In this paper, we propose a subspace state space system identification method using prior knowledge. In this method, we give an algorithm to identify the partial state which corresponds to unknown modes of the system. This scheme is effective when some system poles cannot be identified because of the measurement noise or a high gain feedback controller. The effectiveness of the proposed method is evaluated by numerical examples including the comparison with the conventional 4SID method
The OGY method can harness chaotic motion of plants to the periodic orbit which is arbitrarily chosen and has been already verified on comparatively simple and well-known chaotic attractors like Lorentz attractor and so on. Its method is expected because it doesn't need any arithmetic model like differential equations but time-series data of single variable which represents the substantial behavior of target plants. We have already studied on an application of its method to a forced pendulum, whose pivot point is sinusoidally vibrated horizontally, as a typical mechanical plant and shown some problems to be considered. In this paper, trying to apply the OGY method to the number of the saddle points of the periodic orbits which occur chaotic motion on the forced pendulum, influence of noise given onto the parameters of the forced pendulum is studied using a numerical simulation. Consequently it is suggested that the angle between stable direction and unstable one and the relation between their directions around saddle points on the Poincare section and the directions of the axes of Poincare sections is substancial for the control performance.
Since Karmarkar's projective algorithm, many interior point methods for linear programming problem have been proposed. This paper presents a primal affine scaling algorithm for linear programming problem with bounded variables. This algorithm is derived from the standard affine scaling algorithm, separating the equality constraints about the bounded variables with the added slack variables. An implementation detail of this algorithm is also described. A program code based on this algorithm is developed using sparse matrix data structures, and applied to utility plant optimization problem. Numerical examples show that the proposed algorithm is practical for industrial plant optimization problem, and much superior in computing time to the standard primal affine scaling algorithm.
In this paper an adaptive regulator is designed for collocated parabolic distributed parameter systems with bounded deterministic disturbances in the case of the input and output operators being unbounded. The adaptive regulator is constructed by the concept of high-gain output feedback and the estimation mechanism of the unknown parameters for the disturbance. In the control system, the convergence of the system state to zero will be guaranteed.
This paper characterizes the reduced-order stabilizing controllers based on the change-of-variables type LMI's, where static output feedback controllers are discussed explicitly. In addition, necessary and sufficient conditions for the controller to achieve the H∞ control and/or the pole placement specifications are given. Furthermore, we give a computation method to solve LMI conditions of the change-of-variables type, and evaluate its effectiveness via a numerical example.