RHC method for application to BMI-based systems

Purpose To present research in the area of control method for the man-machine systems with BMI (Brain machine interface). Concrete target system is, for instance, a car cruising system and so on. Design/methodology/approach The improved RHC (receding horizon control) method for the sampled-data systems and the adaptive DA converter which has the way to switch the sampling functions according to the system status are used. The feature selection method based on the k-SVM (kernel support vector machines) with the backward stepwise selection for the BMI signals are also used. Findings This paper proposes the new improved RHC method with the adaptive DA converter for the application of the BMI-based systems. The proposed method is illustlated as useful and efective method for the systems to which switch of control laws is indispensable by the simulations. Research limitations/implications Although the proposed method is effective for the BMI based-systems with switching of control laws, the more fast algorithm for RHC will be need to apply to the man-machine systems with the BMI in practical use. Practical implications The basic concept or framework of the proposed method can be used for the real man-machine systems with the BMI, for examples, car crusing systems, wheel-chaired systems and so on. Originality/value The paper contributes to the development of the new effective control method for the BMI-based man-machine systems. Key-words Man-machine systems, BMI(Brain machine interface) Receding horizon control, Adaptive DA converter Paper type Research paper