A New Self-Organizing Double Function-Link Brain Emotional Learning Controller for MIMO Nonlinear Systems Using Sliding Surface

This paper aims to propose a new type of neural network which is the self-organizing double function-link brain emotional learning controller (SO-DFL-BELC) for multiple input multiple output (MIMO) nonlinear systems. The proposed controller is a newly designed neural network containing the key mechanism of a typical brain emotional learning controller (BELC), which is a mathematical model that approximates the judgmental and emotional activity of a brain, in which it is combined with some additional functions and methods. Firstly, a double function-link (DFL) network is applied to expand the function for a BELC to improve the accuracy of the system weights. Secondly, the self-organizing mechanism is utilized to increase or decrease the number of neurons that possibly supports the main controller to adapt to the sharp change of the input and to reduce the computation time significantly. Thirdly, the learning rules of the SO-DFL-BELC are developed based on the gradient descent algorithm and sliding surface. Finally, all parameters of the system can be optimized. The proposed SO-DFL-BELC is applied to control two different MIMO nonlinear systems that are a 4D chaotic system and a four-tank system. The simulation results show the favorable control performance of the proposed control algorithm.