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This paper proposes a new reinforcement-learning method using online rule generation and Q-value-aided ant colony optimization (ORGQACO) for fuzzy controller design. The fuzzy controller is based on an interval type-2 fuzzy system (IT2FS). The antecedent part in the designed IT2FS uses interval type-2 fuzzy sets to improve controller robustness to noise.… (More)
—This paper proposes a cooperative continuous ant colony optimization (CCACO) algorithm and applies it to address the accuracy-oriented fuzzy systems (FSs) design problems. All of the free parameters in a zero-or first-order Takagi–Sugeno–Kang (TSK) FS are optimized through CCACO. The CCACO algorithm performs optimization through multiple ant colonies,… (More)
This paper proposes a multi-objective, rule-coded, advanced, continuous-ant-colony optimization (MO-RACACO) algorithm for fuzzy controller (FC) design and its application to multi-objective, wall-following control for a mobile robot. In the MO-RACACO-based FC design approach, the number of rules and all free parameters in each rule are optimized using the… (More)
pheromone trails. The RSOIT2FS-ACO method is applied to a truck backing control. The proposed RSOIT2FS-ACO is compared with other reinforcement fuzzy systems to verify its efficiency and effectiveness. A comparison with type-1 fuzzy systems verifies the robustness of using type-2 fuzzy systems to noise.