Advanced AI Control Pathways
R. Pasko · 2026
This chapter presents advanced AI control architectures that extend beyond the foundational methods developed earlier in the book. It begins with model predictive control as a classical reference point and then introduces neural network predictive control as a learned extension that uses an internal model to support prediction and control. The chapter next presents regressor-based neural inversion control, emphasizing structured inversion, practical safeguards, and experimental evaluation on a nonlinear plant. It then turns to model-based reinforcement learning and deep reinforcement learning as broader frameworks for control through prediction and interaction, including a Soft Actor-Critic example for planar arm reaching. The chapter concludes with a modular neuro-adaptive controller that combines LSTM-based predictive and decision sections with spiking, reward-driven adaptation, illustrating a more integrated and experimental learning-based control architecture.