Black-box modeling techniques based on artificial neural networks are opening new horizons for modeling and controlling nonlinear processes in biotechnology and chemical process industries. The link between dynamic process models and actual process control is provided by the concept of model based control (MBC), e.g. Internal Model Control (IMC) or Model Based Predictive Control (MBPC). To avoi...

In this paper, a global stability and robust nonlinear controller for VSC-HVDC transmission Converters applying input-output linearization (IOL) techniques is proposed. The feedback linearization based nonlinear differential-geometric techniques is used to cancel nonlinearities and decouple the input control variables. Global tracking capability with zero steady-state error of the voltage loop ...

The problem of exact linearization via feedback consists in transforming a nonlinear system into a linear one using a state feedback. In the multivariable case, the nonlinear control law achieves also decoupling. The use of feedback linearization requires the complete knowledge of the nonlinear system. In practice, there are many processes whose dynamics is very complex, highly nonlinear and us...

We study distributed linearization or topological sorting in peer-to-peer networks. We define strict and eventual versions of the problem and consider these problems with and without input identifiers that do not exist in the system. None of these problems are solvable in the asynchronous messagepassing systems. We define a collection of oracles and prove which oracle combination is necessary t...

Based on the mathematical model of the permanent magnet synchronous motor (PMSM), a novel sliding mode variable structure input-output feedback linearization controller is proposed. A precision linearization method is employed to achieve input-output linearization and decoupling control of the motor, which is decoupled into a second-order linear speed subsystem and a first-order linear d-axis c...

We present and compare three new compact linearizations for the quadratic 0–1 minimization problem, two of which achieve the same lower bound as does the “standard linearization”. Two of the linearizations require the same number of constraints with respect to Glover’s one, while the last one requires n additional constraints where n is the number of variables in the quadratic 0–1 problem. All ...

The mathematical basis for bias linearization of quadratic magnetic actuators, as typified by magnetic bearings, is developed. The approach generalizes prior ad–hoc methods of linearizing the relationship between actuator force and electromagnet current, obviating the earlier assumptions of stator symmetry. This relationship is fundamentally quadratic in the regime where the magnetic material i...

This paper extends the energy-based version of the stochastic linearization method, known for classical nonlinear systems, to open quantum systems with canonically commuting dynamic variables governed by quantum stochastic differential equations with non-quadratic Hamiltonians. The linearization proceeds by approximating the actual Hamiltonian of the quantum system by a quadratic function of it...

Extended linearization is the process of factoring a nonlinear system into a linear-like structure x = A(x)x + B(x)u which contains state-dependent coefficient (SDC) matrices. An extended linearization control technique is any technique which (a) treats the SDC matrices A(z) and B(x ) as being constant and (b) uses a linear control synthesis method on the linear-like structure to produce a clos...

In order to expand a formal linearization method on designing an observer for nonlinear dynamic and measurement systems, we exploit the discrete Fourier expansion that can reduce computational complexity. A nonlinear multidimensional dynamic system is considered by a nonlinear ordinary differential equation, and a measurement equation is done by a nonlinear equation. Defining a linearization fu...