Reference Variables Generation Using a Fuzzy Trajectory Controller for PM Tubular Linear Synchronous Motor Drive R. LUÍS J.C. QUADRADO ISEL, R. Conselheiro Emídio Navarro, 1950-072 LISBOA CAUTL, R. Rovisco Pais, 1049-001 LISBOA PORTUGAL Abstract: The usage of linear permanent-magnet (PM) actuators, and their associated controllers, increases in a wide variety of applications, due to the exhibited high force density, robustness, and accuracy. The s-curve motion profiling is the motion trajectory usually employed in common industrial applications. In this control scheme, the trajectory shape is determined by maximum acceleration, maximum speed, and the target distance. The values of speed and acceleration must be chosen carefully. If they are chosen excessively large or very small, it may not be possible for the system to track the generated trajectory with good accuracy. This paper, considers the control of a single degree-of-freedom (DOF) mechanical system, in which a PM Tubular Linear Synchronous Motor (PM-TLSM) is used as the actuator. Since the fuzzy logic control controllers are based on heuristics are therefore able to incorporate human intuition and experience. The resulting motion trajectory obtained from the fuzzy logic is particularly suited to high accuracy applications such as parallels manipulators, robotics systems and factory automation. Computer simulation results verify the effectiveness of the proposed scheme. Key-Words: Fuzzy logic control, Trajectory tracking, Modelling and Simulation. 1 Introduction In recent years, the fuzzy control has been increaselly developed and has become one of the most successful tools in the industry. Fuzzy logic control provides a formal methodology for representing, manipulating and implementing a human’s heuristic knowledge on how to control a system [1]. Figure 1 shows the main components of the fuzzy controller. Fig.1: Block diagram of the fuzzy controller embedded in the closed-loop control system. The “rule-base” holds the knowledge, in the form of a set of rules, i.e. the way to control the system. The “inference engine” evaluates which control rules are appropriate at the current time and then decides what the input to the process should be. The “fuzzification” interface modifies the inputs so that, they can understand and activate the rules in the “rule-base”. The “defuzzification” interface converts the conclusions reached by “inference engine” into the process inputs. The Fuzzy logic incorporates a simple, rule- based “IF X AND Y THEN Z” approach to solve the control problem rather than attempting to model a system mathematically. The fuzzy logic model is empirically-based, relying on the operator's experience rather than its technical understanding of the system. 2 System description This paper uses two fuzzy logic controllers to predict the necessary speed and acceleration references allowing the system to track the position reference correctly. Another possible approach relies on the theory and application of the time-optimal control of the linear actuators, which is obtained in feedback form via a three-dimensional state-space analysis, [2]. Proceedings of the 6th WSEAS Int. Conf. on FUZZY SYSTEMS, Lisbon, Portugal, June 16-18, 2005 (pp1-6)