A simple control policy for achieving minimum jerk trajectories
Point-to-point fast hand movements, often referred to as ballistic movements, are a class of movements characterized by straight paths and bell-shaped velocity profiles. In this paper we propose a bangbang optimal control policy that can achieve such movements. This optimal control policy is accomplished by minimizing the L? L ? norm of the jerk profile of ballistic movements with known initial position, final position, and duration of movement. We compare the results of this control policy with human motion data recorded with a manipulandum. We propose that such bangbang control policies are inherently simple for the central nervous system to implement and also minimize wear and tear on the bio-mechanical system. Physiological experiments support the possibility that some parts of the central nervous system use bangbang control policies. Furthermore, while many computational neural models of movement control have used a bangbang control policy without justification, our study shows that the use of such policies is not only convenient, but optimal.
minimum jerk jerk ballistic movements bell-shape velocity profiles bangbang optimal control manipulandum infinity norm human movements motor control feedback effects