A Global-Local Mapping Approximation method is presented in this paper for identifying discrete systems using input-output data. The method is based on the idea that any nonlinear system can be represented as a sum of a discrete linear model and unmodeled nonlinearities. Linear system is then perturbed by a nonlinear term which represents the system nonlinearities that are not captured by the l...

Identifiability is important to guarantee convergence in system identification applications, and observability is important in applications such as control and diagnosis. In this paper, recent results on analysis of nonlinear differentialalgebraic equations are used to derive criteria for local identifiability and local weak observability for such models. The criteria are based on rank tests. E...

Prediction-based Iterative Learning Control (PILC) is proposed in this paper for a class of time varying nonlinear uncertain systems. Convergence of PILC is analyzed and the uniform boundedness of tracking error is obtained in the presence of uncertainty and disturbances. It is shown that the learning algorithm not only guarantees the robustness, but also improves the learning rate despite the ...

Standard (black-box) regression models may not necessarily suffice for accurate identification and prediction of thermal dynamics in buildings. This is particularly apparent when either the flow rate or the inlet temperature of the thermal medium varies significantly with time. To this end, this paper analytically derives, using physical insight, and investigates linear regression models with n...

Identification of a nonlinear additive system is considered. An input signal is designed in such a way that the problem of identification of nonlinear additive systems is reduced to a problem of identification of static nonlinear functions. Then, three approaches are established to estimate the order of the system. The methods exploit the structure of the nonlinear additive model so that their ...

This paper discusses the Hammerstein model identification using a blind approach. By fast sampling at the output, it is shown that identification of the linear part can be achieved based only on the output measurements that makes the Hammerstein model identification possible without knowing the structure of the nonlinearity and the internal variables.

The highly nonlinear responses of compliant ocean structures characterized by a large-geometry restoring force and a coupled fluid-structure interaction excitation are of great interest to ocean engineers. Practical modeling, parameter identification, and incorporation of the inherent nonlinear dynamics in the design of these systems are essential and challenging. An experimental mooring system...

Many control systems encountered in physical, automobile engineering, economic phenomena and biomedical engineering fields are nonlinear and nonstationary to some extent. In general, nonlinear processes can be adequately characterized by a nonlinear model. Recently, a system can be obtained directly from experimental input/ output data by determining the system structure and the numerical value...

The aim of this paper is to extend our previous work on a novel and recent class of nonlinear filters called Spline Adaptive Filters (SAFs), implementing the linear part of the Wiener architecture with an IIR filter instead of an FIR one. The new learning algorithm is derived by an LMS approach and a bound on the choice of the learning rate is also proposed. Some experimental results show the e...

The Volterra series have been successfully and widely applied as a nonlinear system modeling technique. Considered as a prototype, the second order Volterra filter (FV2) has an increased complexity in comparison with a linear filter. The filter based on the multi memory decomposition (MMD) structure represents a good approximation of the FV2 and significantly reduces the number of the filter op...