Related Pages
Technical Articles
Index
Sitemap
Home Page
Technical Article
Putting Fuzzy Logic to Work
![[picture of 'Fuzzy PLC']](images/fuzzy_plc.jpg)
Improved
Supervisory Control with the Fuzzy PLC
Although fuzzy logic has been around since the mid 1960s it is only recently that its full potential for solving an extensive array of complex control tasks has begun to be widely appreciated.
The marriage of fuzzy logic to the new generations of compact programmable logic controllers to produce the 'fuzzy PLC', has now given engineers a realistic, easy to use, and importantly, low cost means of making the most of what is an extremely useful and powerful automation technology.
A certain aura has been built up surrounding fuzzy logic technology. A lot of engineers whose applications would greatly benefit from its use are put off simply by the name itself. For many, the word fuzzy conjures up thoughts of imprecision, yet nothing could be further from the truth! Fuzzy offers a great deal to a wide range of closed-loop and open-loop applications. It is of particular benefit for processes with non-linear characteristics and sequences with opposing control objectives, for example control of temperature and humidity. Fuzzy logic is ideal where more than a single control variable is involved.
Fuzzy logic has proved its broad potential in industrial automation applications. Like the PLC it has found great success in the production and processing environments. And, just like the PLC, which is now more compact, more powerful and more cost efficient than ever before, it is now moving into the arena of building automation particularly in providing effective control for ventilation, humidity and temperature.
So what exactly is fuzzy logic? By definition fuzzy logic is a form of reasoning which uses approximation rather than complex mathematical models. With fuzzy logic control algorithms can be given in everyday language using if/then rules. Fuzzy control systems operate in three stages: fuzzification where physical values are converted into linguistic values, fuzzy inference involving if/then rules generating output values; and finally defuzzification, meaning the conversion of linguistic values back to physical values.
In automation applications, engineers primarily rely on proven concepts. For discrete event control they mostly use ladder logic. This is a programming language resembling electrical wiring which runs on the PLC. For continuous control, either bang-bang or PID type controllers are mostly employed.
The problem with conventional PID and bang-bang type controllers is that they can only handle one type of variable, so problems must be solved using a number of independently operating control loops, which are unable to talk to one another. In cases where it is necessary to exploit interdependencies of physical variables the engineer has to set up a complete mathematical model of the process and from it derive differential equations that are essential to the implementation of a solution.
In the real world of automation this is rarely feasible. Creating a mathematical model for a real-world problem can involve years of work. Most mathematical models employ extensive simplifications that require 'fudging' in order to optimise the resulting controller later on. And, optimising the system at one operating point using global factors inevitably degrades performance at other operating points.
Most engineers do not have the background required for rigorous mathematical modelling. Thus generally in automation applications, 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 and the fuzzy PLC provide an elegant and highly efficient solution. Fuzzy logic lets engineers design supervisory multi-variable controllers from experience and experimentation rather than from mathematical models. The fuzzy PLC can be programmed very simply using approximate if/then rules in iterative loops, eliminating the need for complex formulae. Use of fuzzy logic enables engineers to slash design times by more than half.
The best way of appreciating the capabilities of fuzzy logic and the fuzzy PLC is by looking at some of the diverse applications that have already benefited from their adoption.
An excellent example of the successful use of the fuzzy PLC is in automatic gantry crane operation. The pendulum motion of loads suspended from a gantry crane endangers both the operating personnel and the load being transported. The crane operator, by skilful manual application of the controls, ensures that this unavoidable pendulum motion subsides as quickly as possible, since extended loading and unloading is costly.
Increasingly however, operating conditions mean that suppression of load swing by the operator is not possible, so alternative mechanical or control engineering solutions have to be found. Mechanical solutions such as cable bracing or scissor-action systems are extremely expensive to install and maintain. Active crane swing compensation, on the other hand is a relatively inexpensive means of achieving much greater safety and the faster transfer of loads.
Moeller has developed an active crane swing compensation scheme that consists of positioning logic, encapsulated together with a fuzzy logic regulator to effect swing damping. This intelligent regulator, built into a fuzzy PLC perfectly reproduces the skill of the crane driver. This type of anti-sway control system was recently successfully applied to a 64-ton gantry crane. The crane's productivity increased by 20%.
As mentioned earlier, fuzzy logic is tailor-made for temperature control. In plastic injection moulding machines precise temperature control is crucial to achieve high and consistent product quality. This requires laborious fine tuning of the algorithms concerned because of the relatively large dead times involved in an extrusion machine and the significant coupling between the different temperature zones.
In order to greatly reduce machine commissioning time, Moeller developed a self-tuning controller using the fuzzy PLC. Compared to conventional tuning algorithms, the fuzzy logic based controller did not require the cooling down of the machine to room temperature before self-tuning could work. Even very difficult temperature zones with big dead times can be handled by this algorithm and the result is a very robust controller indeed. This is very important because the temperature characteristics of an empty machine and one filled with plastic material are poles apart. The fuzzy logic controller in the moulding machine reached the set-point faster and with a significantly smaller overshoot than the conventional solution.
The fuzzy PLC is set to play an increasing role in the control of heating, lighting and air-conditioning systems in buildings. Climate control systems in particular show a high potential for energy savings. This is borne out in a recently completed application at a major hospital in Europe. The integration of fuzzy logic into the hospital's climate control system yielded a 25% saving on electrical energy which was put at around £35,000 annually.
The fuzzy logic controller outputs the set values for the hospital system's coolant valve, water heater valve and humidifier water valve. The fuzzy logic control strategy employs 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 approaches. For example we know 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 controller 'to tell' the humidity controller that it is going to activate the heater valve. This means that the humidity controller can now respond to this action before it can detect it by its sensor. The result is an increase in control quality.
Advances in turbine technology in recent years has seen the commercial use of wind energy become a reality. As wind farms get bigger, adopting larger and larger plant, so the cost/performance ratio has steadily improved. The problem is that very large wind energy converters require advanced control systems both to ensure high efficiency and prolonged life. The controller sets the angle of the rotor blades based on the wind situation (pitch control). However, wind is not a one-dimensional phenomenon. Strength, 'gustiness', and the fluctuation of the wind angle must all be evaluated to determine the optimal rotor blade angle.
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, a fuzzy logic system based on human experience was added to the wind converter's standard controller to find the best compromise to this trade-off. Not only has the quality of control increased together with the constancy of delivered power but also mechanical stresses on the tower, nacelle and rotor blades have been reduced thus prolonging the life of the plant.
In all of these examples, and the hundreds more that have been developed over the past few years, the key to success lies in the clever combination of both conventional automation techniques and fuzzy logic. Fuzzy logic has never been intended to replace conventional control engineering. Rather it complements conventional approaches with a highly efficient methodology to implement multi-variable control strategies. Thus, the major potential for fuzzy lies in the implementation of supervisory control loops. The advent of the Fuzzy PLC means that its benefits are at the disposal of everyone and at the right price.
