Human Supervision of High-Gain Observer-Based Control of Uncertain Nonlinear Multi-Agent Systems

As tomorrow's machines become more general and autonomous, building resiliency into these systems is of critical importance. High-gain observers aggressively respond to errors and guarantee semi-global stability. Supervisory shared control architectures demonstrate the utility of a human-on-the-loop approach to enhance controllers via configurable parameters. Here, we investigate how a cascade high-gain observer-based controller designed to achieve leader-follower tracking can be modified to allow for human intervention, with the specific objective of maintaining performance goals while minimizing control saturation and aggressiveness. We perform envelope-straining experiments that suggest an adjustment of the high-gain parameter before the saturation limits is a better strategy to minimize aggression and thus energy cost. Copyright (c) 2024 The Authors.