Abstract
This paper is devoted to the guaranteeing estimation method with application to the calibration problem of a gyro unit. Mathematical models are constructed to describe the kinematics of the gyro unit on a test bench. The applicability limits and errors of the models are investigated. A numerical solution procedure is developed for guaranteeing estimation problems based on their reduction to l1-approximation problems.
Notes
An open source Python-embedded modeling language for convex optimization problems; https://web.stanford.edu/boyd/papers/pdf/cvxpyrewriting.pdf.
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APPENDIX
APPENDIX
Proof of Lemma 1. On the time interval T the rotation occurs about a fixed direction. Therefore, the rotation matrix Dcir is decomposed by averaging as follows:
where ci is the result of the time averaging of the functions sin ψ(t) and cos ψ(t).
Due to formulas (6), (7), and (A.1), in the course of averaging, the vector uz is represented as the sum of two terms, one proportional to w and the other orthogonal to D(0)w:
where u⊥ = \(D{\kern 1pt} '{\kern 1pt} {{\bar {D}}_{{{\text{cir2}}}}}{{D}_{{{\text{fix}}}}}{{u}_{x}}\).
The orthogonality of u⊥ to the direction D(0)w = Dinit(I3 + \(\hat {\beta }\))w can be established using formulas (2) and (6): D(0)w = D′Dcir(0)Dfixw = D'(0, 0, 1)T; the corresponding scalar product is explicitly calculated as
Using the component c1 as an example, we explain the idea of estimating from above the result of the time averaging of the function cos ψ(t). Consider the continuous case of averaging:
The dynamics of the angle ψ are described by a differential equation and constraints on the functions on its right-hand side:
The change of variables t = t(ψ), \(\epsilon \)(ψ) = ε(t(ψ)), |\(\epsilon \)(ψ)| \(\leqslant \) εmax, in the integral yields
This integral can be written as the sum of integrals on the half-periods of the function cosψ (intervals where the function has a fixed sign) and two integrals corresponding to the time intervals at the beginning and end of the interval [ψ0, ψ(T)]. For example, if ψ0 < π/2, this interval is represented as follows:
where ncir is the number of complete revolutions of the system about the axis of rotation and the length of the last interval does not exceed π, i.e., π/2 + 2πncir \(\leqslant \) ψ(T) \(\leqslant \) 3π/2 + 2πncir.
The integrand on each such interval has a fixed sign, and the maximum value of the integrand (hence, that of the integral) is achieved at \(\epsilon \)(ψ) = –sgn(cos ψ)εmax:
Therefore, each integral can be estimated bilaterally (from below and above):
As a result, the absolute value of the integral on the averaging interval admits the following upper bound:
The angular rate and the number of complete revolutions of the system are related by
for some Δψ \(\leqslant \) 2π. Consequently,
Thus, we obtain
where the parameter C = \(\frac{2}{{\pi (1 - \varepsilon _{{\max }}^{2}{\text{/}}{{s}^{2}})}}\) is an upper bound for the fraction \(\frac{{2(1 - \Delta \psi {\text{/}}(sT))}}{{\pi (1 - \varepsilon _{{\max }}^{2}{\text{/}}{{s}^{2}})}}\).
Proof of Proposition 1. We transform the integrand of the objective function into problem (18) by substituting formulas (12) and (13) with the additional notation \({v}\) = \({v}\)(s, y) = Dinit(sy + yyTux):
Hence,
These formulas involve, first, the properties of matrix operations
and, second, the possibility of transferring the scalar product yTux to the other part of the corresponding multiplier group: \(\hat {\alpha }y{{y}^{{\text{T}}}}{{u}_{x}}\) = \( - {{y}^{{\text{T}}}}{{u}_{x}}\hat {y}\alpha \).
Let us define the matrices \(C_{\alpha }^{'}\) and \(C_{\beta }^{'}\):
Then the right-hand side of (A.3) is represented as a function that linearly depends on the variables q, α, β, ε, and \(\delta \tilde {\nu }\):
Substituting formula (A.4) into the original objective functional (19) yields
Since q = col(γ, ν0), the function l(Φ) – aTq linear depends on q, and the multiplier at q is
Therefore, if condition (22) is violated, we have \({{\sup }_{{q \in {{{\mathbf{R}}}^{{12}}}}}}\)|l(Φ) – aTq| = +∞ for a fixed Φ and arbitrary admissible α, β, ε, and δν'. Consequently,
In other words, it is necessary to maximize the absolute value of a linear function where each term depends on only one variable not figuring in the other terms. This means that the maximum can be found independently in each of the variables. For a fixed Φ, the maximum is determined in an explicit form:
A similar chain of considerations applies to the other terms in the objective functional (18). Thus, the explicit calculation of the supremum of the original objective functional finally leads to the optimization problem (21)–(22).
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Akimov, P.A., Matasov, A.I. The Guaranteeing Estimation Method to Calibrate a Gyro Unit. Autom Remote Control 84, 699–714 (2023). https://doi.org/10.1134/S0005117923070020
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DOI: https://doi.org/10.1134/S0005117923070020