This chapter presents several advanced applications of the new method for adaptive model-based control. It describes the application of the new method for adaptive model-based control to the case of robotic dynamic systems, which is very important for solving the problem of controlling real-world manipulators in real-time. The chapter also describes the application of the method for adaptive model-based control to the case of biochemical reactors in the food industry, which is a very interesting case due to the complexity of this non-linear problem. It considers briefly the problem of controlling international trade between three or more countries, with our new method for adaptive model-based control. The concept of model-based adaptive control is based on selecting an appropriate reference model and adaptation algorithm which modifies the feedback gains to the actuators of the actual system. The chapter analyses the neural networks used for identification and control for the case of adaptive model-based control of biochemical reactors producing yogurt.