In this study, a Genetic Algorithm (GA) is proposed to search for the optimal input membership functions of the fuzzy logic controller. With the optimal membership function, the fuzzy logic controller can efficiently control a rotary inverted pendulum. The advantage of the proposed method is tuning the parameters of membership functions automatically rather than tuning them manually. In genetic algorithm, these parameters are converted to a chromosome which is encoded into a binary string. Because the membership functions are symmetric to zero, the length of each chromosome could be reduced by half. The computation time will also be shorter with the shorter chromosomes. Moreover, the roulette wheel selection is chosen as reproduction operator and one-point crossover operator and random mutation operator are also used. After the genetic algorithm completes searching for optimal parameters, the optimal membership function will be introduced to the fuzzy logic controller. Finally, simulation results show that the proposed GA-tuned fuzzy logic controller is effective for the rotary inverted pendulum control system with robust stabilization capability.
This study proposes the Radio Frequency Identification (RFID) indoor positioning and navigation method based on fuzzy neural network. The proposed method is applied to a wheelchair home health care robot with wireless communication. One reader and four tags are used. Based on the Received Signal Strength Indication (RSSI) data, the position of the robot can be determined. Further, to overcome the measurement error problem due to environmental parameter variation, a Fuzzy Neural Network (FNN) is proposed to compensate the measurement data. The FNN automatically adjust the weight, the variance and the mean value to overcome effectively the environmental parameter variation. A back-propagation algorithm is developed to achieve self-learning. The successful experiment results show that the proposed system architecture and positioning system provide satisfactory accuracy and make home health care wheelchair robot positioning system available for navigation and guidance.
This paper presents a robust proportional-derivative (PD) based cerebellar model articulation controller (CMAC) for vertical take-off and landing flight control systems. It is known that PD control is a simple and effective control method. However, it does not ensure the robustness if it is used alone for uncertain systems. CMAC can be used for robust control. However, it requires training patterns for tuning some weighting factors. A novel CMAC incorporating with a PD controller design is proposed in this paper. Successful on-line training and recalling process of CMAC accompanying the PD controller was developed. The advantage of the proposed method is mainly the robust tracking performance against aerodynamic parametric variation and external wind gust. Even when the PD controller is not designed well, the CMAC is capable of doing a robust tracking control through on-line recalling and training procedures.
In this paper, we propose an adaptive radio frequency identification (RFID) indoor positioning system technology for wheelchair home health care robot with wireless communication. The proposed RFID positioning system uses one reader and four tags which is low cost when applying in a large space of the indoor environment. It reduces the measured calculation by using multiple RFID tags instead of multiple RFID readers. While the measured experimental RFID data found with error leading to signal changes in different environmental parameters, we developed the adaptive fuzzy neural network technology to adjust the measurement data. Through the compensation of the measurement error, the actual wheelchair robot location-based application could be performed to overcome the uncertain environmental parameters. The positioning system provides very good accuracy and make home health care wheelchair robot positioning system available for navigation and guidance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.