Observer-based adaptive emotional controller for a class of uncertain nonlinear systems

Uncertainties and complexities of the actual control problems, such as unknown dynamics, unmeasurable states, external disturbances, and measurement noise, require powerful control structures capable of handling such complexities. Emotional controllers offer fast system response while also carrying a simple structure. However, the emotional controllers to date have not been evaluated rigorously. Here, the continuous radial basis emotional neural network (CRBENN) is employed to approximate the unknown dynamics in observer-based adaptive control structures for uncertain affine nonlinear systems. The system dynamics are unknown. Also, external disturbance and measurement noise affect system performance. Compared to the previous emotional controllers, the system states are not measurable and are estimated using a state estimator. The H∞ tracking performance is verified using Lyapunov stability theory, and suitable adaptive laws are designed for the weights of the proposed emotional networks that are consistent with the basic brain emotional learning model. Results indicate that the proposed controllers reach a lower tracking error with similar control energy consumption compared to another neuro-controller