An intelligent fuzzy controller based on genetic algorithms

Khanian, Mahdi Yousefi Azar; Fakharian, Ahmad; Chegini, Mehran Godarzvand; Jozi, B.

doi:10.1109/cira.2009.5423157

Cited by 2 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [14,15] labels have been uniformly distributed, but the granularity of each input variable is defined using expert knowledge. On the other hand, in [13,17,18,19,21] an approximative approach is used, i.e., different membership functions are learned for each rule, reducing the interpretability of the learned controller.…”

Section: Related Workmentioning

confidence: 99%

“…Among the most popular approaches can be found evolutionary algorithms [4,5], neural networks [6] and reinforcement learning [7,8]. Also hibridations of them, like evolutionary neural networks [9], reinforcement learning with evolutionary algorithms [10,11], the widely used genetic fuzzy systems [12,13,14,15,16,17,18], or even more uncommon combinations like ant colony optimization with reinforcement learning [19] or differential evolution [20] or evolutionary group based particle swarm optimization [21] have been successfully applied. Furthermore, over the last few years, mobile robotic controllers have been getting some attention as a test case for the automatic design of type-2 fuzzy logic controllers [8,5,20].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Learning fuzzy controllers in mobile robotics with embedded preprocessing

Rodríguez-Fdez¹,

Mucientes²,

Bugarín³

2015

Applied Soft Computing

View full text Add to dashboard Cite

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Learning fuzzy controllers in mobile robotics with embedded preprocessing

Rodríguez-Fdez¹,

Mucientes²,

Bugarín³

2015

Applied Soft Computing

View full text Add to dashboard Cite

show abstract

Fuzzy PID control theory in multi-objective optimal longitudinal following of intelligent trolley

Sun

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

As a kind of mobile robot, the intelligent trolley is also called a wheeled robot, and this field is the fastest growing in the current technological development. Smart trolleys have good automatic guidance functions, often used in modern logistics conveying systems. A fuzzy control algorithm has a good control effect for nonlinear systems and has a strong anti-interference ability. In order to optimize the multi-objective longitudinal following system of smart carts, therefore, this paper develops the study of fuzzy PID control algorithm for multi-objective longitudinal following of smart carts. Based on the incremental PID control, an improved PID algorithm is constructed by introducing incomplete differentiation, differential prior, and “optimal curvature” algorithm. Based on the actual control experience, the fuzzy control table is designed, and the adaptive control algorithm is obtained. Finally, the simulation results of the fuzzy PID control algorithm are tested by MATLAB/Simulink, and the most suitable fuzzy PID control algorithm for multi-objective optimal longitudinal following of intelligent carts is derived.

show abstract

An intelligent fuzzy controller based on genetic algorithms

Cited by 2 publications

References 9 publications

Learning fuzzy controllers in mobile robotics with embedded preprocessing

Learning fuzzy controllers in mobile robotics with embedded preprocessing

Fuzzy PID control theory in multi-objective optimal longitudinal following of intelligent trolley

Contact Info

Product

Resources

About