Imperial College London Featured PhD Programmes
Sheffield Hallam University Featured PhD Programmes
Norwich Research Park Featured PhD Programmes
University of Kent Featured PhD Programmes
Max Planck Society Featured PhD Programmes

Movement control using dynamics matching and anticipatory coupling

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  • Full or part time
    Prof S Nasuto
    Dr Y Hayashi
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

"There is a lot of interest in explaining motor coordination and control. Such models allow for better understanding of the healthy coordination and provide diagnostic tools and starting points for rehabilitation solutions in case of motor disorders due to stroke or neural degeneration.

Classical approaches are based on control theoretic models and treat the problem from the perspective of steering signals that need to be delivered to actuators in order for the system to achieve the desired trajectory. Such formulation is devoid of dynamics and formulates the problem as that of identification of a drive for a nonautonomous system. In such a formulation any delays between the issued signal and the externally determined response timing are problematic (the controlled signal is always reactive and lags the tracked target). In order to overcome this, the most common solution is coached in terms of internal predictive models. These tend to be computational predictive constructs, posing huge computational demands on the natural system that would have to implement such solution. However, one could reframe the problem – by seeking a solution to the control problem as a result of interaction between two coupled dynamical systems. The target trajectory would be a result of such dynamical coupling and interaction rather than some internal computations. The recently proposed dynamical phenomenon of anticipating synchronisation could provide hints at how such a system could deal with any forms of delays and achieve fast reaction times.

This project will investigate how anticipating synchronisation or similar anticipatory coupling schemes could be used in order to achieve the control law that matches dynamics, in a way analogous to impedance control, and tracks the target trajectory. As the impedance in the workspace will generally depend on the posture, it will change in a complex way along the trajectory. The problem thus may be to track and anticipate the changes of the impedance in the space of possible dynamics. The theoretical models of control will be derived and characterised analytically and in simulation of a 2 armed robot in order to achieve broad analogy with human arm control. This will enable to devise the experiments that could in principle provide data for evaluation of the proposed control schemes. The project offers an opportunity to focus either on theoretical developments of novel control strategies inspired by cognitive theories and combining functional analysis and modern control or built more practical solutions. The focus will be tailored to strengths and preferences of the candidate.

The project will be hosted by the School of Biological Sciences, University of Reading. The University of Reading is one of the UK’s 20 most research-intensive universities and among the top 200 universities in the world. Achievements include the Queen’s Award for Export Achievement (1989) and the Queen’s Anniversary Prize for Higher Education (1998, 2006 and 2009). This project will take place in the Brain Embodiment Lab within Biomedical Engineering Section of the School of Biological Sciences (SBS), which has a strong reputation for its innovative research in cybernetics, and biomedical engineering, including Brain Computer Interfaces, animats - robots controlled by cultures of living neuronal cells and cognitive robotics systems.

For informal inquiries please contact Prof SJ Nasuto, email: [Email Address Removed].

Funding Notes

Eligibility requirements:

Applicants should have a bachelors (at least 2.1 or equivalent) or masters degree in physics,
applied mathematics, engineering, computing or a strongly related discipline.
Strong analytic and programming skills are preferable.
Experience in image processing and experimental data analysis are desirable.

FindAPhD. Copyright 2005-2019
All rights reserved.