Retrospective-Cost Adaptive Control of Uncertain Hammerstein Systems Using a NARMAX Controller Structure

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97107/1/AIAA2012-4448.pd

An efficient CS-CPWL Based Predistorter

We study the performance of Hammerstein predistorters (PD) to model and compensate nonlinear effects produced by a high power amplifier with memory. A novel Hammerstein model is introduced that includes, as the basic static nonlinearity, the complex simplicial canonical piecewise linear (CS-CPWL) description. Previous results by the authors have shown that the use of this kind of static nonlinearity leads to an efficient representation of basic nonlinear models. Furthermore, different tradeoffs between modeling capability and performance are considered.Fil: Bruno, Marcelo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Cousseau, Juan Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Werner, Stefan. Helsinki University Of Technology. Departament Of Signal Processing And Acoutics; FinlandiaFil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Cheong, Mei Yen. Helsinki University Of Technology. Departament Of Signal Processing And Acoutics; FinlandiaFil: Wichman, R.. Helsinki University Of Technology. Departament Of Signal Processing And Acoutics; Finlandi

Identification of Nonlinear Systems Structured by Wiener-Hammerstein Model

Wiener-Hammerstein systems consist of a series connection including a nonlinear static element sandwiched with two linear subsystems. The problem of identifying Wiener-Hammerstein models is addressed in the presence of hard nonlinearity and two linear subsystems of structure entirely unknown (asymptotically stable). Furthermore, the static nonlinearity is not required to be invertible. Given the system nonparametric nature, the identification problem is presently dealt with by developing a two-stage frequency identification method, involving simple inputs

Fuzzy Hammerstein Model of Nonlinear Plant

This paper presents the synthesis and analysis of the enhanced predictive fuzzy Hammerstein model of the water tank system. Fuzzy Hammerstein model was compared with three other fuzzy models: the first was synthesized using Mamdani type rule base, the second – Takagi-Sugeno type rule base and the third – composed of Mamdani and Takagi-Sugeno rule bases. The synthesized model is invertible so it can be used in the model based control. The fuzzy Hammerstein model was synthesized to eliminate disadvantages of the other fuzzy models. The advantage of the fuzzy Hammerstein model was experimentally proved and presented in this paper

Recurrent Equilibrium Networks: Flexible Dynamic Models with Guaranteed Stability and Robustness

This paper introduces recurrent equilibrium networks (RENs), a new class of nonlinear dynamical models for applications in machine learning, system identification and control. The new model class has ``built in'' guarantees of stability and robustness: all models in the class are contracting - a strong form of nonlinear stability - and models can satisfy prescribed incremental integral quadratic constraints (IQC), including Lipschitz bounds and incremental passivity. RENs are otherwise very flexible: they can represent all stable linear systems, all previously-known sets of contracting recurrent neural networks and echo state networks, all deep feedforward neural networks, and all stable Wiener/Hammerstein models. RENs are parameterized directly by a vector in R^N, i.e. stability and robustness are ensured without parameter constraints, which simplifies learning since generic methods for unconstrained optimization can be used. The performance and robustness of the new model set is evaluated on benchmark nonlinear system identification problems, and the paper also presents applications in data-driven nonlinear observer design and control with stability guarantees.Comment: Journal submission, extended version of conference paper (v1 of this arxiv preprint

Retrospective-Cost Adaptive Control of Uncertain Hammerstein-Wiener Systems with Memoryless and Hysteretic Nonlinearities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97108/1/AIAA2012-4449.pd

Modelling and inverting complex-valued Wiener systems

We develop a complex-valued (CV) B-spline neural network approach for efficient identification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton algorithm effectively estimates the model parameters that include the CV linear dynamic model coefficients and B-spline neural network weights. The identification algorithm naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton algorithm based on the estimated B-spline neural network model, with the aid of the De Boor recursions. The effectiveness of our approach for identification and inversion of CV Wiener systems is demonstrated using the application of digital predistorter design for high power amplifiers with memor

When self-consistency makes a difference

Compound semiconductor power RF and microwave device modeling requires, in many cases, the use of selfconsistent electrothermal equivalent circuits. The slow thermal dynamics and the thermal nonlinearity should be accurately included in the model; otherwise, some response features subtly related to the detailed frequency behavior of the slow thermal dynamics would be inaccurately reproduced or completely distorted. In this contribution we show two examples, concerning current collapse in HBTs and modeling of IMPs in GaN HEMTs. Accurate thermal modeling is proved to be be made compatible with circuit-oriented CAD tools through a proper choice of system-level approximations; in the discussion we exploit a Wiener approach, but of course the strategy should be tailored to the specific problem under consideratio