LAUSR

This paper proposes a control scheme for a N-DoF robotic manipulator in a joint-regulation motion problem, dealing with disturbances (as e.g. exogenous forces, unmodelled dynamics) that hinder task fulfilment, and also considering that not all the required states are available online. Existing literature tackles this problem through Disturbance Observer (DO) strategies which imply complex analysis and design methods or introducing strong assumptions. Conversely, we propose to formulate the system as a Linear Parameter Varying (LPV) model, which allows a straightforward application of the existing linear control structures but without neglecting its non-linear behaviour. We make use of the Robust Unknown Input Observer (RUIO) to obtain (for not measurable states) a decoupled estimation from the unknown disturbance effects, and improve its noise reduction capabilities through the new optimal RUIO design. The robotic manipulator is controlled with a state-feedback control law that, making use of the LPV paradigm, has been designed to seamlessly avoid torque saturation on manipulator’s joints through a gain shifting strategy that modifies its compliant behaviour. Stability and performance requirements are imposed in both RUIO and state-feedback control synthesis problems stated using the LMI framework, applying Polya’s theorems on positive forms of the standard simplex to reduce its overall conservatism. Experiments, using a simulated head system of the TIAGo robot as a testbed in various realistic scenarios, show the benefits when compared to the existing joint-independent PD control strategy and state-of-art EKF disturbance estimation.