Soft robotics is a new field that uses actuators that are non-standard and compatible materials. Industrial robotics is high-throughput manufacturing devices that are quick and accurate. They are built on rigid-body mechanisms. The advancement of robotic production now depends on the inclusion of staff in manufacturing processes, allowing for the completion of activities that need cognitive abilities that are now beyond the scope of artificial networks. Hydrostatic pressure is used to achieve high deflections of structures that are based on the elastomeric in Fluid Actuators (FAs). Soft actuators based on the fluid are a popular choice safe for humans and lightweight robots. However, owing to a deficiency of durable, accurate, and affordable sensors that can be combined with actuator systems that are highly deformable and that use low-cost materials and production, closed-loop management of such actuators remains difficult. Such actuators, in combination with hydrodynamic force feedback, form a series-elastic actuation (SEA), which eliminates virtually friction from all driving-point. Fuzzy control is a smart computing analysis technique that enables complex systems to be controlled independently of a mathematical model. Fuzzy logic is used to optimize the parameters of a Fuzzy Logic Controller (FLC's) function to find the best rational controller for an automated robot. Because discontinuous endpoint friction is undetectable to the pressure of the fluid internally, feedback from traditional external force using force/tactile sensing is preferred. As a result, a fuzzy-based control using linear feedback was developed and used to test the integrated system's response dynamically and location accuracy.

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The academic performance assessment of students helps teachers, administrators, and policymakers to initiate corrective measures on academically poor students. This paper revisits the Zadeh-Deshpande formalism for evaluating students’ answer scripts using the concept of the reliability of information (degree of confidence) via the “degree of match” and fuzzy inference system in students’ performance evaluation. The case study infers that the overall performance of all the students is “average”. Furthermore, 206 of 237 students (87%) are declared as “average” with “high degree of certainty” by the evaluators (teachers). The aim of the proposed method is not to replace the traditional method of evaluation. Instead, the proposed technique is a step forward to enrich the present system of students’ performance assessment. Policymakers can use this method as it provides reliable information. A comparison between the results obtained from the academic performance of students using multiexpert and single-expert systems is also discussed in the paper.

Examinee and examiner plays a pivotal role in educational grading system. Perception of experts in evaluating student's answer script is of vital significance. Multiple experts evaluating students' academic performance involves epistemic uncertainty which can be modelled using fuzzy set theory. How many evaluators/experts are almost similar in their perceptual subjective evaluation of the students answer paper? In other words, how many experts are reliable for a particular evaluation task with defined possibility level? The paper addresses the issue of the selection of similar examiners using fuzzy relational calculus. The case study reveals that all twenty evaluators agree to one another considering the similarity measures on fuzzy sets. The computed Fleiss Kappa coefficient is 0.74 which signifies excellent agreement between all the twenty experts.

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