Papers by Keyword: Subspace Identification

Abstract: In this paper, subspace identification methods are proposed to estimate the linear tire cornering stiffness, which are only based on the road tests data without any prior knowledge. This kind of data-driven method has strong robustness. In order to validate the feasibility and effectiveness of the algorithms, a series of standard road tests are carried out. Comparing with different subspace algorithms used in road tests, it can be concluded that the front tire cornering stiffness can be estimated accurately by the N4SID and CCA methods when the double lane change test data are taken into analysis.

Abstract: Subspace identification method was adopted to build a state-space model of the battery pack by directly using the acquisition data of current and voltage. The terminal voltage was split into four parts according to the relationship between the current and each element of the models output voltage. Then an equivalent circuit model composed of resistances and capacities was set up to simulate the relationship. Based on the battery model, a state space model with SOC as the state variable and voltage U_Cb as the output was set up. By applying a designed adaptive Kalman filter method to the model and adopting the voltage U_T1 from the subspace method as the measured output, the optimum estimation of SOC can be acquired with only calculations of one dimension.

Abstract: In order to deal with nonlinear, time-varying and disturbance-involved characteristics in the practical industrial processes, an indirect adaptive state-space MPC (model predictive control) method based on subspace identification is proposed. The state-space model, obtained through the POMOESP (Past Output MOESP, MOESP is one form of the subspace identification methods) algorithm, is regarded as the system model. Then, this model is used to design the model predictive controller that involves the solution of a quadratic programming problem to constraints. This controller is applied to the process control simulation on a 2-CSTR. Through comparisons of performance with a linear state-space MPC scheme, the superiority of the proposed control method is illustrated.

Abstract: In this contribution we present a validation of an identification procedure and a modeling method with regard to detection, localisation and quantification of damage in a structure. Vibration measurements of an excited experimental structure are used as input for a stochastic subspace system identification algorithm. The identified experimental modal data (eigenvalues and mode shapes) serve to update the underlying finite element model. The experimental setup consists of a cantilever beam and an additional equipment to damage the structure locally and progressively. In contrast to earlier contributions the evolution of damage is quantified in order to estimate the lifetime of the structure.