Real-time, adaptive, model-independent control of low-dimensional chaotic and nonchaotic dynamical systems

Current model-independent control techniques are limited, from a practical standpoint, by their dependence on a precontrol learning stage. Here we develop a model-independent control technique, for chaotic and nonchaotic low-dimensional dynamical systems, that operates in real-time (i.e., it does not require a learning stage). We show that this technique is adaptive to system nonstationarities, robust to noise, and capable of stabilizing higher-order unstable periodic orbits. Because this technique is real-time, adaptive, and model-independent, it is practical for real-world systems.