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.

This paper presents the concept of convex lifting which will be proven to enable significant implementation benefits for the class of piecewise affine controllers. Accordingly, two different algorithms to construct a convex lifting for a given polyhedral/polytopic partition will be presented. These two algorithms rely on either the vertex or the halfspace representation of the related polyhedra. Also, we introduce an algorithm to refine a polyhedral partition, which does not admit a convex lifting, into a convexly liftable one. Furthermore, two different schemes will be put forward to considerably reduce both the memory footprint and the online evaluation effort, which play a key role in implementation of piecewise affine controllers. Finally, these results will be illustrated via numerical examples and a complexity analysis.

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 robot's 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.