A brief survey of observers for disturbance estimation and compensation

Abstract

An accurate dynamic model of a robot is fundamentally important for a control system, while uncertainties residing in the model are inevitable in a physical robot system. The uncertainties can be categorized as internal disturbances and external disturbances in general. The former may include dynamic model errors and joint frictions, while the latter may include external payloads or human-exerted force to the robot. Disturbance observer is an important technique to estimate and compensate for the uncertainties of the dynamic model. Different types of disturbance observers have been developed to estimate the lumped uncertainties so far. In this paper, we conducted a brief survey on five typical types of observers from a perspective of practical implementation in a robot control system, including generalized momentum observer (GMO), joint velocity observer (JVOB), nonlinear disturbance observer (NDOB), disturbance Kalman filter (DKF), and extended state observer (ESO). First, we introduced the basics of each observer including equations and derivations. Two common types of disturbances are considered as two scenarios, that is, constant external disturbance and time-varying external disturbance. Then, the observers are separately implemented in each of the two simulated scenarios, and the disturbance tracking performance of each observer is presented while their performance in the same scenario has also been compared in the same figure. Finally, the main features and possible behaviors of each type of observer are summarized and discussed. This survey is devoted to helping readers learn the basic expressions of five typical observers and implement them in a robot control system.

Hybrid Adaptive Impedance and Admittance Control Based on the Sensorless Estimation of Interaction Joint Torque for Exoskeletons: A Case Study of an Upper Limb Rehabilitation Robot

“…Visual servo control [36] is a promising alternative for manipulation but does not provide a complete perception of interaction force. To avoid the use of sensors, different methods have been proposed to estimate the interaction force/torque, e.g., extended Kalman filters [37][38][39][40][41], adaptive Kalman filters [42], extended state observers [43][44][45][46][47][48][49][50], disturbance observers [51,52], nonlinear observers [53], deep neural networks [54], model-based compensation techniques [55,56], task-oriented models based on dynamic model learning and a robust disturbance state observer [57], a sensorless force estimation method using a disturbance observer and the neural learning of friction [58], and extended Kalman filters [59].…”

“…The performance of the proposed HAIPC was compared with those of HIPCSW [15] and HIPC [35] to evaluate the effectiveness of the proposed control. The desired interaction joint torque is represented in Equation (47), which represents the physical human-robot interaction joint torque at the shoulder and elbow joints. In real time, the magnitude of the interaction joint torque is unknown; therefore, in this work, its values are estimated using ESO along the joint angular position.…”

“…In real time, the magnitude of the interaction joint torque is unknown; therefore, in this work, its values are estimated using ESO along the joint angular position. However, for simulation purposes, a known human interaction joint torque is used, as given in Equation (47). The magnitude of the interaction joint torque differs from patient to patient depending on the level of recovery and frequency of therapy.…”

“…Herein, the human-robot interaction is of two modes: passive mode, assuming no interaction force (zero torque) applied from the human; and active mode, where different magnitudes of torque are applied. (47) The passive mode runs when the patient's limbs are completely driven by the exoskeleton's motors during physical therapy along the desired angular trajectories. In this case, the input control torque is closer to admittance position control because there is soft contact between the exoskeleton and human limbs.…”