A Friction Compensation Control for Power Steering

The effects of friction are critical to the dynamics of electric power steering (EPS). On the one hand, friction contributes to the stability of the system and filters some of the disturbances (road vibration, etc.). On the other hand, it affects negatively the driving feel and refrains from accurate positioning of the steering wheel. Also, for steering manufacturers, friction hinders the development and tuning of the assistance strategy. Therefore, being able to control or eventually suppress friction in EPS is a real issue. In this paper, a control strategy for the active compensation of friction in a column-assist type electric power steering (C- EPS) is presented. The assist motor and the electronic control unit are used to cancel the friction effect in order to imitate the behavior of an ideal, frictionless system. The feasibility of this strategy is demonstrated on the power column (the upper part of the steering system) using the same information (signals) as that available in an actual product. The proposed control is based on a model of the power column including slip and load dependent friction forces. For this purpose, a detailed simulation model, developed and validated in a previous work, is reduced into a lower-order model to enable real time computation. The LuGre model is used to compute both the static and dynamic friction forces with continuous formulation. The control architecture is composed of two cascaded feedback loops. The internal loop estimates the internal friction of the system and compensates it through the motor input. The external loop contains a frictionless reference model used as a trajectory planner and a linear controller which attempts to minimize the error between the plant response and the reference response. The stability and the robustness of the control strategy are analyzed formally. Specifically, it is shown that the limit error between the plant and the reference responses can be made arbitrarily small with appropriate values of the gains. Experi- mental results demonstrate that the control strategy is successful in tracking the frictionless, reference trajectory and confirm the robustness against inaccurate friction parameters.

The role of feedback in morphological computation with compliant bodies

Auke Ijspeert, Rolf Pfeifer

The generation of robust periodic movements of complex nonlinear robotic systems is inherently difficult, especially, if parts of the robots are compliant. It has previously been proposed that complex nonlinear features of a robot, similarly as in biological organisms, might possibly facilitate its control. This bold hypothesis, commonly referred to as morphological computation, has recently received some theoretical support by Hauser et al. (Biol Cybern 105:355-370, doi:10.1007/s00422-012-0471-0, 2012). We show in this article that this theoretical support can be extended to cover not only the case of fading memory responses to external signals, but also the essential case of autonomous generation of adaptive periodic patterns, as, e.g., needed for locomotion. The theory predicts that feedback into the morphological computing system is necessary and sufficient for such tasks, for which a fading memory is insufficient. We demonstrate the viability of this theoretical analysis through computer simulations of complex nonlinear mass-spring systems that are trained to generate a large diversity of periodic movements by adapting the weights of a simple linear feedback device. Hence, the results of this article substantially enlarge the theoretically tractable application domain of morphological computation in robotics, and also provide new paradigms for understanding control principles of biological organisms.

Springer Verlag2012

Implementation of a Human Feedback-based Locomotion and its Control by means of a Feedforward Component inspired by Central Pattern Generators

Florin Dzeladini

Walking gaits of various animals have been modeled using this framework of differential equations, and more specifically, using network of coupled oscillators or CPG (Central Pattern Generators). In these models, oscillators are coupled among themselves and thus influence each other. Nerve signals generating swimming in the Lampreys have been modeled using such a system. These oscillatory networks were successfully used to model several swimming animals. However, results obtained with walking animals have been rather disappointing. This is not surprising, since CPGs does not take into account interaction with the environment for shaping the movement, which seems to be more important for walking animals. In this project we study the interaction between feedback and CPGs in humans using a bio-inspired musculoskeletal model of human walking. We start with a pure feedback based model of human walking and extend it by introducing a feedforward component inspired by CPGs. We then test the properties of such a hybrid feedback and feedforward system. We show that, not only those new models are stable with characteristics close to the original model, but with online control they showed a clear increase of the robustness compared to pure feedback model. Moreover, modifications of some general parameters of the feedforward component allow easy changes in gait characteristics, such as gait speed.

2013

Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

Nicolas Benoît Dominique Van der Noot

Bipedal locomotion is a challenging task in the sense that it requires to maintain dynamic balance while steering the gait in potentially complex environments. Yet, humans usually manage to move without any apparent difficulty, even on rough terrains. This requires a complex control scheme which is far from being understood. In this thesis, we take inspiration from the impressive human walking capabilities to design neuromuscular controllers for humanoid robots. More precisely, we control the robot motors to reproduce the action of virtual muscles commanded by stimulations (i.e. neural signals), similarly to what is done during human locomotion. Because the human neural circuitry commanding these muscles is not completely known, we make hypotheses about this control scheme to simplify it and progressively refine the corresponding rules. This thesis thus aims at developing new walking algorithms for humanoid robots in order to obtain fast, human-like and energetically efficient gaits. In particular, gait robustness and richness are two key aspects of this work. In other words, the gaits developed in the thesis can be steered by an external operator, while being resistant to external perturbations. This is mainly tested during blind walking experiments on COMAN, a 95 cm tall humanoid robot. Yet, the proposed controllers can be adapted to other humanoid robots. In the beginning of this thesis, we adapt and port an existing reflex-based neuromuscular model to the real COMAN platform. When tested in a 2D simulation environment, this model was capable of reproducing stable human-like locomotion. By porting it to real hardware, we show that these neuromuscular controllers are viable solutions to develop new controllers for robotics locomotion. Starting from this reflex-based model, we progressively iterate and transform the stimulation rules to add new features. In particular, gait modulation is obtained with the inclusion of a central pattern generator (CPG), a neural circuit capable of producing rhythmic patterns of neural activity without receiving rhythmic inputs. Using this CPG, the 2D walker controllers are incremented to generate gaits across a range of forward speeds close to the normal human one. By using a similar control method, we also obtain 2D running gaits whose speed can be controlled by a human operator. The walking controllers are later extended to 3D scenarios (i.e. no motion constraint) with the capability to adapt both the forward speed and the heading direction (including steering curvature). In parallel, we also develop a method to automatically learn stimulation networks for a given task and we study how flexible feet affect the gait in terms of robustness and energy efficiency. In sum, we develop neuromuscular controllers generating human-like gaits with steering capabilities. These controllers recruit three main components: (i) virtual muscles generating torque references at the joint level, (ii) neural signals commanding these muscles with reflexes and CPG signals, and (iii) higher level commands controlling speed and heading. Interestingly, these developments target humanoid robots locomotion but can also be used to better understand human locomotion. In particular, the recruitment of a CPG during human locomotion is still a matter open to debate. This question can thus benefit from the experiments performed in this thesis.

EPFL2017

Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

Nicolas Benoît Dominique Van der Noot

EPFL2017