PI Institution: University of California-San Diego
The objective of this research is develop new algorithms for approximately-optimal control of complex dynamical systems. The approach combines inspiration from neuroscience with mathematical advances in control theory. The algorithms have a hierarchical structure reminiscent of the way the brain generates complex behavior. The lower level of the hierarchy augments the body and makes it easier to control. The higher level monitors progress and steers the system towards achievement of the common task. In this way the complexities due to the body are separated from those due to the task.
Intellectual merit The project includes two complementary classes of algorithms. The first class represents a significant advance in the theory of stochastic optimal control. A general family of problems are identified where the fundamental equations characterizing the optimal solution turn out to be linear, even though the controlled system is nonlinear. The second class of algorithms represents a practical framework for attacking high-dimensional nonlinear problems, particularly those that arise in biomechanics.
Broader impacts The proposed theoretical developments represent foundational work which is likely to have a lasting impact. The proposed numerical algorithms have the potential to extend the range of practically-solvable optimal control problems. Optimal control is of interest in many fields of science and engineering, including the recovery of motor function via brain-machine interfaces. Educational activities include mentoring of the graduate students funded by this proposal, as well as design and teaching of both graduate and undergraduate classes at the interface of Neuroscience and Engineering.
