Decoupled Sliding Mode Control of Underactuated Nonlinear Systems Using a Fuzzy Brain Emotional Cerebellar Model Control System

This paper proposes a new intelligent control algorithm for the decoupling control of a class of single-input fourth-order underactuated nonlinear systems. By introducing an intermediate variable, a coupling sliding surface of two second-order sliding surfaces can be defined. Then, by applying a single-input fuzzy brain emotional cerebellar model articulation controller (FBECMAC)-based control system, the decoupling control of underactuated systems can be achieved with favorable transient response. The proposed control system consists of an FBECMAC and a fuzzy compensator. The FBECMAC is used as the main controller to approach an ideal controller to achieve desired control performance, and the fuzzy compensator is used to eliminate the approximation error to achieve system stability. The brain emotional model has an amygdala cortex and an orbitofrontal cortex, so the FBECMAC contains two neural networks; the amygdala cortex is a decision-making neural network and the orbitofrontal cortex is an emotional neural network. The proposed FBECMAC is adaptive and can adjust the parameters to achieve efficient control performance. The fuzzy compensator can also adjust its singleton fuzzy value to satisfy system stability. Finally, the FBECMAC-based decoupled sliding mode control system is applied to control one degree underactuated systems, such as a bridge crane and an aeroelastic system. Simulation results have validated the effectiveness of the proposed control approach. The proposed method can be applied to the practical systems if the computation time is acceptable for these practical systems.