Publication

Data-driven Precompensator Tuning for Linear Parameter Varying Systems

Alireza Karimi, Roland Longchamp, Mark Edward John Butcher
2008
Conference Papers

Abstract

Methods for direct data-driven tuning of the parameters of precompensators for LPV systems are developed. Since the commutativity property is not always satisfied for LPV systems, previously proposed methods for LTI systems that use this property cannot be directly adapted. When the inverse of the system exists in the proposed parameterisation of the precompensator, the LPV transfer functions commute and an algorithm using only two experiments on the real system is proposed. It is shown that this algorithm gives consistent estimates of the parameters of the system inverse despite the presence of stochastic disturbances. For the more general case, when the system inverse does not belong to the set of parameterised precompensators, another technique is developed. This technique requires a number of experiments equal to twice the number of precompensator parameters and it is shown that the calculated parameters minimise the mean squared tracking error.

Official source

https://infoscience.epfl.ch/entities/publication/c3589d10-8378-4002-a00d-f24584732c30

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Alireza Karimi, Roland Longchamp, Mark Edward John Butcher
2008
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/c3589d10-8378-4002-a00d-f24584732c30

About this result

Related concepts (32)

Linear time-invariant system

In system analysis, among other fields of study, a linear time-invariant (LTI) system is a system that produces an output signal from any input signal subject to the constraints of linearity and time-invariance; these terms are briefly defined below. These properties apply (exactly or approximately) to many important physical systems, in which case the response y(t) of the system to an arbitrary input x(t) can be found directly using convolution: y(t) = (x ∗ h)(t) where h(t) is called the system's impulse response and ∗ represents convolution (not to be confused with multiplication).

Commutative property

In mathematics, a binary operation is commutative if changing the order of the operands does not change the result. It is a fundamental property of many binary operations, and many mathematical proofs depend on it. Most familiar as the name of the property that says something like "3 + 4 = 4 + 3" or "2 × 5 = 5 × 2", the property can also be used in more advanced settings. The name is needed because there are operations, such as division and subtraction, that do not have it (for example, "3 − 5 ≠ 5 − 3"); such operations are not commutative, and so are referred to as noncommutative operations.

Runge–Kutta methods

In numerical analysis, the Runge–Kutta methods (ˈrʊŋəˈkʊtɑː ) are a family of implicit and explicit iterative methods, which include the Euler method, used in temporal discretization for the approximate solutions of simultaneous nonlinear equations. These methods were developed around 1900 by the German mathematicians Carl Runge and Wilhelm Kutta. The most widely known member of the Runge–Kutta family is generally referred to as "RK4", the "classic Runge–Kutta method" or simply as "the Runge–Kutta method".

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