Parameter and structure identification of linear multivariable systems

A recursive pseudo-inverse algorithm is presented for estimating the parameters of the transfer-function matrix of a multiple-input-multiple-output discrete-time system from the samples of the input-output data. The algorithm is suitable for on-line identification of linear multivariable systems.

Relay Tuning of Multivariable PI Controllers

A formulation is presented for identification of linear multivariable from a single set of input-output data. The identification method is formulated with the mathematical framework of learning identifications, by extension of the repetition domain concept to include shifting time intervals. This method contrasts with existing learning approaches that require data from multiple experiments. In this method, the system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded real eigenvalue assignment procedure. Through this relationship, the Markov parameters of the observer are identified. The Markov parameters of the actual system are recovered from those of the observer, and then used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and numerical examples presented to illustrate.

Topics in Identification of Multivariable Linear Systems

A new algorithm for the identification of continuous time nonlinear systems from sampled data records is derived based on state variable filters coupled with an orthogonal least squares estimator. Delayed filtered inputs, outputs and associated higher order derivatives collected from the state variable filters are used for the identification of the unknown system parameters using an orthogonal least squares estimator. Because of the high dimensionality of general nonlinear systems the error reduction ratio derived from the orthogonal least squares estimator is used to detect the model structure. Plots of the unmodelled estimation errors against the state variables is also proposed as a means of investigating the nonlinear system characteristics. Simulation studies are included to illustrate the concepts.

Can Stock Price Fundamentals Properly Be Captured? Using Markov Switching in Heteroskedasticity Models to Test Identification Schemes

This study presents a parametric system identification approach to estimate the dynamics of a chemical plant from experimental data and develops a robust PID controller for the plant. Parametric system identification of the heat exchanger system has been carried out using experimental data and prediction error method. The estimated model of the heat exchanger system is a time-delay model and a robust PID controller for the time-delayed model has been designed considering weighted sensitivity criteria. The mathematical background of parametric system identification, stability analysis, and H∞ weighted sensitivity analysis have been provided in this paper. A graphical plot has been provided to determine the stability region in the (Kp,Ki), (Kp,Kd) and (Ki,Kd) plane. The stability region is a locus dependent on parameters of the controller and frequency, in the parameter plane.

On the optimal location of sensors for parametric identification of linear structural systems

On-Line Eigenvector Algorithms for the Identification of Dynamic Systems

A criterion is introduced for determining the optimum sampling interval for the identification of system parameters. It increases the accuracy and stability in identification procedures. This is done by employing a bilinear transformation from the z-plane to the w-plane. Some results of simulations are also included. These reveal the usefulness of the suggested algorithm even in the presence of measurement noise.

The problem of recursive robust identification of linear discrete-time single-input single-output dynamic systems with correlated disturbances is considered. Problems related to the construction of optimal robust stochastic approximation algorithms in the min-max sense are demonstrated. Since the optimal solution cannot be achieved in practice, several robustified stochastic approximation algorithms are derived on the basis of a suitable non-linear transformation of normalized residuals, as well as step-by-step optimization with respect to the weighting matrix of the algorithm. The convergence of the developed algorithms is established theoretically using the ordinary differential equation approach. Monte Carlo simulation results are presented for the quantitative performance evaluation of the proposed algorithms. The results indicate the most suitable algorithms for applications in engineering practice.

Constrained multisine input signals for plant-friendly identification of chemical process systems

Recent Advances in Modeling and Identification of Stochastic Multivariable systems and Their Applications

We introduce a consecutive identification of ANFIS-based fuzzy systems with the aid of genetic data granulation to carry out the model identification of complex and nonlinear systems. The proposed model implements system structure and parameter identification with the aid of information granulation and genetic algorithms. The design methodology emerges as a hybrid structural optimization and parametric optimization. Information granulation realized with HCM clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions in the premise and the initial values of polynomial functions in the consequence. And the structure and the parameters of fuzzy model are identified by GAs and the membership parameters are tuned by GAs. In this case we exploit a consecutive identification. The numerical example is included to evaluate the performance of the proposed model.

Early control textbooks in Norway