Markovian Robust Compliance Control Based on Electromyographic Signals

In this paper, we deal with the human-robot interaction control problem. Levels of actuation of the user are considered in the human-robot interaction model from a stochastic point of view. It is given in terms of a Markovian approach. Electromyographic signals are used to compute jump parameters between different levels of interaction. In this way, human neuromuscular system defines the behavior of the Markov chain. A unified approach composed by robust Kalman filter and robust regulator for discrete-time Markovian jump linear systems is proposed. Also, a serious game is used to generate visual feedback and promote the active participation of the user. Experimental results show high accuracy in the Markovian compliance control for a robotic platform applied in ankle rehabilitation.