Design and Implementation of Fuzzy Control for Industrial Robot

The dynamic equations of motion for a mechanical manipulator are highly non-linear and complex. It is therefore, very difficult to implement real-time control based on a detailed dynamic model of a robot, if not impossible (Luh et al., 1980; Lee et al., 1982). The control problem becomes more difficult if adaptive control is necessary to accommodate changing operational conditions. Such a requirement frequently exits in the manufacturing environment; therefore, an alternative design approach would be attractive to the industrial practitioner. A better solution to the complex control problem might result if human intelligence and judgement replaces the design approach of finding an approximation to the true process model. A practical alternative would be the use of fuzzy logic. It has been reported that fuzzy logic controllers performed better, or at least as good as, a conventional controller and can be employed where conventional control techniques are inappropriate (Li et al., 1989; Sugeno, 1985; Ying et al., 1990). In contrast to adaptive control, fuzzy logic algorithms do not require a detailed mathematical description of the process to be controlled and therefore the implementation of fuzzy logic should, theoretically, be less demanding computationally. Fuzzy logic algorithms can be designed for environments where the available source information is not accurate, subjective and of uncertain quality. Furthermore, these algorithms provide a direct means of translating qualitative and imprecise linguistic statements on control procedures into precise computer statements. In this chapter, a proposed fuzzy logic design to control an actual industrial robot arm is outlined. The description of fuzzy logic controller is described in Section 2. It includes the methodology for the design of a fuzzy logic controller for use in robotic application. Section 3 presents the robot control system architecture. In Section 4, the relevant issues that arise relating to the design techniques employed are discussed in detailed. These issues include choise of sampling time, fuzzy rules design strategy, and controller tuning strategy. To evaluate the effectiveness of the proposed design strategy, studies are made to