The robotic handling of non-rigid objects, such as fabrics, is a very complicated problem since it is very difficult to model and predict the behavior of the fabric. The non-linearity, the large deformations and the very low bending resistance of the fabrics increase the complexity and difficulty of the robotic handling. In this paper, the robotized sewing is examined, where the fabric must be held taut and unwrinkled. Actually, a constant tensional force, which must be applied to the fabric throughout the feeding to the sewing machine, affects the seam’s quality to a great extent [1]. Gershon [2] clearly justified the need for force feedback control, in order to obtain a fabric’s constant tensional force in the sewing task. While there is a big variety of force control methods on handling of rigid objects, the robotic research literature is not so rich concerning the handling of limp materials. In the FIGARO system [3], a PI controller is used, where the gains were chosen by trial and error. These gains should be modified when a new type of fabric should be handled. Best sewing machine showroom in chennai Gershon [2] strongly suggested that the conventional control methods are inadequate to handle the fabric tensional force. An adaptive control approach was adopted by R. Patton et.al [4] for controlling the tensional forces applied to a fabric, where the fabric’s stiffness was the unknown variable. They mentioned that non-adaptive control schemes are unsuitable for fabric handling due to high variations of fabrics’ stiffness. Koustoumpardis et al [5] introduced an intelligent force control scheme based on a feedforward neural network (FNN) controller, using a force sensor mounted on the robots wrist. In some control applications, where it is very difficult to obtain the plant model or the required computation time is too high, fuzzy adaptive control was introduced. The direct fuzzy adaptive control is the most common approach, particularly the fuzzy model reference learning control (FMRLC). The main advantage of this approach is the simplicity together with the high performance [9], a fact that makes it appealing for implementation in a wide range of industrial processes. H. F. Ho et.al. [6], used direct fuzzy adaptive control for a nonlinear helicopter system. The control objective was to maintain the elevation and azimuth angles to maintain the desired trajectories. https://vssewingmachine.in/ Rehman [10] used a fuzzy model reference learning controller in order to regulate the speed of an induction motor. Tarokh [7], proposed an adaptive fuzzy control scheme for explicit force control of a robot manipulator in contact with an environment whose parameters are unknown and vary considerably but slowly.
The adaptation mechanism modifies the fuzzy force controller according to the difference between the actual and the desired force responses. The robotics group of the department of the Mechanical Engineering and Aeronautics has been working the last years on the robotic handling of fabrics. The robotic sewing is one of the tasks that consist the group’s research. In this framework, a feedforward neural network (FNN) controller [5], able to guide a wide range of fabric types, was implemented. The target of the controller was to apply a desired constant tensional force to the fabric during the whole sewing process. In order to investigate further this area, the implementation of a FMRL controller was decided. Also, the fact that no work using fuzzy adaptive control has been found in the robotics handling of fabrics area consisted an additional motive to use this method. In the present paper, the FMRL force control scheme is designed, tested and evaluated. The goal is the successful guidance of fabrics towards sewing by a robot. The robotic handling of fabrics, particularly sewing, is an area with model uncertainty, non-linearity, with very noisy force signals and fast varying characteristics. Since no similar work has been found in this area, this paper shows the wide application range of the FMRL approach.
