensing systems, including the associated closed-loop control systems, for any operating points. In the light of

the various types of load-sensing systems that can be realised, it is necessary to restrict the study to the electrohydraulic load-sensing system presented here. A detailed analysis on the practical application of autonomous servo-control systems can be found in Baum /BAU01/, which also describes the adaptive servo-control concept presented here in the form of extracts.

Figure 1 shows a general concept for isolation of the servo-control system. This concept makes use of the preliminary work on hydraulic cylinder drives and rotary units in the digital control loop conducted at the IFAS. Without going into the details of this work any further – detailed information can be found in Klein /KLE93/, Boes /BOE95/ and Weishaupt /WEI93/ – it is true to say that the purpose of the adaptive servo-control systems that were successfully tested on fluid systems is to use the fluid axis with user-defined dynamic system performance to the extent permitted by the limits of technical feasibility.

One remarkable aspect of the autonomous servo-control concept that

1: General concept for an autonomous multivariable control system

xreq

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Hydraulic cylinder axisin the digital control loop

Autonomous servo-controlsystem for meshed

multivariable systems

Adaptive angulartravel controller

Pressure supplyto the electro-hydraulicload-sensing system

Hydraulic rotary unitin the digital control loop

Dr.-Ing. Heiko Baum FLUIDON Gesellschaft für Fluidtechnik mbH Jülicher Straße 336, 52070 Aachen currently employed on the scientific staff at the Institute of Fluid Power Transmission and Control (IFAS) at RWTH Aachen, Director: Univ. Prof. Dr.-Ing. H. Murrenhoff

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we are aiming for here is that, while every fluid axis constitutes a closed control loop in itself, each one also influences the pressure control loop of the load-sensing system in its capacity as a load with known dynamic performance. The known dynamic performance is nothing more than the dynamic response determined by the project engineer's selection of "ideal poles".

This ideal dynamic performance is enabled by the fact that the operating-point-specific feedback vector parameters for the status control system in these concepts are either calculated by means of analysis or read out of tables for adaptation. Irrespective of the method used to dimension the various axes, calculation of the feedback vector can be essentially disassociated from the design of the autonomous servo-control system. Although this reduces the complexity of the configuration to a considerable extent, integration into a load-sensing system does require adaptation to the changing system pressure of the load-sensing system.

The only important aspect for the controller synthesis performed here is that the various fluid axes which are acting as loads are only characterised by the natural frequency ϖϖϖϖ0 and attenuation δδδδ parameters from the autonomous servo-

control system's point of view. This is possible because the studied fluid axes can be described very well by a second or third-order PTN system for macroscopic examination, in spite of the fact that they must be portrayed as higher-order systems when examined more closely in the course of configuration. This means that only the dynamic system performance identified by the respective axis controller or defined by the user, and the controller parameters calculated for the respective operating point during decoupling, must be known for open-loop adaptation of the autonomous servo-control system. Figure 2 shows the principle of the way in which this open-loop adaptation can be realised.

Contrary to what would appear advisable according to the preliminary theoretical study, both pressure controller and angular travel controller have been realised as full-scale PIDT1 controllers, as generally applicable basic controllers are required. Unsuitable for pressure control, the controller's I component is set to zero automatically and eliminated during automatic parameter computation.

Open-loop adaptation of the controller actually takes place later as a function of the respective operating point. Unlike the other work presented in connection with open-loop adaptation up to now, the data

records required for this are stored in the form of a neuronal database rather than being summarised in tables. The loop controller parameters depend on the following influencing variables: ααααreq as the volume flow parameter, ppump as the pressure parameter, Tfluid as a measure of the changing dynamic system performance over the fluid temperature and the dynamic characteristics ϖϖϖϖ0 and δδδδ0 of the connected loads.

A PIDT1 controller also offers an adequate solution for the interface controller used to decouple the influence of the loads from the closed-loop pressure control system. An analysis of the work performed in connection with meshed load-sensing system up to now shows that the closed-loop pressure control system can be isolated from the influence exerted by the loads to an adequate extent by feeding the sum of the velocity differences, weighted according to the active cylinder surfaces or absorption capacity, back to the angular travel controller. This is nothing other than the accumulation of all volume flow values that the system lacks to achieve the currently required operating point, however. All of the system variables required to determine this value must be available for calculation of the specified angular travel, which means that it can be easily generated within the servo-control system and used as an input variable for the interface controller. On the other hand, a neuronal database is used to apply open-loop adaptation to the various operating states.

Automatic generation of loop controller parameters The first stage of the work performed within the framework of configuration normally involves defining the requirements profile that constitutes the framework for development of a fluid system. The requirements profile is usually defined by the customer as he has all of the information required to ensure subsequent smooth integration of the drive into the system as a whole at his disposal.

Practical experience has shown that it is a good idea to formulate tender and performance specifications as the basis for subsequent realisation of the drive concept on the part of the contractor (refer to Figure 3).

Tender and performance specifications offer a means of formulating the customer's wishes and requirements in a systematic, rational

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Adaptive angulartravel controller

Autonomous servo-control conceptfor the pressure supply to the

electro-hydraulic load-sensing system

2: Principle of open-loop adaptation with neuronal networks

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manner, and of defining and recording development objectives in terms of engineering requirements, as well as the permitted time scale and budget. Tender and performance specifications may be characterised in the following manner along the lines of VDI/VDE Guideline 3694 of 1991 in accordance with Kleinaltenkamp /KLE95/: � The tender specification must take the

requirements into consideration from the user's point of view, including all boundary conditions. It defines WHAT is to be solved and FOR WHAT. � The performance specification gives a

detailed description of the application requirements and describes the realisation requirements. It defines HOW the requirements are to met and WITH WHAT.

In terms of a general, parameter-based study of the fluid system, the requirements laid down in the tender and performance specifications also characterise the operative range of the drive and therefore provide the boundary conditions for subsequent optimisation of the initial system design. The purpose of automatic optimisation is then to transpose this scope of requirements onto the facilities of the CAE tool.

The simulation mode required to compute the loop controller parameters is inevitably oriented to the circuit diagram for the load-sensing system presented in Baum /BAU00/. The simulation model is therefore not described in greater detail in this article. Figure 4 shows the simulation model that has been generated for the purpose of computer-aided autonomisation. The neuronal networks and the hardware interface have not been integrated into this model yet for reasons related to the computing speed.

The various hydraulic components and loop-control components have been combined into seven function groups according to the tasks that they perform and are printed against a grey background for easier orientation. This article does not describe these function groups in detail, nor does it specifically present the simulation results obtained during automatic operative range simulation. As the results are identical to those obtained during simulation of the complete servo-control concept, apart from open-loop adaptation of the loop controller parameters, they will be presented in that section of the article.

Aiming to present the procedure for computer-aided autonomisation in the form of a consistent method, the next section describes the way in which the

parameters required for adaptation of the servo-control system are generated automatically using the facilities integrated into the CAE tool. Figure 5 shows the steps required for this in their logical order.

The operative ranges of the system laid down in the tender and performance specifications during the drive's design phase constitute the starting point for generation of loop-controller parameters.

In this case, these ranges are defined by the maximum system pressure, the maximum volume flow and the power limitations of the drive motor and the hydraulic components. Efforts should be made, however, to narrow this maximum possible operative range down to the greatest extent possible, allowing for the drive's later operating environment. This enables the number of operating points investigated during parameter

Requirements profile

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3: Implementation of the customer's

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4: Simulation model for computer-aided autonomisation

Speed control

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5: Steps for automatic generation of the loop controller parameters

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computation to be reduced drastically, which also reduces the computing time accordingly.

Characteristic operating points are selected once the operative range has been narrowed down. Apart from the marginal area, these operating points must also cover the entire operative range with an adequately dense grid in order to avoid having to interpolate over excessively large distances during subsequent adaptation of the loop controller parameters. In other respects, these operating points can also be described by means of specific operating parameters, such as rpm, system pressure, angular travel etc., according to the drive concept and the selected components. These parameters are combined to produce a configuration file for automatic computation, which is transferred to the memory.

Having loaded the set of parameters for an operating point, underlaid optimisation of the set of loop controller parameters belonging to the operating point in question is started automatically. The variation ranges of the model parameters to be optimised during the process are initially defined here in a similar way as for the parameter variation. If the boundary conditions for optimisation have been entered, they may be started or the configuration transferred to the memory.

A quality criterion must be defined, however, before commencing optimisation, whereby the definition of this quality criterion assumes central Neuronal database of the loop controller parametersimportance with respect to optimisation of the servo-control system. It offers a means of assessing the control results in an objective, reproducible manner. The integral or planar criteria that record the quality of the system with a single numerical value are most suitable for the current optimisation task. The speed deviation is analysed, as well as the difference in pressure and both measures of quality are weight to calculate an overall quality value. If there is a sudden change in load or speed during the simulation on which optimisation is based, the time constants for the quality criteria are reset in order to prevent the later change in the controlled system brought about by the optimisation algorithm being overrated.

If the level of accuracy or maximum number of simulations defined in the optimisation algorithm is reached during the simulation of an operating point, the loop control parameters of the simulation

7: Neuronal database of the loop controller parameters

Speed rangefor the drive [rpm]

Pressure range for the drive [bar]

Variation of loop controller parameter"Kp_Pressure" over the operative range

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Variation of loop controller parameter"Kd_Couple" over the operative range


6: Parameter ranges for the pressure regulation system Variation of the DSH network loop controller parameter

"Kp_Pressure" over the operative range



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8: Simulation model with neuronal networks and HIL interface

Interfacefrom thehardware

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Drive withLS pump

andcontrol fluid

supplyLoading unit

Input signal

Interfaceto the

hardware

Adaptive angulartravel input

Interface tomeasuring data

import

Neuronal databaseof loop controller

parameters

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mode that apply at this moment are transferred to the memory. The parameter variation loads the next operating point and the optimisation process starts again. Automatic simulation stops running when all of the selected operating points have been computed. The result is a list of data records containing the parameters that define the various operating points, as well as the optimised loop controller parameters.

The following configuration stage initially involves processing the calculated loop-controller parameters in such a way that they can be used to design the servo-control hardware. To this end, the CAE tool offers a function that enables the selection of specific parameters from any number of parameter data records and stores them in a file. These are the optimised loop control parameters and the system parameters that characterise the various operating points in this case. The selected parameters served as training data for the artificial neuronal networks.

Figure 6 shows the changes in loop controller parameters "Kp-Pressure" and "Kd-Interface" for the pressure regulation system applied over the defined operative range of the drive, by way of example, in order to illustrate the transformation of the calculated parameter base into the neuronal parameter base. The angular travel of the pump has been converted into motor rpm for this.

The parameter levels for the two control parameters following training of the neuronal networks shown in Figure 7 indicate the appearance of the neuronal data base for the servo-control concept, calculated from this initial data. If the original parameter levels are compared with the neuronal reproductions, it is evident that the fissured surface of the exactly calculated values has been transformed into a homogeneous parameter level as a result of training the network.

This corresponds exactly to the good approximation characteristic of the neuronal network. Efforts are made to calculate a solution with the least mean error for all samples by means of the training. The reproduced are somewhere between the maximum and minimum values of the exactly computed loop controller parameters, there are no disadvantages involved in using the neuronal networks in the adaptive pressure regulator. It is, however, clearly evident that, where pronounced changes are required in the controller parameters as a function of the operating points,

these are also mapped by the neuronal parameter levels. When computation of the neuronal database has been completed, it is then integrated into the concept as a whole and undergoes initial testing in a hardware-in-the-loop simulation.

The simulation model of the load-sensing system shown in Figure 8 is essentially based on the model presented above in Figure 4. It has been supplemented, however, by the neuronal database, the interfaces to the servo-control hardware and a measuring data import facility, that is capable of processing both measures and simulated results.

The drive with load-sensing pump, hydraulic axis and loading unit function groups correspond to the function groups already mentioned above. The secondary axis function group and all closed-loop control function groups are superfluous, however, as these are replaced by the adaptive servo-control concept. The function groups described briefly in the following are added as new elements: � Setpoint input: This function group

contains the function generators with setpoints that characterise the various operating points of the drive. More specifically, these are the specified load-sensing pressure difference, speed and load pressure values. The function generators supply the setpoint values for the simulations, as well as for the test benches. � Interface to the hardware: This

component has been specially designed to enable communication with the hardware. The four inputs at the top represent the outputs of the D/A converter, the next sixteen inputs represent the inputs of the A/D converter and the other inputs are used as supplementary inputs, e.g. for the parameters of the neuronal database. The test rig hardware can be addressed directly via the D/A converter terminals according to the respective operating mode. The A/D converter terminals constitute the actual interface to the

HIL. If the test rig is simulated in the computer, the signals output to the controller hardware from the simulation model replace the actually measured values. � Interface from the hardware: This

component is the counterpart to the component described above. Here too, the first four outputs represent the outputs of the D/A converter and the next sixteen outputs represent the inputs of the A/D converter. The following three inputs supply auxiliary information, e.g. the timing period for the controller hardware.

If the simulation model is operated in the HIL, the outputs of the D/A module supply the controller correcting values calculated for the controller hardware. If the controller hardware is used in test-rig mode, the signals output to the drive's actuators by the hardware are output to the results memory of the simulation model for documentation purposes.

Irrespective of whether the interface is operated in HIL or test-rig mode, each of the A/D converter's inputs always supplies the value of the sensor that is designated by the name of the subsequent signal node. This means that access to the neuronal database is always the same for both HIL and test-rig modes. The sensor signals are scaled in the way determined by the parameters of the respective sensor in the interface dialogue.

The simulation model also contains the following function groups, that are not explained in any greater detail here: � Interface for measuring data import � Adaptive angular travel input � Neuronal database for the loop

controller parameters At this point, it should be mentioned that the closed-loop control assemblies that have now become obsolete are not removed from the simulation until the results for the hardware controller match the results for these function groups. This makes it much easier to develop the loop controller algorithms, particularly as the target hardware permits the use of object-oriented source code for real-time

9: Operative range of the drive with overlapping load

and speed changes

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programming. It is therefore not only possible to use the actual controller source code in HIL simulation or test-rig mode, but also in SIL mode that enables analysis of the controller results using modern software tools (debuggers).

Commissioning the drive The simulations performed in order to determine the loop controller parameters for the autonomous servo-control concept

were not oriented to any specific operating cycle. On the contrary, computer-aided autonomisation involved approaching a certain operating point repeatedly with changes in load and speed until such time as the optimum loop controller parameters were determined for this using the algorithm. The resulting set of all optimised loop controller parameters makes up the neuronal database for open-loop adaptation. If the

practical applicability of this database is to be investigated, attempts must be made to simulate a representative load cycle for the drive.

The load-sensing system described above is a test rig and not a real system, e.g. installed in a mobile application, which means that the representative load cycle must be generated by artificial means. The important factor in this context is to ensure that all of the operating points are approached that result in extreme loads on the drive. These specifically include the operating points that describe the performance limits of the drive and the operating points that represent the most critical dynamic boundary conditions, e.g. low attenuation with the drive running at slow speed under low load. Figure 9 shows an operating cycle generated in accordance with these boundary conditions.

Beginning with the test rig at a standstill, the drive initially approaches an operating point where the motor speed is 500 rpm with a proportional pressure control valve set to 20 % of its operative range within 10 s. The load moment determined for the motor by the position of the proportional pressure control valve can be calculated from the characteristic of the valve and the load pump data, or read off from the simulation results. The characteristic for valve activation is realised by a characteristics map in the simulation. The load-sensing pressure difference is adjusted to 30 bar until the initial operating point is reached.

From this operating point, changing speed and load pressure setpoints are superimposed over a period of 10 s. Each parameter is maintained constant for 2 s. The simulation runs through all of the operating points that characterise the dynamic performance of the load-sensing system during the measuring cycle. At the end of the changing speed and load run, the test rig has returned to the initial operating point and is brought to a standstill again during the next 10 s. HIL simulation of the drive is not described in detail in this article; please refer to the detailed information given in Baum /BAU01/. This publication also describes the way in which the individual control loops can be commissioned in succession to demonstrate that the automatically computed loop control parameters offer a very suitable means of achieving stable operation of the meshed loop controller in the load-sensing system, therefore achieving non-interacting control. In this connection, one aspect that must be evaluated very

0 6 12 18 24 30Time [s]

pLoadDBV_meas.Value 0 bis 240 barnSpeedl_meas.Value 0 bis 2000 1/minnSpeedReq.Value 0 bis 2000 1/min

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nSpeedl_sim.Value 0 bis 2000 1/minpLoadDBV_sim.Value 0 bis 240 bar

nSpeedReq.Value 0 bis 2000 1/min

10: Comparison of speeds and load pressure values

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pPB_sim.Pressure 0 bis 220 barpPA_sim.Pressure 0 bis 220 barpPump_sim.Pressure 0 bis 220 bar

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pPB_mess.Value 0 bis 220 barpPA_mess.Value 0 bis 220 barpPump_mess.Value 0 bis 220 bar

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S_MotorSpeed_sim.Value -5 bis 10 VS_AngularTravel_sim.Value -10 bis 20 V

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12: Comparison of actuating signals

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Kd_Kop.Value 0 bis 200 msKd.Value 0 bis 200 msKp.Value 0 bis 4 VKp_Kop.Value 0 bis 4 V

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Kd_Kop.Value 0 bis 200 msKd.Value 0 bis 200 msKp.Value 0 bis 4 VKp_Kop.Value 0 bis 4 V

13: Loop controller parameters (simulation on the left, measured values on the right)

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positively is that this is also possible when the control parameters are computed by a test-rig simulation model that has been parameterised on the basis of catalogue data to a great extent, rather than being optimised with respect to the non-linearity of the real test rig. This is where the positive characteristics of the selected PDT1 controller described in Baum /BAU00/ come into their own.

The results obtained while commissioning the drive are described briefly below. A direct comparison is made between measured data and the results of HIL simulation, whereby attention is specifically drawn to the special features of the load-sensing system. The presented simulations are performed using a simulation model for which the parameterisation has been improved by analysing the results of tests conducted on the test rig. Previously parameterised solely on the basis on manufacturer's data sheets, the model can now be upgraded by the real friction conditions, the inertia of the connected shafts and the real valve characteristics measured at the test rig on-line with the CAE tool during commissioning. The setpoint values used in the simulation are identical to the characteristics described in the load profile.

Figure 10 shows a comparison between simulated ( ) and measured ( ) motor speed, as well as between simulated ( ) and measured ( ) load pressure.

There is good conformance between the simulated and measured load pressure characteristics. This means that the simulation model is capable of reproducing realistic loading for the load-sensing system. As far as speed is concerned, there is also good conformance between simulated and measured data with respect to the dynamic performance with the revised model. No longer optimised after parameter adaptation, the loop controller parameters are not capable of giving the system sufficient stability with respect to a change in the influencing variable, however. The loop controller parameter base must be computed again for this.

The good conformance between the dynamic responses of real and simulated load-sensing systems is also evident in the system pressure values shown in Figure 11.

In this context, it is important that the conformance between the simulated pressure values ( , and ) and measured pressure values ( , and ) does not only take place in the quasi-

stationary states at the beginning and end of the measurement run, when the test rig is slowly accelerating to the initial operating point or decelerating again to a standstill. It is also clearly evident that the simulated load-sensing system demonstrates the same dynamic pressure fluctuations as the real drive during the actual load profile, whereby the amplitudes of the oscillations in the simulation are approximately the same as the amplitudes of the measured oscillations. Furthermore, the kink in the system pressure characteristic ( simulated, measured) shows that the non-linear valve characteristic of the closed-loop motor control system has been taken into consideration in the simulation model.

The comparison of actuating signals shown in Figure 12 again confirms that the simulation offers an expedient means of emulating real system behaviour for automatic computation of loop controller parameters. The simulated ( ) and measured ( ) actuating signals for the speed control system match very well when the reference speed variable changes.

Differences do become evident, however, if there are changes to the load pressure influencing variable. As already shown in the comparison of speeds, the adaptive control in the simulation demonstrates a very strong reaction to changes in the influencing variable. The system pressure is not maintained at a constant level for a while and there is an inevitable change in the speed of the motor. The speed control system attempts to implement corrective action here, as is clearly shown by the differences between simulated and measured actuating signals. This means that efforts must be made to improve the adaptive control system's response to changes in the influencing variable when the loop controller parameter base is computed again.

A comparison of the simulated ( ) and measured ( ) correcting variables for the angular travel controller indicates that high-frequency oscillation is superimposed on top of the actuating signal on the real system. Measures must be implemented to determine the cause of this oscillation here as it becomes obvious that this oscillation is superimposed onto rapid changes in the correcting variable of the type that occur during the simulation. This causes the drive to lose some of its dynamic performance.

Figure 13 shows the open-loop adaptation parameters that correspond to the servo-control results presented here.

Both charts clearly show a symmetry in the parameter adaptation between starting and stopping the measurement. The adaptation becomes "blurred", however, when measured values are compared with simulated values directly. The networks' input variables are filtered to a great extent for the measured values, causing a certain smoothing of the dynamic performance, to which the network reacts accordingly.

Summary To summarise, it may be said that the open-loop neuronal adaptation of a servo-control system presents a concept that enables a simple, easy-to-use means of decoupling a meshed multivariable control system without the need for in-depth, theoretical knowledge of closed-loop control systems. The loop controller parameters required for decoupling are calculated automatically in an initial approach using a simulation model that is based on catalogue data. The hardware required for closed-loop control of the drive can then be developed and tested in comfort, using the same simulation model.

The subsequent commissioning of the system shows that the calculated loop controller parameters can also be transferred to a real hydraulic system that contains non-linearity not taken into consideration during the first system simulation. This is made possible by the rugged construction of the PDT1 controller used.

It is then possible to improve the parameterisation of the simulation model during commissioning to such an extent – by comparing measured data with simulated data – as to finally produce a simulation model that is an exact replica of the real test rig response. This simulation model enables recalculation of the loop controller parameter base, which logically results in an improvement to the control results. The "fine tuning" of the control parameters performed on the system by the commissioning engineer up to now will be replaced - at least in part - by an external "fine tuning" procedure using the simulation model.

The control results could be improved further by the implementation of a speed control system that is also adaptive. This adaptation was omitted within the framework of the DFG project because of the complexity involved. The aim was to demonstrate the practical application of

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the presented methods to achieve automatic decoupling of a meshed multivariable control system. Appropriate approaches to the adaptation of speed control systems can be found in the projects that served as the basis for the servo-control concept realised here, but these already exist. Determination of the necessary loop controller parameters can be directly integrated into the automatic computation of the loop controller parameter base.

The only other aspect that should be mentioned at this stage is that all of the measurements discussed in this article were conducted with the CAE tool connected to the real load-sensing system via the hardware interface. This means that the measured data is directly available in the simulation model and can be used for improvement or parameterisation purposes.

Bibliography: /BAU00/ Baum, H., Murrenhoff, H.: CAE-Tool zur vereinfachten Inbetriebnahme von elektrohydraulischen Systemen mit Mehrgrößenregelung [CAE tool for the simplified commissioning of electrohydraulic systems with multivariable control systems] O+P “Ölhydraulik und Pneumatik” 44 (2000) No. 5, pages 321-330 /BAU01/ Baum, H.: Elektrohydraulisches Load-Sensing mit selbsteinstellenden Regelungskonzepten [Electrohydraulic load-sensing with self-tuning control concepts] Final report on DFG Project MU 1225/11-1 Aachen, March 2001 /FLU01/ N. N.: Benutzerhandbuch DSHplus 3.0 [User's manual for DSHplus 3.0] FLUIDON GmbH, Aachen, 2001 /BOE95/ Boes, C.: Hydraulische Achsantriebe im digitalen Regelkreis Dissertation, RWTH Aachen, 1995 /KLE93/ Klein, A.: Einsatz der Fuzzy-Logik zur Adaption der Positionsregelung fluidtechnischer Zylinderantriebe [Using fuzzy logic for adaptation of the position control systems for hydraulic cylinder drives] Dissertation, RWTH Aachen, 1993 /WEI95/ Weishaupt, E.: Adaptive Regelungskonzepte für eine hydraulische Verstelleinheit am Netz mit aufgeprägtem Versorgungsdruck im Drehzahl- und Drehwinkelregelkreis [Adaptive control concepts for a hydraulic actuating unit on the network with impressed supply pressure in the speed and rotary angle control loop] Dissertation, RWTH Aachen, 1995 /KLE95/ Kleinaltenkamp, M., Plinke, W.: Technischer Vertrieb –Grundlagen [Fundamentals of technical sales] Springer-Verlag, 1995, page 550ff

Proof of figure: Autor : Quelle

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Adaptive Servo-Control Concept for the Non-Interacting Control of Electrohydraulic Systems