Learning Precisely Timed Feedforward Control of the Sensor-Denied Inverted Pendulum

TL;DR

This paper presents a biologically inspired learning algorithm for precisely timed feedforward control of a nonlinear pendulum with large delays and partial observations, effectively overcoming sensor and delay challenges in control systems.

Contribution

It introduces a novel learning-based control method that integrates sensor data into timed feedforward actions to handle delays and partial observations in nonlinear systems.

Findings

Successfully controls pendulum with large delays and partial observations

Demonstrates minimal computation and training data requirements

Applicable to biological and engineered delayed systems

Abstract

Time delays due to signal latency, computational complexity, and sensor-denied environments, pose a critical challenge in both engineered and biological control systems. In this work, we investigate biologically inspired strategies to develop precisely timed feedforward control laws for engineered systems with large time delays. We demonstrate this approach on the nonlinear pendulum with partially denied observations, so that it is only possible to measure the state of the system near the upright position. Given a large disturbance that overwhelms the local feedback controller, it is necessary to add or remove energy from the pendulum so that it returns to the upright position after one full revolution. The partial observation near the upright position introduces a significant delay between observations and the region where actuation is most effective. Thus, we develop a learning algorithm that integrates sensor information into a precisely timed feedforward control signal to overcome this delay with minimal computation, training data, and set of control decisions. This simple controller can serve as a model for many biological systems, and can be implemented in engineered systems with time delays.