Coordinated Natural Rhythmic Movements by Distributed Biological Oscillators

University Of Virginia Main Campus, Charlottesville VA

Investigators

Abstract

The overall goal of this project is to understand the biological control mechanism underlying natural rhythmic movements observed during animal locomotion, and to establish a design principle for engineering applications. In particular, we focus on the distributed control mechanism realized by neuronal oscillators called the central pattern generators (CPGs), and aim to establish a design theory for CPG-based feedback controllers to robustly achieve natural rhythmic movements of mechanical systems. We hypothesize that entrainment to a natural mode of oscillation can be robustly achieved for the closed-loop system by placing a CPG unit in the feedback loop between each collocated sensor/actuator pair of a multi-degree-of-freedom, lightly damped, mechanical system. We will develop analytical formulas for the control design parameters to achieve an oscillation with a given frequency and mode shape, using the method of multivariable harmonic balance (MHB). We will then examine whether the hypothesis is supported by the predictions from the MHB analysis. Feedback controls are essential for many engineering applications in which we desire to adjust movements of a physical system. Current technologies enable extremely fast and accurate motions with the aid of computer-controls. However, such control devices are only good at following a command, and lack sophistication to "think" what to do when the situation changes. For instance, we would walk differently on ice so that we don't slip or fall, but a walking robot would not be able to adjust its motion. This is because the motion is planned ahead assuming a nominal situation, and a controller forces the robot to move as planned even when the situation changes. In biological systems, appropriate motions are planned in real time by CPGs, based on sensory feedback information. This research will uncover the biological mechanism underlying this integration of motion planning and controls, enabling designs of robust and adaptable engineering systems. The synergistic effect between neuroscience and control engineering will accelerate advance of both fields. In particular, the knowledge from neuroscience will help to uncover engineering principles, while the results from control engineering will in turn provide new predictions for biological mechanisms that can be tested through physiological experiments.