Abstract
Motion control is widely used in industrial applications since machinery, robots, conveyor bands use smooth movements in order to reach a desired position decreasing the steady error and energy consumption. In this paper, a new Proportional-Integral-Derivative (PID) -type fuzzy logic controller (FLC) tuning strategy that is based on direct fuzzy relations is proposed in order to compute the PID constants. The motion control algorithm is composed by PID-type FLC and S-curve velocity profile, which is developed in C/C++ programming language; therefore, a license is not required to reproduce the code among embedded systems. The self-tuning controller is carried out online, it depends on error and change in error to adapt according to the system variations. The experimental results were obtained in a linear platform integrated by a direct current (DC) motor connected to an encoder to measure the position. The shaft of the motor is connected to an endless screw; a cart is placed on the screw to control its position. The rise time, overshoot, and settling time values measured in the experimentation are 0.124 s, 8.985% and 0.248 s, respectively. These results presented in part 6 demonstrate the performance of the controller, since the rise time and settling time are improved according to the state of the art. Besides, these parameters are compared with different control architectures reported in the literature. This comparison is made after applying a step input signal to the DC motor.
Highlights
Linear motor motion controllers are presented in many industrial applications, including sliding door closers, assambly, conveyor systems, electronic manufacturing, material handling, industrial test, and robotic applications [1]
An FPGA is used to measure the position of the encoder and send it to the computer using the RS232 communication protocol
A PWM module was implemented in the FPGA to apply the control signal to the servo amplifier
Summary
Linear motor motion controllers are presented in many industrial applications, including sliding door closers, assambly, conveyor systems, electronic manufacturing, material handling, industrial test, and robotic applications [1]. Motion control is applied to avoid the stress that is produced by a fast movement and to reduce the vibrations that are caused by the high rate of change in acceleration; trajectories are created to reach a desired position that the actuators must achieve [3,4]. In 1965, Zadeh proposed a theory of creating and processing models that are similar to those used by a human brain, called fuzzy logic (FL) [6]. This theory was never intended for use in control systems. The logic applied to fuzzy systems consists of sets; a fuzzy set is a class of objects with a continuum of grades of membership, whereas a classic set is composed by true or false values [9]
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