A TOPSIS based Self-Organizing Double Loop Recurrent Broad Learning System for Uncertain Nonlinear Systems

This study proposes an efficient intelligent control structure for uncertain nonlinear systems. The controller is implemented by a sliding mode control framework including a modified broad leaning network (BLS) with a double-loop recurrent structure. In addition, the proposed BLS involves a self-organizing mechanism to increase or decrease the size of the BLS. The technique for order of preference by similarity to ideal solution (TOPSIS) method is used to build the self-organizing mechanism. Moreover, two dynamic thresholds of TOPSIS are automatically determined according to the stability of the controller. One dynamic threshold is used to consider whether to retain or remove existing network neurons in the BLS; and the other is used to generate new neurons, so as to meet the requirements of different control states and save computing resources. To improve the network's dynamic characteristics, a double-loop recurrent structure is further introduced into the self-organizing BLS. The Lyapunov stability function is used to ensure the stability of the control system. The proposed controller is applied to the simulation control of a nonlinear chaotic system and a three-link robot manipulator. The experimental results show that the proposed controller can achieve better control performance against other network-based controllers. The source code of this work is placed at https://github.com/wzhuang-xmu/SODLRBLS