Optimal Event-triggered Control using Adaptive Dynamic Programming discusses event triggered controller design which includes optimal control and event sampling design for linear and nonlinear dynamic systems including networked control systems (NCS) when the system dynamics are both known and uncertain. The NCS are a first step to realize cyber-physical systems (CPS) or industry 4.0 vision. The authors apply several powerful modern control techniques to the design of event-triggered controllers and derive event-trigger condition and demonstrate closed-loop stability. Detailed derivations, rigorous stability proofs, computer simulation examples, and downloadable MATLAB codes are included for each case. In this section, we will provide a concise overview of arti?cial neural networks (NN), emphasizing aspects most pertinent to their applications in closed-loop control of dynamical systems. Numerous other neural network architectures, such as Long Short-Term Memory (LSTMs) networks, autoencoders, Generative Adversarial Networks (GANs), transformers, graph neural networks, reservoir networks, graph convolutional networks, and more advanced generative networks like diffusion models, have been introduced.