Sliding mode based fault diagnosis with deep reinforcement learning add‐ons for intrinsically redundant manipulators

Abstract

AbstractThis article presents a fault diagnosis control scheme for intrinsically redundant robot manipulators based on the combination of a deep reinforcement learning (DRL) approach and a battery of sliding mode observers. The DRL plays the role of detecting and isolating possible sensor faults, thus generating an alarm and pin‐pointing the source. This in turn allows to compensate the sensor faults independently from the actuator ones. The latter are therefore detected and isolated by a set of sliding mode observers driven by input laws designed according to an optimal reaching algorithm. In order to design and apply such observers, a global feedback linearization is performed, which transforms the multi‐input‐multi‐output (MIMO) nonlinear robot model into a chain of double integrators. The proposal is analyzed and assessed in realistic conditions using the PyBullet environment in which a 7 degrees‐of‐freedom (DOFs) Franka Emika Panda robot manipulator is reproduced.