Many control applications require the use of both symbolic and numeric computation. An illustrative example is the analysis and controller design of a mechanical system. To begin with, we need to compute the equations of motion. To study local stability we need to compute its linearization. To apply control concepts like feedback linearization we may need to compute the inverse system. All thes...

An adaptive linearization scheme based on the exact model-matching (EMM) approach is proposed to compensate nonlinearity of high power amplifiers (HPAs). It is illustrated that the input-output property of the broadband amplifier system can be approximated by a memory polynomial model (MPM). Then, the compensation of HPA input is determined from the MPM in a real-time manner, and consequently, ...

Consideration was given to the linearization of logical functions defined by a set of pairwise orthogonal terms. The linearization is carried out by computing the autocorrelation functions. Proposed was a method consisting of (i) calculation of the autocorrelation function in the space of orthogonal terms, (ii) generation of the corresponding matrix of linear transformation, and (iii) the linea...

Complete eigenfunctions of linearized integrable equations expanded around an arbitrary solution are obtained for the Ablowitz-Kaup-Newell-Segur (AKNS) hierarchy and the Korteweg-de Vries (KdV) hierarchy. It is shown that the linearization operators and the recursion operator which generates the hierarchy are commutable. Consequently, eigenfunctions of the linearization operators are precisely ...

This paper proposes a trajectory tracking control for non-holonomic systems using dynamic feedback linearization based on piecewise multi-linear (PML) models. The approximated model is fully parametric. Input-output (I/O) dynamic feedback linearization is applied to stabilize PML control system. Although the controller is simpler than the conventional I/O feedback linearization controller, the ...

A novel Integrated Robust Optimal Design (IROD) methodology is presented in this work which combines a traditional sensitivity theory with relatively new advancements in Bilinear Matrix Inequality (BMI) constrained optimization problems. IROD provides the least conservative approach for robust control synthesis. The proposed methodology is demonstrated using numerical examples of integrated con...

The first and second moments of response variables for SDOF system with pseudoelastic material are obtained by a direct linearization procedure. This procedure is an adaptation of well-known statistical linearization methods, and provides concise, model-independent linearization coefficients. The method can be applied to systems that incorporate any SMA hysteresis model having a differential co...

Linearization of the input}output response of a nonlinear system via state feedback has found many applications in control of nonlinear processes. However, in the presence of uncertainties, this approach leads to inexact linearization and results in the loss of performance and stability. In this paper, a controller design methodology is developed based on input/output linearization and multi-ob...

− This paper proposes the use of feedback linearization for the characterization of thermoresistive sensors and for the control of measurement systems based on thermoresistive sensors kept at constant temperature. Two important benefits brought by feedback linearization are: regarding sensor characterization, it allows the determination of static and dynamic parameters by a single experimental ...

Convolutional neural networks (CNN) have recently achieved remarkable performance in a wide range of applications. In this research, we equip convolutional sequence-to-sequence (seq2seq) model with an efficient graph linearization technique for abstract meaning representation parsing. Our linearization method is better than the prior method at signaling the turn of graph traveling. Additionally...