Purpose of research. The present paper conserns the problem of using reaction predictors in the control system of bipedal walking robots. The main advantage of using predictors is the ability to exclude unknown reaction forces from the dynamics equations and, consequently, from the robot control problem statements based on the model. An additional advantage of predictor setting of control tasks is also discussed in the paper, namely the possibility of its use to predict changes in contact interaction modes, such as slipping motion or foot lifting from the supporting surface.Methods. The following methods are used in the research: the method of dynamics of multi-mass systems is necessary for developing a mathematical model of the behavior of a walking robot and describing its contact interaction with the support surface, the method of neural networks is used to develop a predictor that allows one to forecast the values of reactions between the robot’s foot and the surface.Results. The paper shows that there is a connection between the frequencies of the harmonic components of robot movements (the ratio p of these frequencies in the experiment and the training sample) and the quality of reactions predictor operation of the support surface. This indicates the importance of applying a representative spectrum of walking robot movement frequencies in forming a training sample, and the poor generalizability of the predictor in relation to movement frequency.Conclusion. The paper has considered the use of a reaction predictor to identify the possibility of changing the mode of contact interaction, based on the measurement of discrepancies between local linearizations for various discrete steps. The results obtained in this work will be used in the development of a motion control system for a bipedal walking robot, which allows the device to adapt to the parameters of the support surface on which the movement occurs.
Implementation Method of the Robot Adaptation to Contact Interaction Mode Changes Using Deep Fully Connected Neural Networks