A minimum attention control law for ball catching

Digital implementations of control laws typically involve discretization with respect to both time and space, and a control law that can achieve a task at coarser levels of discretization can be said to require less control attention, and also reduced implementation costs. One means of quantitatively capturing the attention of a control law is to measure the rate of change of the control with respect to changes in state and time. In this paper we present an attention-minimizing control law for ball catching and other target tracking tasks based on Brockett's attention criterion. We first highlight the connections between this attention criterion and some well-known principles from human motor control. Under the assumption that the optimal control law is the sum of a linear time-varying feedback term and a time-varying feedforward term, we derive an LQR-based minimum attention tracking control law that is stable, and obtained efficiently via a finite-dimensional optimization over the symmetric positive-definite matrices. Taking ball catching as our primary task, we perform numerical experiments comparing the performance of the various control strategies examined in the paper. Consistent with prevailing theories about human ball catching, our results exhibit several familiar features, e.g., the transition from open-loop to closed-loop control during the catching movement, and improved robustness to spatiotemporal discretization. The presented control laws are applicable to more general tracking problems that are subject to limited communication resources.