Abstract
This study presents a field-programmable gate array (FPGA)-based mechatronic design and real-time fuzzy control method with computational intelligence optimization for omni-Mecanum-wheeled autonomous vehicles. With the advantages of cuckoo search (CS), an evolutionary CS-based fuzzy system is proposed, called CS-fuzzy. The CS’s computational intelligence was employed to optimize the structure of fuzzy systems. The proposed CS-fuzzy computing scheme was then applied to design an optimal real-time control method for omni-Mecanum-wheeled autonomous vehicles with four wheels. Both vehicle model and CS-fuzzy optimization are considered to achieve intelligent tracking control of Mecanum mobile vehicles. The control parameters of the Mecanum fuzzy controller are online-adjusted to provide real-time capability. This methodology outperforms the traditional offline-tuned controllers without computational intelligences in terms of real-time control, performance, intelligent control and evolutionary optimization. The mechatronic design of the experimental CS-fuzzy based autonomous mobile vehicle was developed using FPGA realization. Some experimental results and comparative analysis are discussed to examine the effectiveness, performance, and merit of the proposed methods against other existing approaches.
Highlights
Real-time fuzzy control is a modern control strategy for a variety of challenging control applications because it provides a convenient method for constructing nonlinear controllers [1,2,3]
This section is devoted to discussing the experimental results and comparative analysis to platform built around the Altera System-on-Chip (SoC) field-programmable gate array (FPGA)
This study has presented a FPGA-based mechatronic design and real-time fuzzy control method
Summary
Real-time fuzzy control is a modern control strategy for a variety of challenging control applications because it provides a convenient method for constructing nonlinear controllers [1,2,3]. This approach has significantly improved the performance by considering the control parameter tuning issue. The control parameters are self-tuned in this kind of real-time controller to achieve optimal performance using fuzzy theory. This approach provides a formal methodology for representing, manipulating, and implementing human heuristic knowledge to develop intelligent controllers [4,5,6]. The real-time fuzzy control method has been widely used in many real-world disciplines, there remain a number of drawbacks in the design stages. One major difficulty is the construction of the fuzzy model in terms of the center and width of the membership functions and fuzzy rules [5,6]
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