Fuzzy Application Library/Technical Applications/Industrial Automation
Recent Successful Fuzzy Logic Applications in Industrial Automation
by Dr. Jörg Gebhardt and Constantin von Altrock
Citation Reference: This paper was been published at the Fifth IEEE International Conference on Fuzzy Systems, held in New Orleans September 1996. The Fuzzy Logic Application Note series is published by Inform Software Corporation on its Internet server to promote the use of fuzzy logic technologies in applications.
In this paper, we review eight (8) recent applications of fuzzy logic in industrial automation. All applications used the so-called "fuzzyPLC", an innovative hardware platform that merges fuzzy logic and traditional automation techniques. Following a quick overview on the fuzzyPLC, we discuss the eight (8) applications and focus on how fuzzy logic enabled a superior solution compared to conventional techniques. Whenever possible, we quantify the benefit in cost saving or quality improvement. For detailed information on the reviewed applications, the respective papers are referenced.
1. Fuzzy Logic in Industrial Automation
In recent years, fuzzy logic has proven well its broad potential in industrial automation applications. In this application area, engineers primarily rely on proven concepts. For discrete event control, they mostly use ladder logic, a programming language resembling electrical wiring schemes and running on so called programmable logic controllers (PLC). For continuous control, either bang-bang type or PID type controllers are mostly employed.
While PID type controllers do work fine when the process under control is in a stable condition, they do not cope well in other cases:
The reason for this is that a PID controller assumes the process to behave in a strictly linear fashion. While this simplification can be made in a stable condition, strong disturbances can push the process operation point far away from the set operating point. Here, the linear assumption usually does not work any more. The same happens if a process changes its parameters over time. In these cases, the extension or replacement of PID controllers with fuzzy controllers has been shown to be more feasible more often than using conventional but sophisticated state controllers or adaptive approaches . However, this is not the only area where there is potential for fuzzy logic based solutions.
The real potential of fuzzy logic in industrial automation lies in the straightforward way fuzzy logic renders possible the design of multi-variable controllers. In many applications, keeping a single process variable constant can be done well using a PID or bang-bang type controller. However, set values for all these individual control loops are often still set manually by operators. The operators analyze the process condition, and tune the set values of the PID controllers to optimize the operation. This is called "supervisory control" and mostly involves multiple variables.
|Figure1: Using a Fuzzy Logic Controller to Determine the Set Values for Underlying PID Control Loops (large)|
Alas, both PID and bang-bang type controllers can only cope with one variable. This usually results in several independently operating control loops. These loops are not able to "talk to each other". In cases where it is desirable or necessary to exploit interdependencies of physical variables, one is forced to set up a complete mathematical model of the process and to derive differential equations from it that are necessary for the implementation of a solution. In the world of industrial automation, this is rarely feasible:
Also, many practitioners do not have the background required for rigorous mathematical modeling. Thus, the general observation in industry is that single process variables are controlled by simple control models such as PID or bang-bang, while supervisory control is done by human operators.
This is where fuzzy logic provides an elegant and highly efficient solution to the problem. Fuzzy logic lets engineers design supervisory multi-variable controllers from operator experience and experimental results rather than from mathematical models. A possible structure of a fuzzy logic based control system in industrial automation applications is exemplified by Figure 1. Each single process variable is kept constant by a PID controller, while the set values for the PID controller stem from the fuzzy logic system. This arrangement is typical for cases like control of several temperature zones of an oven or control of oxygen concentrations in different zones of a wastewater basin. In other cases, it could be reasonable to develop the complete closed loop control solution in a fuzzy system.
This illustrates why it is very desirable to integrate conventional control engineering techniques, such as ladder logic or instruction list language for digital logic and PID control blocks tightly together with fuzzy logic functionality.
2. Merging Fuzzy Logic and PLCs
In 1990, when more and more successful applications proved the potential of fuzzy logic in industrial automation, the German company Moeller GmbH and the U.S./German company Inform Software created the fuzzyPLC based on the observation that fuzzy logic needs tight integration with conventional industrial automation techniques.
The fuzzyPLC Hardware and Firmware
|Figure 2: The fuzzyPLC contains fuzzy and conventional logic processing capabilities, field bus connections, and interfaces.|
To make it available at a low cost, the core of the fuzzyPLC uses a highly integrated two-chip solution. An analog ASIC handles the analog/digital interfaces at industry standard 12 bit resolution. Snap-On modules can extend the periphery for large applications of up to about 100 signals. An integrated field bus connection, based on RS485, provides further expansion by networking. The conventional and the fuzzy logic computation is handled by a 16/32 bit RISC microcontroller. The operating system and communication routines, developed by Moeller, are based on a commercial real time multitasking kernel. The fuzzy inference engine, developed by Inform Software, is implemented and integrated into the operating system in a highly efficient manner, so that scan times of less than one millisecond are possible. The internal RAM of 256 KB can be expanded by memory cards using flash technology. Thus, the fuzzyPLC is capable of solving quite complex and fast industrial automation problems in spite of its compact and low price design.
The fuzzyPLC Engineering Software
The fuzzyPLC is programmed by an enhanced version of the standard fuzzy logic system development software fuzzyTECH from Inform Software. fuzzyTECH is an all-graphical, design, simulation, and optimization environment with implementation modules for most microcontrollers and industrial computers. To support the complete functionality of the fuzzyPLC, fuzzyTECH has been enhanced with editors and functions to support the conventional programming of the PLC. Thus, a user only needs one tool to program both conventional and fuzzy logic parts of the solution.
|Figure 3: Both the traditional logic and the Fuzzy Logic system is programmed by fuzzyTECH 4.0 . (larger)|
The fuzzyTECH software combines all necessary editors for membership functions, linguistic variables, rule tables, and system structure with analyzer functions and optimization features. The software runs on a PC and is linked to the fuzzyPLC by a standard serial cable (RS232) or the field bus (RS485). Through this link, the developer downloads the designed system to the fuzzyPLC. Because fuzzy logic systems often require optimization "on-the-fly", fuzzyTECH and the fuzzyPLC feature "online-debugging" where the system running on the fuzzyPLC is completely visualized by the graphical editors and analyzers of fuzzyTECH. Plus, in online-debugging modes, any modification of the fuzzy logic system is instantly translated to the fuzzyPLC without halting operation.
3. Application Case Studies
In this section, we review eight (8) recent highly successful applications of fuzzy logic in industrial automation using the fuzzyPLC:
Anti-Sway Control of Cranes
In crane control, the objective is to position a load over a target point. While the load connected to the crane head by flexible cables may well sway within certain limits during transportation, the sway must be reduced to almost zero for load release when the target position is reached. Hence, a controller must use at least two input variables, for example position and sway angle. Thus, a simple PID controller cannot be used as it is restricted to one input. Conventional solutions of the problem require highly elaborate approaches, like model based control or state variable controllers that need intensive engineering and hardware resources. These technologies tend to push system costs into regions that make anti-sway systems economically unaffordable. For these reasons, most cranes are still operated manually.
|Figure 4: The 64 ton crane of Hochtief Corp. uses fuzzyPLC based anti-sway positioning control (large, Video)|
In spite of the difficulties involved with automated control, human operators can control cranes quite well in most cases. Because fuzzy logic is a technology that facilitates control system design based directly on such human experiences, it has been used for crane automation for almost a decade. The types of cranes include container cranes in harbors, steel pan cranes, and cranes in a manufacturing environment. Recently, a 64 ton crane that transports concrete modules for bridges and tunnels over a distance of 500 yards has been automated with a fuzzyPLC in Germany . The benefit was a capacity gain of about 20% due to the faster transportation and an increase in safety. Accidents were frequent, because the crane operators walk parallel to the crane during operation with a remote controller. Before, when they had to watch the load to concentrate on the sway angle, they frequently stumbled over parts lying on the ground. The crane was commissioned in Spring 1995 and the fuzzy logic anti-sway controller has been continuously enabled by the crane operator, showing the high degree of acceptance by the operators. This fact is of special importance since not only technological feasibility but also psychological aspects are important for the success of an industrial automation solution.
|Figure 5: A software simulation of the crane controller is contained in  (large)|
A software simulation and a simple example of an anti-sway crane controller for didactic purposes can be found in . The real solution uses about 10 inputs, 2 outputs, and 4 rule blocks with a total of 75 rules.
Fire Zone Control in Waste Incineration Plants
Maintaining a stable burning temperature in waste incineration plants is important to minimize the generation of toxic gases, such as dioxin and furan, as well as to avoid corrosion in the burning chambers. There are two primary difficulties of this temperature control process:
Because the heat generated from the burning process is used to produce electrical energy, a stable incineration process is also of high commercial interest.
|Figure 6: In a waste incineration plant, a crane continuously delivers waste from the bunker to the belt running through the burning zone. The exhaust gases are cooled and cleaned. (large)|
In recent applications at waste incineration plants in the cities of Hamburg and Mannheim in Germany, fuzzy logic has been successfully applied. In Mannheim, where two fuzzyPLCs were used to control the burning process, the steam generation capacity of one furnace is 28 Mg/h. Using the industry standard conventional controller, steam generation fluctuated by as much as 10 Mg/h in just one hour. The fuzzy logic controller was capable of reducing this fluctuation to less than ±1 Mg/h. This dramatically improved robustness and also caused the NOx and SO2 emission to drop slightly, and the CO emission to drop to half [11, 12].
Dosing Control in Waste Water Treatment Plants
Waste water treatment processes are a combination of biological, chemical, and mechanical processes. This makes the creation of a complete mathematical model for their control intractable. However, there is a large amount of human experience that can be exploited for automated controller design. As such operator experience can be efficiently put to work by fuzzy logic, many plants already use this technique .
In a recent application in Bonn, dosing of liquid FeCl3 for phosphate precipitation has been successfully automated using the fuzzyPLC. Recently changes in legislation require water treatment plants in Germany to limit the total amount of phosphate in the released water to 1 milligram per liter. To extract the phosphate from the water, FeCl3 is added, which converts the phosphate into FePO4 that is sedimented with the sludge. Because a violation of the legal phosphate limit results in severe penalties, the operators tend to overdose the FeCl3.
|Figure 7: By injecting FeCl3 into the sludge, dissolved phosphate precipitates from the waste water (large)|
To optimize the FeCl3 dosing, a fuzzy logic controller that uses the input variables phosphate concentration, its derivative, water flow, its derivative, and dry substance contents was designed. The output of the fuzzy logic controller is the change of the set variable for the injected FeCl3. An underlying conventional PI type controller stabilizes the FeCl3 flow to this set point. The PI type controller is implemented as a function block in the fuzzyPLC as well. This is an example of the combination of fuzzy logic and conventional control engineering techniques.
|Figure 8: A software simulation of a simplified precipitation controller is contained in  (large)|
The total fuzzy logic controller uses 207 rules to express the control strategy based on the five (5) input variables of the fuzzy logic control block. The total implementation time was three (3) staff months and resulted in savings of about 50% of the FeCl3 compared to the manual control before. Taking implementation time and hardware/software costs into consideration with the savings on FeCl3 results a return in investment time of half a year.
Control of Tunnel Inspection Robots
The German Aerospace corporation DASA has developed a sewage pipe inspection system using two robot units and a support truck . The objective of the robots is to detect leakage in segments of the pipe by applying air pressure to the sealed space between the two robots. Because the vertical access shafts can be quite far away from each other, the robots have to operate up to 400 yards away from the truck. The robots are connected to each other and the truck by cables that provide air pressure, electrical energy, and control signals to the robots.
|Figure 9: The two robot units in the sewage pipe (right) are supplied from a specialized truck by cables (large)|
When DASA developed the system, a severe control problem came up. To avoid entanglement of the cables that can result in the robots getting stuck in the pipe, cable tension must be controlled very carefully. A conventional approach using complex state variable controllers turned out to be too costly in terms of both money and design time. A control system implemented on two fuzzyPLCs using about 200 rules each showed very good results in a very short engineering time at less than 10% of the costs of a conventional solution.
Positioning of Presses
One area with big potential for fuzzy solutions is the control of drives. In this example, we discuss hydraulic axis control. One of the most complex fuzzy projects was done for a hydraulic press used to press laminates, printed circuit boards, and floor coverings. The task was the synchronized control of a 14-axis system. The position control of the axis, a superimposed pressure controller, the parallel running of the steel belt and the synchronization of all axes had to be solved.
|Figure 10: Control of hydraulic systems is difficult as many non-linearities, Such as the "stick-slip" effect, are involved (large)|
The automation system employed has a highly decentralized structure and consists of two large master PLCs, a number of smaller compact PLCs, a PC based supervisory system, and seven fuzzyPLCs. All units are networked using the integrated field bus interfaces. Very important for the synchronization of the entire machine is the ability of the field bus network to satisfy the real time requirements. One typical problem involved in the control of hydraulic systems is the so-called "stick slip effect". The transition of an axis from standstill to motion is highly non-linear because of the transition from stick friction to slip friction. This makes designing a good controller for hydraulic systems difficult. In the cited application, fuzzy logic rendered a good solution technique, freeing system design from the burden of the theory of non linear systems synthesis. The overall design time using fuzzy logic was only a third of what a conventional approach had required in past applications of conventional control for similar presses .
Temperature Control in Plastic Molding Machines
In plastic molding machines, temperature control is crucial to achieve high and consistent product quality. This requires laborious tuning of the involved control algorithms, because the dead times involved in an extrusion machine are significant and there is significant coupling between the different temperature zones .
|Figure 11: To achieve high product quality, keeping the temperature constant is critical in molding plastic (large)|
To cut down the commission time for these machines, KM corporation has developed a self-tuning controller using the fuzzyPLC. At start up time, some parameters are estimated that are used to scale the non linear fuzzy controller. In contrast to conventional tuning algorithms, this controller does not require a cooling down of the machine to room temperature before self tuning can work. Even very difficult temperature zones with big dead times can be handled by this algorithm and the result is a very robust controller. This is important because the temperature properties of an empty machine and one filled with plastic material are extremely different. Compared to conventional systems, the fuzzy logic enhanced temperature controller performs with a faster response time and a significantly smaller overshoot combined with extreme robustness.
|Figure 12: The Fuzzy Logic controller in the molding machine reaches the set point faster and avoids overshoot (large)|
Climate Control Using Fuzzy Logic
Climate control systems reveal a high potential for energy savings. In a recent application at a major hospital in Europe, the integration of fuzzy logic saves about 25% on electrical energy, equivalent to the amount of 50,000 per year.
|Figure 13: An Application of Fuzzy Logic in the A/C system of a large hospital in Germany saved more than 25% on energy costs (large)|
The fuzzy logic controller outputs the set values for the coolant valve, the water heater valve, and the humidifier water valve. The fuzzy logic control strategy uses different temperature and humidity sensors to determine how to operate the air conditioning process in a way that conserves energy. Again, the capability of processing interdependent variables results in significant advantages over conventional solutions. For example, one knows that when temperature rises, relative humidity of the air decreases.
This knowledge can be exploited by implementing a fuzzy logic control strategy that allows the temperature controller "to tell" the humidity controller that it is going to activate the heater valve. The humidity controller now can respond to this before it can detect it by its sensor. The result is an increase in control quality [4, 8].
Figure 14: Fuzzy Logic allows to increase the energy
efficiency of an A/C system by evaluating several process values.
Wind Energy Converter Control
In recent years, technological advancements made the commercial use of wind energy feasible. A trend to larger plants further improved the cost/performance ratio. However, such large wind energy converters require advanced control systems both to ensure high efficiency and long life. The controller sets the angle of the rotor blades based on the wind situation (pitch control). However, wind is not a one-dimensional figure. Strength, gustiness, and fluctuation of the wind angle must be evaluated to determine the optimal rotor blade angle.
|Figure 15: To maximize the efficiency of a wind energy converter, the pitch controller must consider many inputs (large)|
There is a trade-off between efficiency, safety and wear of the wind energy converter. If the blade angle is set to draw the maximum amount of energy from the wind, the risk of sudden wind gusts causing excessive mechanical stress on the converter increases. For these reasons, an Aerodyn wind energy converter was enhanced with a fuzzy system based on human experience to find the best compromise to this trade-off . The first implemented system is running in a field test and shows quite promising results. The quality of the controller is not only measured in constancy of the delivered power, but also in measures of mechanical stress on the tower, the nacelle and the rotor blades. The next step will be the application of the achieved results to the first 1.2 MW systems that are to be launched in the marketplace in 1996.
As a consequence of the high degree of awareness raised by a large number of publications over the past five years in Europe, a substantial number of successful applications have been generated. Of these, we have presented a selection of eight (8) recent applications in this paper. In all applications, the key to success lies in the clever combination of both conventional automation techniques and fuzzy logic. Fuzzy logic by no means replaces conventional control engineering. Rather, it compliments conventional techniques with a highly efficient methodology to implement multi-variable control strategies. Thus, the major potential for fuzzy logic lies in the implementation of supervisory control loops.
|||von Altrock, C. and Krause, B., "On-Line-Development Tools for Fuzzy Knowledge-Base Systems of Higher Order", 2nd Int'l Conference on Fuzzy Logic and Neural Networks Proceedings, IIZUKA, Japan (1992), ISBN 4-938717-01-8.|
|||von Altrock, C., Krause, B. and Zimmermann, H.-J., "Advanced Fuzzy Logic Control Technologies in Automotive Applications", IEEE Conference on Fuzzy Systems (1992), ISBN 0-7803-0237-0, p. 831-842.|
|||von Altrock, C., Franke, S., and Froese, Th., "Optimization of a Water-Treatment System with Fuzzy Logic Control", Computer Design Fuzzy Logic '94 Conference in San Diego (1994).|
|||von Altrock, C., "Fuzzy Logic and NeuroFuzzy Applications Explained", Prentice Hall, ISBN 0-13-368465-2 (1995).|
|||von Altrock, C., Arend, H.-O., Krause, B., Steffens, C., and Behrens-Rommler, E., "Customer-Adaptive Fuzzy Control of Home Heating System", IEEE Conference on Fuzzy Systems in Orlando (1994).|
|||von Altrock, C., Arend, H.-O., Krause, B., Steffens, C., and Behrens-Rommler, E., "Customer-Adaptive Fuzzy Control of Home Heating System", IEEE Conference on Fuzzy Systems in Orlando (1994).|
|||Gebhardt, J. and Müller, R., "Application of Fuzzy Logic to the Control of a Wind Energy Converter, First European Congress on Fuzzy and Intelligent Technologies (EUFIT 93), Aachen, 09/93|
|||Gebhardt, J., Fuzzy Logic and the Programmable Logic Controller, Control Systems 09/94|
|||Gebhardt, J., New Industrial Applications of the Fuzzy-PLC Proceedings of the 3. European Congress on Fuzzy and Intelligent Technologies (EUFIT 95), Aachen, 08/95|
|||Gebhardt, J., Standard-Solutions and Industrial Practice - Dream or Reality?, 3. European Congress on Fuzzy and Intelligent Technologies (EUFIT 95), Aachen|
|||Gierend, Ch., "Fuzzy Logic Control of a Waste Incineration Plant", 5. Aachen Fuzzy-Symposium (1995).|
|||Krause, B., von Altrock, C., Limper, K., and Schäfers, W., "A Neuro-Fuzzy Adaptive Control Strategy for Refuse Incineration Plants", Fuzzy Sets and Systems, V. 63, 3 (1994).|
|||N.N., Fuzzy-Logic: Hardware and Engineering, 2/95 AWB 27-1240-GB, Moeller corporation|
|||N.N., Fuzzy-Logic: Programming and Operation of the User Interface, 11/94 AWB 27-1149-GB|