**1. Introduction**

In the last few years the applications of artificial intelligence techniques have been used to convert human experience into a form understandable by computers. Advanced control based on artificial intelligence techniques is called intelligent control. Intelligent systems are usually described by analogies with biological systems by, for example, looking at how human beings perform control tasks, recognize patterns, or make decisions. Fuzzy logic is a way to make machines more intelligent enabling them to reason in a fuzzy manner like humans. Fuzzy logic, proposed by Lotfy Zadeh in 1965, emerged as a tool to deal with uncertain, imprecise, or qualitative decision-making problems (Zadeh, 1965).

Controllers that combine intelligent and conventional techniques are commonly used in the intelligent control of complex dynamic systems. Therefore, embedded fuzzy controllers automate what has traditionally been a human control activity.

Traditional control approach requires modeling of the physical reality. Three methods may be used in the description of a system (Passino & Yurkovich, 1998) :


Control of Efficient Intelligent Robotic Gripper Using Fuzzy Inference System 87

other device (Simoes & Friedhofer, 1997; Simoes & Franceschetti, 1999). A fuzzy logic system has four blocks as shown in figure 2. Crisp input information from the device is converted into fuzzy values for each input fuzzy set with the fuzzification block. The universe of discourse of the input variables determines the required scaling for correct per-unit operation. The scaling is very important because the fuzzy system can be retrofitted with other devices or ranges of operation by just changing the scaling of the input and output. The decision-making-logic determines how the fuzzy logic operations are performed, and together with the knowledge base determine the outputs of each fuzzy IF-THEN rule. Those are combined and converted to crispy values with the defuzzification block. The output

Temperature

In order to process the input output reasoning, there are six steps involved in the creation of

3. Create the degree of fuzzy membership function for each input and output.

5. Decide how the action will be executed by assigning strengths to the rules.

crisp value can be calculated by the center of gravity.

Fig. 1. Fuzzy sets defining temperature.

Fig. 2. Fuzzy Controller Block Diagram.

1. Identify the inputs and their ranges and name them. 2. Identify the outputs and their ranges and name them.

6. Combine the rules and defuzzify the output.

4. Construct the rule base that the system will operate under.

a rule based fuzzy system:

Fuzzy control strategies come from experience and experiments rather than from mathematical models and, therefore, linguistic implementations are much faster accomplished. Fuzzy control strategies involve a large number of inputs, most of which are relevant only for some special conditions. Such inputs are activated only when the related condition prevails. In this way, little additional computational overhead is required for adding extra rules. As a result, the rule base structure remains understandable, leading to efficient coding and system documentation.
