PhD Fellow @ Institut de Robòtica e Informática Industrial (CSIC-UPC)
Disturbance Observer-based LPV Feedback Control of a N-DoF Robotic Manipulator including Compliance through Gain Shifting
Abstract - 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 on-line. Existing literature tackles this problem through Disturbance Observer (DO) strategies which imply complex analysis and design methods or assuming 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 the manipulator' 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.
Keywords - Robotic system; Linear parameter varying; Unknown-input observer; Shifting paradigm; Robustness; Service robot