Neural Network Controller

Neural network controllers leverage the power of deep learning to design control systems for complex dynamical systems, aiming to improve performance, robustness, and efficiency compared to traditional methods. Current research emphasizes developing provably stable and safe controllers, often using techniques like Lyapunov functions, control barrier functions, and integral quadratic constraints, alongside various neural network architectures including feedforward networks, recurrent networks, and hypernetworks. This field is significant because it enables the control of systems previously intractable with classical methods, impacting diverse applications from robotics and autonomous vehicles to biomedical engineering and industrial automation.