Internal model (motor control)

Résumé

In the subject area of control theory, an internal model is a process that simulates the response of the system in order to estimate the outcome of a system disturbance. The internal model principle was first articulated in 1976 by B. A. Francis and W. M. Wonham as an explicit formulation of the Conant and Ashby good regulator theorem. It stands in contrast to classical control, in that the classical feedback loop fails to explicitly model the controlled system (although the classical controller may contain an implicit model). The internal model theory of motor control argues that the motor system is controlled by the constant interactions of the “plant” and the “controller.” The plant is the body part being controlled, while the internal model itself is considered part of the controller. Information from the controller, such as information from the central nervous system (CNS), feedback information, and the efference copy, is sent to the plant which moves accordingly. Internal

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https://en.wikipedia.org/wiki/Internal_model_(motor_control)

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This course covers some theoretical and practical aspects of robust and adaptive control. This includes H-2 and H-infinity control in model-based and data-driven framework by convex optimization, direct, indirect and switching adaptive control. The methods are implemented in a hands-on lab.

Teaching objectives: history, upper limb and hand neuroprostheses, lower limb neuroprostheses, student project.

The course gives (1) a review of different types of numerical models of control of locomotion and movement in animals, (2) a presentation of different techniques for designing models, and (3) an analysis of the use and testing of those models in robotics and neuroprosthetics.

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This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balance in front of strong external perturbations and avoid collision with an external object. The key elements for this success are threefold. First, optimal control over a receding horizon is implemented with Crocoddyl, an optimal control library based on differential dynamics programming, providing state-feedback control in less than 10 msecs. Second, a warm start strategy based on memory of motion has been implemented to overcome the sensitivity of the optimal control solver to initial conditions. Finally, the optimal trajectories are executed by a low-level torque controller, feedbacking on direct torque measurement at high frequency. This paper provides the details of the method, along with analytical benchmarks with the real humanoid robot Talos.

IEEE2021

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Bio-inspired balance controller for a humanoid robot

François André Heremans, Auke Ijspeert, Renaud Ronsse, Nicolas Benoît Dominique Van der Noot

Humanoid robots are gaining much interest nowadays. This is partly motivated by the ability of such robots to replace humans in dangerous environments being specifically designed for humans, such as man-made or natural disaster scenarios. However, existing robots are far from reaching human skills regarding the robustness to external perturbations required for such tasks, although torque-controlled and even bio-inspired robots hold new promises for research. A humanoid robot robustly interacting with its environment should be capable of handling highly uncertain ground structures, collisions, and other external perturbations. In this paper, a 3D bio-inspired balance controller is developed using a virtual lower limbs musculoskeletal model. An inverse muscular model that transforms the desired torque patterns into muscular stimulations closes the gap between traditional and bio-inspired controllers. The main contribution consists in developing a neural controller that computes the muscular stimulations driving this musculoskeletal model. This neural controller exploits the inverse model output to progressively learn the appropriate muscular stimulations for rejecting disturbances, without relying on the inverse model anymore. Two concurrent approaches are implemented to perform this autonomous learning: a cerebellar model and a support vector regression algorithm. The developed methods are tested in the Robotran simulation environment with COMAN, a compliant child-sized humanoid robot. Results illustrate that - at the end of the learning phase - the robot manages to reject perturbations by performing a full-body compensation requiring neither to solve an inverse dynamic model nor to get force measurement. Muscular stimulations are directly generated based on the previously learned perturbations.

Ieee2016

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Combined Analysis of Cortical (EEG) and Nerve Stump Signals Improves Robotic Hand Control

Silvestro Micera, Jacopo Rigosa

Background. Interfacing an amputee's upper-extremity stump nerves to control a robotic hand requires training of the individual and algorithms to process interactions between cortical and peripheral signals. Objective. To evaluate for the first time whether EEG-driven analysis of peripheral neural signals as an amputee practices could improve the classification of motor commands. Methods. Four thin-film longitudinal intrafascicular electrodes (tf-LIFEs-4) were implanted in the median and ulnar nerves of the stump in the distal upper arm for 4 weeks. Artificial intelligence classifiers were implemented to analyze LIFE signals recorded while the participant tried to perform 3 different hand and finger movements as pictures representing these tasks were randomly presented on a screen. In the final week, the participant was trained to perform the same movements with a robotic hand prosthesis through modulation of tf-LIFE-4 signals. To improve the classification performance, an event-related desynchronization/synchronization (ERD/ERS) procedure was applied to EEG data to identify the exact timing of each motor command. Results. Real-time control of neural (motor) output was achieved by the participant. By focusing electroneurographic (ENG) signal analysis in an EEG-driven time window, movement classification performance improved. After training, the participant regained normal modulation of background rhythms for movement preparation (alpha/beta band desynchronization) in the sensorimotor area contralateral to the missing limb. Moreover, coherence analysis found a restored alpha band synchronization of Rolandic area with frontal and parietal ipsilateral regions, similar to that observed in the opposite hemisphere for movement of the intact hand. Of note, phantom limb pain (PLP) resolved for several months. Conclusions Combining information from both cortical (EEG) and stump nerve (ENG) signals improved the classification performance compared with tf-LIFE signals processing alone; training led to cortical reorganization and mitigation of PLP.

2012

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Combined Analysis of Cortical (EEG) and Nerve Stump Signals Improves Robotic Hand Control

Silvestro Micera, Jacopo Rigosa

2012