Fuzzy Rule-based Learning Research Articles

A Hybrid Switched Reactive-Based Visual Servo Control of 5-DOF Robot Manipulators for Pick-and-Place Tasks

This paper addresses the problem of visual servo control for a five-degree-of-freedom robot manipulator to perform pick-and-place tasks. A new hybrid switched reactive-based visual servo control structure, inspired from a sensing-and-reaction behavior without the inverse interaction matrix computing, is proposed to handle this problem efficiently. The proposed structure is similar to the logic-based approaches, but the requirements of the fuzzy modeling and/or the fuzzy-rule base learning are omitted. To achieve this, a novel hardware-aware reactive function approach is presented to directly map an image position error vector to a desired end-effector velocity vector under the consideration of hardware limitations. This approach helps to simplify the implementation of visual servo systems with improved reliability. Moreover, the proposed control system is a hybrid switching controller consisting of both image-based and position-based reactive planning schemes, which allows improving the robustness and effectiveness of the visual servo system. Experimental results validate the performance of the proposed visual servo control method in a realistic scenario setting.

Modelling trip distribution with fuzzy and genetic fuzzy systems

This paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity- and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost.

Fuzzy rule base learning through simulated annealing

We study the use of simulated annealing to optimize the membership functions of Takagi-Sugeno rules. The necessary adaptation of simulated annealing in order to be efficient for this problem is discussed in detail. The convergence is carefully studied for the test application of the approximation of an analytical function specially built to test the efficiency of the algorithm. The obtained results are compared with gradient descent optimization results. We point out that simulated annealing is particularly interesting in the case (usual in practical implementations) when there are few rules compared to the complexity of the problem.

Scalability in fuzzy rule-based learning

Learning algorithms have been developed to construct fuzzy rule-based models from training data. The quality of the resulting model is affected by the decomposition of the input and output domains and by the number and precision of the training examples. This paper investigates the robustness of fuzzy models produced from training data. The objective is to analyze the effects of increasing complexity on the off-line performance of the learning algorithm and the on-line performance of the model, where the complexity is measured by the number of variables describing the problem domain and the number of rules in the model. A hierarchical model is proposed to reduce the complexity in high dimensional systems.

A fuzzy rule-based iterative learning control method with application to hydroforming processes

A pressure tracking control of hydroforming processes which is used for precision forming of sheet metals is considered in this paper. In this process, forming pressure of the process needs to be strictly controlled to ensure the high quality of the forming products. However, satisfactory control performance is often difficult to achieve using conventional control methods due to the complexities and the uncertainties of the process. To overcome the problems, a fuzzy rule-based learning control scheme is proposed. In the proposed scheme, a fuzzy rule base constructed from expert knowledge is adopted to generate an appropriate control input which will provide satisfactory pressure tracking performance of the hydroforming process. A series of experiments was performed to show the effectiveness of the proposed control scheme and to investigate the design parameters of the proposed algorithm. The experimental results show that the proposed fuzzy rule-based learning controller can guarantee good tracking performance and, thus, high quality of the products even when only a vague, imprecise and fragmentary knowledge of the process is available.