Hideo Shioya

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This paper considers the modeling and control problem for nonstationary nonlinear systems whose dynamic characteristics depend on time-varying working-points and may be locally linearized. It is proposed to describe the system behavior by the RBFARX model, which is an ARX model with Gaussian radial basis function (RBF) network-style coefficients depending(More)
An integrated modeling and robust model predictive control (MPC) approach is proposed for a class of nonlinear systems with unknown steady state. First, the nonlinear system is identified off-line by RBF-ARX model possessing linear ARX model structure and state-dependent Gaussian RBF neural network type coefficients. On the basis of the RBF-ARX model, a(More)
For a class of smooth nonlinear multivariable systems whose working-points vary with time and which may be represented by a linear MIMO ARX model at each working-point, a combination of a local linearization and a polytopic uncertain linear parameter-varying (LPV) state-space model are built to approximate the present and the future system’s nonlinear(More)
An integrated modeling and robust model predictive control (MPC) approach is proposed for a class of nonlinear systems. First, the nonlinear system is identified off-line by a RBF-ARX model possessing linear ARX model structure and state-dependent Gaussian RBF neural network type coefficients. On the basis of the RBF-ARX model, a combination of a local(More)
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