Maria Makarov

Generalized Predictive Control of an anthropomorphic robot arm for trajectory tracking

This paper presents an effective model-based predictive approach for the precise trajectory tracking of an anthropomorphic robot arm. The proposed control strategy is based on feedback linearization and linear Generalized Predictive Control, requiring no on-line optimization procedure. Experimental evaluation of the proposed method and its comparison with two classic robot control approaches illustrate its tracking performances and robustness with respect to non-compensated load variations.

Adaptive Filtering for Robust Proprioceptive Robot Impact Detection Under Model Uncertainties

In the context of safe human-robot physical interaction, this paper introduces a new method for the detection of dynamic impacts of flexible-joint robot manipulators with their environment. The objective is to detect external impacts applied to the robot using only proprioceptive information with maximal sensitivity. Several model-based detection methods in robotics are based on the difference, called residual, between the estimated and the actual applied torques. Sensitivity of such methods can be limited by model uncertainties that originate either from errors on experimentally identified model parameters, possibly varying with the operating conditions, or the use of simplified models, which results in a residual dependence on the robots state. The main contribution of this paper consists of a new adaptive residual evaluation method that takes into account this dependence, which otherwise can lead to a tradeoff between sensitivity and false alarm rate. The proposed approach uses only proprioceptive motor-side measurements and does not require any additional joint position sensors or force/torque sensors. Dynamic effects of a collision on the residual are isolated using bandpass filtering and comparison with a state-dependent dynamic threshold. Adaptive online estimation of filter coefficients avoids the need for extensive experiments for parametric model identification. Experimental evaluation on the CEA backdrivable ASSIST robot arm illustrates the enhancement of the detection sensitivity.

A Frequency-Domain Approach for Flexible-Joint Robot Modeling and Identification

This paper proposes a control-oriented modeling and identification framework for flexible-joint robot arms using motor-side measurements only. From the perspective of model-based control strategies including an inner feedback linearization loop, the proposed method allows an explicit treatment of the vibrational behavior induced by the flexibilities. A theoretical model of the partially decoupled system is derived and a frequency-domain identification procedure allowing an estimation of the flexible parameters is detailed. The obtained description of the system is experimentally validated on the CEA lightweight robot arm ASSIST.

Modeling and Preview

This paper describes a novel approach combining identification and control design for motion control of multiple-link elastic-joint robots with motor sensors only and in presence of model uncertainties. The proposed model-based control design method makes use of the H∞ (H-infinity) framework to design a two-degree-of-freedom controller with anticipation able both to 1) withstand uncertainties or variations in model parameters and 2) follow reference trajectories with prescribed precision thanks to a preview feedforward action which anticipates the future trajectory on a given time horizon. The proposed design methodology is experimentally evaluated on a two-degree-of-freedom lightweight robotic arm, which is first modeled and identified in the frequency domain. Experimental validation of the controller confirms that the objectives of the dynamic precision in trajectory tracking and tip vibration damping are both achieved. Additional analysis and numerical simulations illustrate how the presented preview H∞ controller may be seen as an extension, with supplementary design parameters, of the traditional motor feedback with compensations based on the robot inverse dynamic model. A performance comparison between the proposed control method and the traditional inversion-based control shows the benefits of the anticipatory action and the possibilities offered by an H∞ design framework for the management of tradeoffs in the specifications.

H_\infty

This paper proposes a practical approach for damping control of flexible-joint manipulators. High-bandwidth tracking objectives are achieved together with robustness and effective disturbance rejection using motor measurements only. An inner decoupling loop based on the rigid-body approximation is first applied to minimize the effects of robot nonlinearities. The consequences of this approximate linearization are then explicitly taken into account in the control design and analysis, and a simple decentralized linear control strategy is proposed, combining low-authority collocated damping controllers with a high-authority motion loop based on the Generalized Predictive Control. The described approach is experimentally validated on the lightweight and back-drivable CEA ASSIST robot arm.

Control Design for Motion Control of Elastic-Joint Robots With Uncertainties

This paper presents an innovative collision detection strategy for robot manipulators in the context of the human-robot interaction. Classical approaches consisting of a comparison of the applied motor torques with those provided by a dynamic model can be sensitive to model uncertainties, leading to conservative detection thresholds. In this work, a “gray-box” model is designed based on a use-case study to shape the on-line evaluation of the residuals. This approach takes into account unstructured uncertainties relative to the speed-dependent non-linearities (e.g. friction phenomena) and the acceleration, both of particular interest when dealing with highly time-varying dynamics. Taking advantage of proprioceptive measurements of the robot state, the residual is adaptively filtered regarding these model uncertainties, and the evaluation step is improved by considering a dynamic threshold. The proposed multi-variable algorithm is implemented on the CEA robot arm ASSIST and the experimental results illustrate the enhancement of the detection sensitivity.

Active damping strategy for robust control of a flexible-joint lightweight robot

This paper presents a new control scheme for dexterous manipulation of an object by a multifingered hand. The effects of the uncertainties on the contact orientation and location are investigated and taken into account in the control design, allowing to manipulate the object robustly. The control law has three main objectives: (i) ensuring the motion control of the object, (ii) satisfying the constraints of the manipulation system, and (iii) being robust to the uncertainties on the contact point. The proposed control is based on a state feedback architecture with robust pole placement by an LMI approach. The controller is designed offline and can be related to an object-level impedance controller. The constraints of the manipulation system, e.g. the friction constraints, are taken into account with an additional control action, based on an online LMI evaluation. Simulation results are presented and demonstrate that the proposed control law ensures the three main objectives.

Adaptive residual filtering for safe human-robot collision detection under modeling uncertainties

This paper presents an analysis of object grasping with a multifingered robot hand. Uncertainties on the contact orientation and location are investigated and taken into account to find a new grasp quality measure. This new metric allows to evaluate the controllability of the system and find the Reachable Wrench Space under Uncertainties, a new set introduced in this paper. It includes all the wrenches a grasp can apply whatever the uncertainty is, and gives information about the robustness of the grasp relative to disturbances. Two algorithms are proposed to estimate this new set. Several study cases are presented and tested in simulation to illustrate the new quality metric.

Object-level impedance control for dexterous manipulation with contact uncertainties using an LMI-based approach

This interdisciplinary study aims to understand and model human motor control principles using automatic control methods, with possible applications in robotics for tasks involving a rhythmic interaction with the environment. The paper analyses the properties of a candidate model for the visual servoing of the 1D bouncing ball benchmark task in humans. The contributions are twofold as they i/ enable a computationally efficient way of testing hypotheses in human motor control modeling, and ii/ will allow to export and adapt the lessons learned from this modeling of human behavior for more robust and less model-dependent robotic control methods. Three hypotheses about the sensorimotor couplings involved during the task, i.e. three control structures are analyzed from the point of view of task stability by means of Poincaré maps. Obtained results are used to refine the proposed models of sensorimotor couplings. It is shown that the fixed points of the Poincaré maps are stable and that the obtained linear approximation, derived on these equilibrium points, can be viewed as a state-feedback. As such, the human-like controller is compared to the Linear Quadratic controller around the equilibrium point.

New metric for wrench space reachability of multifingered hand with contact uncertainties

The present paper investigates the sensory-driven modulations of central pattern generator dynamics that can be expected to reproduce human behavior during rhythmic hybrid tasks. We propose a theoretical model of human sensorimotor behavior able to account for the observed data from the ball-bouncing task. The novel control architecture is composed of a Matsuoka neural oscillator coupled with the environment through visual sensory feedback. The architectures ability to reproduce human-like performance during the ball-bouncing task in the presence of perturbations is quantified by comparison of simulated and recorded trials. The results suggest that human visual control of the task is achieved online. The adaptive behavior is made possible by a parametric and state control of the limit cycle emerging from the interaction of the rhythmic pattern generator, the musculoskeletal system, and the environment.NEW & NOTEWORTHY The study demonstrates that a behavioral model based on a neural oscillator controlled by visual information is able to accurately reproduce human modulations in a motor action with respect to sensory information during the rhythmic ball-bouncing task. The model attractor dynamics emerging from the interaction between the neuromusculoskeletal system and the environment met task requirements, environmental constraints, and human behavioral choices without relying on movement planning and explicit internal models of the environment.