Design of a grey-prediction self-organizing fuzzy controller for active suspension systems

• This study developed a grey-prediction self-organizing fuzzy controller (GPSOFC) for active suspension systems.
• The GPSOFC employs a grey-prediction algorithm to pre-correct fuzzy rules to reasonable ones for active suspension control.
• The GPSOFC eliminates the problem caused by the self-organizing fuzzy controller (SOFC) with inappropriately chosen parameters to overcome the difficulty of an SOFC implementation.
• Experimental results demonstrate that the GPSOFC achieved better control performance than the SOFC as well as the passive control for the manipulation of the active suspension system.

Self-organizing fuzzy controllers (SOFCs) have excellent learning capabilities. They have been proposed for the manipulation of active suspension systems. However, it is difficult to select the parameters of an SOFC appropriately, and an SOFC may extensively modify its fuzzy rules during the control process when the parameters selected for it are inappropriate. To eliminate this problem, this study developed a grey-prediction self-organizing fuzzy controller (GPSOFC) for active suspension systems. The GPSOFC introduces a grey-prediction algorithm into an SOFC, in order to pre-correct its fuzzy rules for the control of active suspension systems. This design solves the problem of SOFCs with inappropriately chosen parameters. To evaluate the feasibility of the proposed method, this study applied the GPSOFC to the manipulation of an active hydraulic-servo suspension system, in order to determine its control performance. Experimental results demonstrated that the GPSOFC achieved better control performance than either the SOFC or the passive method of active suspension control.

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