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

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201