Zhonghua Quan

Stability-Guaranteed Horizon Size for Receding Horizon Control

We propose a stability-guaranteed horizon size (SgHS) for stabilizing receding horizon control (RHC). It is shown that the proposed SgHS can be represented explicitly in terms of the known parameters of the given system model and is independent of the terminal weighting matrix in the cost function. The proposed SgHS is validated via a numerical example.

A Robust FIR Filter for Linear Discrete-Time State–Space Signal Models With Uncertainties

This letter proposes a robust finite impulse response (FIR) filter for a class of linear discrete-time systems with quadratic bounded uncertainties. A set of all reachable current states under uncertainties is obtained from inputs and outputs measured on a recent finite time interval, which is represented in an ellipsoidal form. To minimize the maximum estimation error due to uncertainties, the center of the ellipsoid is chosen as an estimated state and the corresponding estimation error is obtained from the major axis of the ellipsoid. It is shown by simulation that the proposed robust FIR filter has a more robust performance than both robust infinite impulse response (IIR) filters and nominal FIR filters.

New Recursive Least Squares Algorithms without Using the Initial Information

In this letter, we propose a new type of recursive least squares (RLS) algorithms without using the initial information of a parameter or a state to be estimated. The proposed RLS algorithm is first obtained for a generic linear model and is then extended to a state estimator for a stochastic state-space model. Compared with the existing algorithms, the proposed RLS, algorithms are simpler and more numerically stable. It is shown through simulation that the proposed RLS algorithms have better numerical stability for digital computations than existing algorithms.

Robust FIR Filters for Linear Continuous-Time State-Space Models With Uncertainties

This letter proposes robust finite impulse response (FIR) filters for linear continuous-time statespace models with bounded uncertainties. A set of all reachable current states under bounded uncertainties is determined from inputs and outputs on a recent finite time interval. If some condition is met, this set is shown to be represented in an ellipsoidal form. The derivation procedure is much simplified by utilizing the result on the optimal tracking control with an indefinite cost function. In order to minimize the maximum estimation error due to uncertainties, the center of the reachable ellipsoidal set is chosen as an estimated state. It is shown through simulation that the proposed robust FIR filter achieves a more robust performance than existing robust infinite impulse response (IIR) filters.

Parallel Implementation of M-Step Kalman FIR Filter for Linear Discrete Time-invariant Systems

This paper proposes a new version of the receding horizon Kalman FIR (RHKF) filter called M-step Kalman FIR filter. The proposed filter is with an FIR filter form and can be represented in an iterative form as well as in a standard FIR form. The proposed filter can be implemented in parallel which is call parallel M-step Kalman FIR filter and thus the computation time can be reduced compared with that of the existing RHKF filter while it still preserves the good properties of the RHKF filter such as deadbeat and unbiasedness. The validity of the proposed filter is illustrated by simulation studies

NEW RECURSIVE LEAST SQUARE ALGORITHMS WITHOUT USING THE INITIAL INFORMATION

Abstract In this paper, new types of recursive least square (RLS) algorithms, without using the initial information of a parameter or a state to be estimated, are proposed. The proposed RLS algorithm is first obtained for a generic linear model and is then extended to a state estimator for a stochastic state-space model. Compared with the existing algorithms, the proposed RLS algorithms are simpler and more numerically stable. It is shown, by simulation studies, that the proposed RLS algorithms have better numerical stability for digital computation than existing algorithms.