Showing papers by "Keigo Watanabe published in 1987"
01 Jan 1987
TL;DR: A new hierarchical multiple model adaptive control (MMAC) approach is described for the control problem of stochastic distributed systems with unknown instrument failures, which leads to a flexible design algorithm for passively adaptive control strategies in steady-state.
Abstract: A new hierarchical multiple model adaptive control (MMAC) approach is described for the control problem of stochastic distributed systems with unknown instrument failures. The global and local hypotheses on the unknown actuator and/or sensor failures are assumed to be introduced to the decentralized multiple model structure. This leads to a flexible design algorithm for passively adaptive control strategies in steady-state. Furthermore, the coordinator algorithm in evaluating the global a posteriori probability is relatively simple to implement.
TL;DR: In this article, a simple method is described for the calculation of the optimal gains for steady-state Kalman filters in continuous-and discrete-time systems with single output, which consists of comparing the optimal closed-loop characteristic polynomial with a determinant expansion containing the unknown constant gains.
Abstract: A simple method is described for the calculation of the optimal gains for steady-state Kalman filters in continuous- and discrete-time systems with single output. The procedure consists of comparing the optimal closed-loop characteristic polynomial with a determinant expansion containing the unknown constant gains. It is then shown that for the case when a computer program is not used for finding the roots of a polynomial, the optimal closed-loop characteristic polynomial in the discretetime problem is not as readily obtainable as that in the continuous-time problem, even though the system under consideration is of lower order. This drawback, however, is shown to be overcome by invoking the bilinear transformation.
TL;DR: It is shown that the MMAF method is useful not only for a cases where the actual system model is included within the candidate models, but also for a case where the Actual System model is not included withinThe candidate models.
Abstract: The multiple-model adaptive filter (MMAF) method is applied to the estimation of error states of inertial navigation systems (INS). Monte Carlo simulations are performed to evaluate the sensitivity of several MMAFs to uncertainties in flight condition, where a Doppler radar receiver or Omega receiver is considered as the reference information source. It is shown that the MMAF method is useful not only for a case where the actual system model is included within the candidate models, but also for a case where the actual system model is not included within the candidate models.
TL;DR: In this paper, the estimation error covariance of a fixed-point smoother in the steady state, which has the steady-state solution of the Kalman filter as the initial condition, is considered for both continuous and discrete-time systems.
Abstract: The estimation error covariance of a fixed-point smoother in the steady-state, which has the steady-state solution of the Kalman filter as the initial condition, is considered for both continuous- and discrete-time systems. Applying some results on a stabilizing solution for a forward-pass fixed-interval smoother, a necessary and sufficient condition is given for assuring the existence of a unique stabilizing solution for such a fixed-point smoother. It is then shown that the resulting condition is equivalent to a well-known condition for the existence of a unique stabilizing solution of the Kalman filter or of the backward information filter.