Abstract In this paper, the robustness of a typical control scheme for Wiener systems is studied. These systems consist of the cascade connection of a linear time invariant system and a static nonlinearity. To control this kind of systems, several approaches were discussed in the literature. Most of these control schemes involve transformation of the measured variable as well as the setpoint, by the inverse of the nonlinear gain. The approach followed in this work uses the inverse model of the static nonlinear gain, while the uncertainty in the Wiener model is treated as a partitioned problem. The linear block is considered as a parameter-affine-dependent model and, on the other hand, the nonlinear block uncertainty is analyzed as a conic-sector. The robustness analysis is performed using µ-theory. The results are evaluated on the basis of a simulation of a pH neutralization process.

This paper presents improvements over the Dynamic Window Approach (I-DWA), used for computing in real time autonomous robot navigation. A novel objective function that includes Lyapunov stability criteria is proposed. It allows to guarantee a global and asymptotic convergence to the goal avoiding collisions and resulting in a more simple and self-contained approach. Experimental results with simulated and real environments are presented to validate the capability of the proposed approach. Additionally, comparisons with the original DWA are given.

In this paper we present a nonlinear infinite impulse response (NIIR) model structure for black-box identification of nonlinear dynamic systems. The proposed model structure allows the implementation of an identification algorithm in which the degrees of freedom of the Nonlinear Output Error (NOE) model can be easily increased or decreased during the identification process. This property is very attractive to find the appropriate NIIR model, avoiding overfitting. This is done using High Level Canonical Piecewise Linear (HL CPWL) functions with an increasing (decreasing) grid division. Therefore, the algorithm may start using a linear estimation of the model. The parameters of the HL CPWL functions are updated using a simple algorithm based on a modified steepest descent method with an independently adaptive learning rate.

In this paper we propose a Wiener-like approximation scheme that uses Rational Wavelets for the linear dynamical structure and Orthonormal High Level Canonical Piecewise Linear functions for approximating the nonlinear static part. This structure allows to approximate anynonlinear, time-invariant, causal dynamic systems with fading memory and has the following advantages: capability of time-frequency location, design of the linear dynamic part taking into account the a priori knowledge of the system, and minimum number of parameters of Orthonormal High Level Canonical Piecewise Linear functions determined straightforwardly.