Fuzzy Modelling and Chaos Control in the Photogravitational Magnetic Binary Problem With Potential From a Belt

Control of chaotic systems has led to many fruitful results, such as the famous OGY and feedback control. Of course, controlling chaos is not limited to the approaches above and is not specifically reviewed one by one here. The representation of chaotic systems using fuzzy models has a unified approach. The fuzzy logic controllers had been proposed for a long time and were successfully applied to control the chaos in many systems. The main purpose of this study is to presents the new fuzzy model for the photogravitational magnetic-binary problem (PMBP) where the bigger primary is a source of radiation and the smaller primary is an oblate body; and they are encompassed by a homogeneous circular cluster of material points centred at the mass centre of the system (belt). It was shown that using a new fuzzy controller, it is possible to control of chaotic behaviour of the photogravitational magnetic-binary problem (PMBP). The simulation results have demonstrated that the proposed method can satisfy the control object and enhanced stability.

Adaptive Neuro-Fuzzy Sliding Mode Controller

A novel adaptive sliding mode controller using neuro-fuzzy network based on adaptive cooperative particle sub-swarm optimization (ACPSSO) is presented in this article for nonlinear systems control. The proposed scheme combines the advantages of adaptive control, neuro-fuzzy control, and sliding mode control (SMC) strategies without system model information. An adaptive training algorithm based on cooperative particle sub-swarm optimization is used for the online tuning of the controller parameters to deal with system uncertainties and disturbances. The algorithm was derived in the sense of Lyapunov stability analysis in order to guarantee the high quality of the controlled system. The performance of the proposed algorithm is evaluated against two well-known benchmark problems and simulation results that illustrate the effectiveness of the proposed controller.