Abstract
This paper describes the development of a new quadratic optimal controller for discrete-time systems with integral action and subject to non-manipulable and possibly non-measurable external inputs or disturbances. The proposed controller is a linear quadratic regulator (LQR) based on an augmented state-space that aims to include the integral of error and disturbance modeling in the solution. For cases where the disturbance is not measured, this controller is applied in conjunction with a specific Kalman filter for estimating non-measurable inputs. The new controller is applied to maximum power point tracking (MPPT) simulations for photovoltaic systems and compared using the perturb and observe method. MPPT is performed by controlling the duty cycle of a DC-DC boost converter connected to the output of the photovoltaic system. The case studies seek to evaluate controller performance regarding variations in temperature and irradiance, measurement noises, and uncertainties in the model. The results show that the new controller is able to increase system efficiency while reducing costs associated with implementing current, voltage, and irradiance filters and sensors.
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The irradiance data used in this study are available in http://sonda.ccst.inpe.br/.
Notes
For discretization, using the usual state-space discretization method with zero-order holder and sampling time \( T_s \), the system is rewritten with input matrix defined as \( [{\mathcal {A}} \text { } {\mathcal {E}}] \) and the resultant matrix is separated in G and E.
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Funding
This study was financed in part by the Coordenacso de Aperfeicoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. This work was supported by Universidade Estadual de Santa Cruz, grant number 073.6766.2019.0021797-78.
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All authors contributed to thestudy conception and design. Material preparation, data collection and analysis were performed by ViníciusSouza Madureira, Thiago Pereira das Chagas and Gildson Queiroz de Jesus. The first draft of the manuscriptwas written by Vinícius Souza Madureira and all authors commented on previous versions of the manuscript.All authors read and approved the final manuscript.
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A proof of theorem 1
A proof of theorem 1
The system (1) can be rewritten in augmented form as
where
Through of (64), the Problem (2) can be rewritten as
with
where the submatrices of \( {{\mathcal {P}}}_k \) are known, symmetric, positive semi-definite weighting matrices with appropriate dimensions.
The problem (65–66) can be solved as a classic LQR problem with the solution given by [14]:
1.1 A.1 Control law
Substituting (64) and (66) in (67) results in
which can be simplified to
and reorganized as follow
or
with
as presented in the Theorem 1.
1.2 A.2 Riccati equation
Substituting (64) and (66) in 68) results in
which can be simplified to
By defining \(\Psi :={{G}_{k}}{{\left( {{R}_{k}}+G_{k}^{T}P{{_{k+1}^{x}}_{k+1}}{{G}_{k}} \right) }^{-1}}G_{k}^{T}={{G}_{k}}{{\Phi }_{k}}\), the above equation become
which is expanded to
Performing the two matrices multiplications results in
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Madureira, V.S., das Chagas, T.P. & de Jesus, G.Q. Integral linear quadratic Gaussian regulator subject to unknown inputs: application in photovoltaic systems. Int. J. Dynam. Control 12, 1477–1490 (2024). https://doi.org/10.1007/s40435-023-01282-7
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DOI: https://doi.org/10.1007/s40435-023-01282-7