Sliding Mode Control Based on Chemical Reaction Optimization and Radial Basis Functional Link Net for De-Icing Robot Manipulator

In this paper, a sliding mode control (SMC) system based on combining chemical reaction optimization (CRO) algorithm with radial basis functional link net (RBFLN) for an n-link robot manipulator is proposed to achieve the high-precision position tracking. In the proposed scheme, a three-layer RBFLN with powerful approximation ability is employed to approximate the uncertainties, such as parameter variations, friction forces, and external disturbances, and to eliminate chattering phenomenon of the SMC. In order to achieve the expected performance in the initial phase as well as the improved convergence rate, the RBFLN parameters need to be optimized in advance. Therefore, the initial parameters of the RBFLN are optimized offline by CRO algorithm instead of random selection. Furthermore, the RBFLN weights are determined online according to adaptive tuning laws in the sense of a projection algorithm and the Lyapunov stability theorem to guarantee the stability and convergence of the system. The simulation results of three-link de-icing robot manipulator (DIRM) are provided to verify the robustness and effectiveness of the proposed methodology.