Adaptive Emotion-Centered Fuzzy Tracking Controller With Robust Outer Feedback

Abstract

We develop a novel robust self-organizing construction based on two powerful patterns—the elegance of cognitive (brain emotional learning) and the influence of decision making (fuzzy logic). Early in the design of the proposed controller, the friction, coriolis, centrifugal, and gravity parts of the robot model are considered as they are uncertain, and then they are compensated by the proposed structure. Unlike the existing approaches, the proposed self-organizing fuzzy emotional (SFE) approach achieves not only optimum parameters but also optimum constructions. In structure learning, a new index based on the measured error, the number of rules, and the max sensory input is considered to generate new rules. The parameter learning condition depends on the stability. Furthermore, we formulate a controller based on SFE for a two-link robot and 3-PSP spatial parallel manipulator. To deal with the increasing and decreasing weight of Amygdala, the robust outer feedback is augmented to the whole controller. Simulation results show the priority of the proposed approach in comparison to alternative approaches especially in improving the ability to deal with uncertainty.