Deep brain emotional learning-based intelligent controller applied to an inverted pendulum system

Biologically inspired controllers demonstrate great success in several applications, mainly in situations that present disturbances and uncertainties in the dynamics of the system. In recent times, several works have appeared in the area of emotional learning which occurs in the human brain, thus allowing to the emergence of new theories and applications in control engineering. In control engineering, it is possible to highlight the BELBIC (Brain Emotional Learning-Based Intelligent Controller). However, the design and commissioning of this type of controller still represents a major challenge for researchers, since it is necessary to determine some characteristic signals to this system (stimuli), which can vary from application to application. This work presents a methodology for the construction of architectures for BELBIC stimulus signals, using as a basis the DRL (Deep Reinforcement Learning) techniques. The DRL allows extracting characteristic patterns from the dynamics of systems which, perhaps, may have high dimensionality and possibly nonlinear dynamics, as is the case of most problems involving real-world dynamic systems. The resulting controller model is validated by applying an inverted pendulum dynamic system in order to demonstrate a new approach to the architectures of the BELBIC that allows to achieve a greater generalization in its application, as well as providing a viable alternative to the traditional models in use.