A combination of fuzzy logic and neural network controller for multiple-input multiple-output systems

Generally, the difficulty of multiple-input multiple-output (MIMO) systems control is how to overcome the coupling effects between the degrees of free dom. Owing to the computational burden and dynamic uncertainty of MIMO syst ems, the model-based decoupling approach is not practical for real-time con trol. A hybrid fuzzy logic and neural network controller (HFNC) is proposed here to overcome this problem and to improve the control performance. Firs tly, a traditional fuzzy controller (TFC) is designed from a single-input s ingle-output (SISO) systems viewpoint for controlling the degrees of freedo m of a MIMO system. Secondly, an appropriate coupling neural network contro ller is introduced into the TFC for compensating the system coupling effect s. This control strategy not only can simplify the implementation problem o f fuzzy control but also can improve the control performance. The state-spa ce approach for fuzzy control systems stability analysis is employed to eva luate the stability and robustness of this intelligent hybrid controller. I n addition, a dynamic absorber with a two-level mass-spring-damper structur e was designed and constructed to verify the stability and robustness of a HFNC by numerical simulation and to investigate the control performance by comparing the experimental results of the HFNC with that of a TFC for this MIMO system.