טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
M.Sc Thesis
M.Sc StudentVolkinshtein Dmitry
SubjectOn the Existence and Feasibility of a Forward Model in
Motor Control
DepartmentDepartment of Electrical Engineering
Supervisor Professor Ron Meir
Full Thesis textFull thesis text - English Version


Abstract

Biological motor control provides highly effective solutions to difficult control problems in spite of the complexity of the plant and the significant delays in sensory feedback. Such delays are expected to lead to non trivial stability issues and lack of robustness of control solutions. However, such difficulties are not observed in biological systems under normal operating conditions. Based on early suggestions in the control literature, a possible solution to this conundrum has been the suggestion that the motor system contains within itself a forward model of the plant (e.g., the arm), which allows the system to `simulate' and predict the effect of applying a control signal. It is believed in the neuroscience community that the cerebellum is used as a forward model. In the first part of this work we formally define the notion of a forward model for control problems, and provide simple conditions that imply its existence for tasks involving delayed feedback control in deterministic and stochastic cases. As opposed to previous work which dealt mostly with linear plants and quadratic cost functions, our results apply to rather generic control systems, showing that any controller (biological or otherwise) which solves a set of tasks, must contain within itself a forward plant model. We suggest that our results provide strong theoretical support for the necessity of forward models in many delayed control problems, implying that they are not only useful, but rather, mandatory, under general conditions.


The second part of the work is dealing with a specific model of the cerebellum that uses Radial Basis Function type of neural network. We investigate the performance of such network as a function approximation algorithm by finding the upper bounds of the prediction error, which are found to be low enough to conclude that such a model can perform well as a forward model.