Fuzzy Iterative Algorithm Research Articles

Robotic Curved Surface Tracking with a Neural Network for Angle Identification and Constant Force Control based on Reinforcement Learning

Aiming to solve the problem that the contact force at a robot end effector when tracking an unknown curved-surface workpiece is difficult to keep constant, a robot force control algorithm based on reinforcement learning is proposed. In this paper, a contact model and force mapping relationship are established for a robot end effector and surface. For the problem that the tangential angle of the workpiece surface is difficult to obtain in the mapping relationship, a neural network is used to identify the tangential angle of the unknown curved-surface workpiece. To keep the normal force of the robot end effector constant, a compensation term is added to a traditional explicit force controller to adapt to the robot constant force tracking scenario. For the problem that the compensation term parameters are difficult to select, the reinforcement learning algorithm A2C (advantage actor critic) is used to find the optimal parameters, and the return function and state values are modified in the A2C algorithm to satisfy the robot tracking scenario. The results show that the neural network algorithm has a good recognition effect on the tangential angle of the curved surface. The force error between the normal force and the expected force is substantially within ± 2 N after 60 iterations of the robot force control algorithm based on A2C; additionally, the variance of the force error decreases by 50.7%, 34.05% and 79.41%, respectively, compared with the force signals obtained by a fuzzy iterative algorithm and an explicit force control with two sets of fixed control parameters.

An approach for inventory routing under vendor managed inventory system

Vendor managed inventory is a collaborative business practice adopted by organisations to improve the business performance. Under this practice, the retailers share demand and other related information with the manufacturer, who in turn manages the inventory of the retailer. In such event, the manufacturer assumes the responsibility of taking the decisions about size and time of delivery, vehicle routing, etc. This study examines the vendor managed inventory practice, specifically inventory routing problem, for retailers and a manufacturer when there is a wide fluctuation of daily demand. We have addressed a special issue related to inventory replenishment in vendor managed inventory system, where demand exceeds the finite production capacity .We construct a numerical experiment in MATLAB to find the optimal daily demand distribution and vehicle routing using fuzzy min-max learning algorithm. First, for deciding the sequence in which the quantity to be delivered to the retailers, a fuzzy set hyperboxes are used .Then, assignment is done with the help of discounting coefficient. Lastly, routing is done using fuzzy iterative algorithm. The focus of this paper is on demonstrating the effectiveness of our approach and comparing the results with that of simple greedy heuristics.