ype-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller （IT2FLC） to control a f...ype-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller （IT2FLC） to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization （PSO）. A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller （T1FLC） is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.展开更多
文摘ype-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller （IT2FLC） to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization （PSO）. A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller （T1FLC） is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.
