Benoît Huard

Sensorless Force/Position Control of a Single-Acting Actuator Applied to Compliant Object Interaction

This paper deals with a sensorless control approach of a lightweight electric actuator. Force and motion control are necessary to perform manipulation tasks with object interaction of different stiffnesses. The controller must ensure stability in a wide range of rigidity, particularly in soft contact cases. The approach proposed here is based on a force/position control scheme. First, a nonlinear position controller is performed through a linear parameter-varying state feedback. A state observer is added to estimate unmeasurable variables coming from an absence of terminal sensors. Subsequently, an output feedback force control is determined using an H∞ framework. The controller is experimentally applied on a single-acting actuator to demonstrate the efficiency of this approach dedicated to compliant object interaction.

Novel self-sensing actuated joint for robotic hands

This work presents the first steps towards the development of an anthropomorphic and dexterous hand with adjustable compliance. In this article, we primarily focus on the mechatronic design, modeling and testing of a novel self-sensing actuated joint. Self-sensing characteristic is achieved by specific design of highly back-driveable and efficient actuators based on a DC motor and a ball-screw, together with a novel flexible pivot providing a frictionless, backlash-free mechanical structure. Thus, forces applied to the finger produce an accurate image on the motor current which can be measured. Based on this feature, external forces applied to the finger can be estimated from the motor current measurement. Overall, this mechatronic design contributes to the improvement of manipulation skills of robotic hands, thanks to the combination of high performance mechanics, high sensitivity to external forces and compliance control capability.

LPV Modeling and Experimental Identification of a New Self-Sensing Finger Joint

Abstract This paper deals with the development of a new finger articulation prototype used as a joint for anthropomorphic robotic hand. We focus on modeling strategies applied to the finger prototype and its experimental identification. Self-sensing characteristic is achieved by an optimal design of highly back-driveable and efficient electromechanical actuators based on a DC motor, which requires an accurate modeling due to the absence of external sensors. The non-linear model of a finger joint can be reformulated as a Linear Parameter-Varying (LPV) model. On the contrary of classic method used for modeling, a quasi-LPV model is derived from Linear Time Invariant (LTI) interpolation, using a simulated annealing algorithm. Then, our proposed quasi-LPV model is validated from the Inverse Dynamic Model (IDM), and shows a good efficiency in predicting the non-linear behavior of the end effector.

Force-Sensing Actuator with a Compliant Flexure-Type Joint for a Robotic Manipulator

This paper deals with the mechatronic design of a novel self-sensing motor-to-joint transmission to be used for the actuation of robotic dexterous manipulators. Backdrivability, mechanical simplicity and efficient flexure joint structures are key concepts that have guided the mechanical design rationale to provide the actuator with force sensing capabilities. Indeed, a self-sensing characteristic is achieved by the specific design of high-resolution cable-driven actuators based on a DC motor, a ball-screw and a monolithic compliant anti-rotation system together with a novel flexure pivot providing a frictionless mechanical structure. That novel compliant pivot with a large angular range and a small center shift has been conceived of to provide the inter-phalangeal rotational degree of freedom of the fingers’ joints to be used for integration in a multi-fingered robotic gripper. Simultaneously, it helps to remove friction at the joint level of the mechanism. Experimental tests carried out on a prototype show an accurate matching between the model and the real behavior. Overall, this mechatronic design contributes to the improvement of the manipulation skills of robotic grippers, thanks to the combination of high performance mechanics, high sensitivity to external forces and compliance control capability.

Multi-model observer for position estimation and object contact detection of a flexible robotic actuator

This paper proposes a multi-model based approach that estimates joint position and collision detection of a lightweight electric actuator dedicated to manipulation tasks. Taking advantage of an optimized back-drivable mechatronic design, the use of proprioceptive measures at the motor level enables the estimation of the unmeasured terminal position of the mechanical motor-to-joint transmission and force contact disturbance. An accurate model of the actuated mechanical flexible transmission is theoretically introduced to precisely describe its non-linear dynamic behavior, and is reformulated into a polytopic form. Then, a multi-model observer-based estimator is synthesized for object contact detection strategy and joint position estimation without additional position/force sensor. A root-clustering synthesis is performed through Linear Matrix Inequalities (LMIs) and is experimentally validated with a single degree-of-freedom manipulator.

Multi-model observer and state feedback position control of a flexible robotic actuator

This paper proposes a state feedback control of a lightweight electric actuator dedicated to manipulation tasks. Taking advantage of an optimized back-drivable mechatronic design, the use of proprioceptive measures at the motor level enables the estimation of the unmeasured terminal position of the mechanical motor-to-joint transmission and force disturbance. An accurate model of the actuated mechanical flexible transmission is introduced to determine unmeasurable states with a model-based observer. The observer and the controller are synthesized by tacking into account the non-linear dynamic behavior of the mechatronic system. Non-linearities are reformulated into a polytopic multi-model, and dynamic performances of the controlled system are fixed by root-clustering. This synthesis is performed through Linear Matrix Inequalities (LMIs) and is experimentally validated with a single degree-of-freedom manipulator.