Backstepping Holonomic Tracking Control of Wheeled Robots Using an Evolutionary Fuzzy System with Qualified Ant Colony Optimization

Abstract This paper presents a backstepping holonomic tracking control method of embedded wheeled robots using an evolutionary fuzzy system with qualified ant colony optimization (ACO). The Taguchi method is applied to design an optimal ACO via an orthogonal array. This qualified ACO is then employed to develop an evolutionary fuzzy system, called FS-TACO. The proposed hybrid intelligent fuzzy system FS-TACO is realized in a field-programmable gate array (FPGA) to dynamic tracking control of three-wheeled holonomic mobile robots. In comparison with conventional control systems, this approach takes the advantages of Taguchi quality method, fuzzy system, ACO and FPGA technique, thereby obtaining better population diversity, avoiding premature convergence, and achieving self-adaptive holonomic control. The FPGA realization using system-on-a-programmable chip methodology of the proposed FS-TACO is more effective in practice for real-world embedded applications. Experimental results and comparative works are conducted to exhibit the merits of the proposed FPGA-based FS-TACO controller for three-wheeled holonomic mobile robots.