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

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 letter, we investigate biologically inspired strategies to develop predictive control laws for engineered systems with time delays of similar order to their system dynamics. 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 triggered 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.