Minh Q. Phan

TL;DR: In this paper, an algorithm to compute Markov parameters of an observer or Kalman filter from experimental input and output data is discussed, which can then be used for identification of a state space representation with associated Kalman gain or observer gain for the purpose of controller design.

TL;DR: In this article, an algorithm to compute Markov parameters of an observer or Kalman filter from experimental input and output data is discussed, which can be used for identification of a state space representation, with associated Kalman gain or observer gain, for the purpose of controller design.

TL;DR: In this paper, the authors discuss the control of vibrating systems, integrating structural dynamics, vibration analysis, modern control and system identification, and show the close integration of system identification and control theory from state-space perspective, rather than from the traditional input-output model perspective of adaptive control.

TL;DR: This paper supplies a presentation of experiments on a commercial robot that demonstrate the effectiveness of iterative learning control, improving the tracking accuracy of the robot performing a high speed maneuver by a factor of 100 in six repetitions.

TL;DR: In this paper, a formulation for identification of linear multivariable systems from single or multiple sets of input-output data is presented, where the observer is expressed in terms of an observer, which is made asymptotically stable by an embedded eigenvalue assignment procedure.