Adaptive Fuzzy Control Applied to Seven-Link Biped Robot Using Ant Colony Optimization Algorithm

Referring to the fact that the nth links bipedal walking robot has high nonlinearity and uncertainty parameters; therefore robust controllers for walking robot should be properly designed. This paper proposes a new robust control scheme based on fuzzy system and Ant Colony Optimization algorithm. Adaptive fuzzy controllers (AFCs) based on Ant Colony Optimization (ACO) algorithm are proposed to eliminate the chattering phenomenon that occurs when the walking robot moves on rough surfaces. Six rotational joints are used to connect the seven links of the bipedal walking robot. Those joints are assumed frictionless and actuated by six independent servomotors. Therefore, six adaptive fuzzy controllers are designed in this work (one for each joint). To design robust fuzzy controllers, the Ant Colony Optimization algorithm is utilized to tune and find the best parameters of the output membership function of the fuzzy controller. For comparison purposes, optimal PID controllers (OPIDCs) are designed with optimal parameters that are chosen by utilizing the Ant colony algorithm. The performances of the two proposed controllers (AFCs and OPIDCs) are tested under significant disturbances situations such as carrying different weighted things by the walking biped robot. In addition, the stability of the adaptive fuzzy controller is studied and proved by applying Lyapunov theory.