scholarly journals Linear parameter-varying model for a refuellable zinc–air battery

2020 ◽  
Vol 7 (12) ◽  
pp. 201107
Author(s):  
Woranunt Lao-atiman ◽  
Sorin Olaru ◽  
Sette Diop ◽  
Sigurd Skogestad ◽  
Amornchai Arpornwichanop ◽  
...  
Keyword(s):  
Dynamic Model ◽  
Linear Time ◽  
Storage System ◽  
High Capacity ◽  
Operating Conditions ◽  
Nonlinear Behaviour ◽  
Linear Parameter ◽  
Linear Parameter Varying ◽  
Air Battery ◽  
Parameter Varying

Due to the increasing trend of using renewable energy, the development of an energy storage system (ESS) attracts great research interest. A zinc–air battery (ZAB) is a promising ESS due to its high capacity, low cost and high potential to support circular economy principles. However, despite ZABs' technological advancements, a generic dynamic model for a ZAB, which is a key component for effective battery management and monitoring, is still lacking. ZABs show nonlinear behaviour where the steady-state gain is strongly dependent on operating conditions. The present study aims to develop a dynamic model, being capable of predicting the nonlinear dynamic behaviour of a refuellable ZAB, using a linear parameter-varying (LPV) technique. The LPV model is constructed from a family of linear time-invariant models, where the discharge current level is used as a scheduling parameter. The developed LPV model is benchmarked against linear and nonlinear model counterparts. Herein, the LPV model performs remarkably well in capturing the nonlinear behaviour of a ZAB. It significantly outperforms the linear model. Overall, the LPV approach provides a systematic way to construct a robust dynamic model which well represents the nonlinear behaviour of a ZAB.

scholarly journals Robust Filtering for Discrete-Time Linear Parameter-Varying Descriptor Systems

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1871 ◽  
Author(s):  
Carlos Rodriguez ◽  
Karina A. Barbosa ◽  
Daniel Coutinho
Keyword(s):  
Discrete Time ◽  
Filter Design ◽  
Linear Time ◽  
Estimation Error ◽  
Sufficient Conditions ◽  
Descriptor Systems ◽  
Linear Parameter ◽  
Linear Parameter Varying ◽  
Parameter Varying ◽  
Time Linear

This paper deals with robust state estimation for discrete-time, linear parameter varying (LPV) descriptor systems. It is assumed that all the system state-space matrices are affine functions of the uncertain parameters and both the parameters and their variations are bounded functions of time with known minimum and maximum values. First, necessary and sufficient conditions are proposed for admissibility and bounded realness for discrete linear time-varying (DLTV) descriptor systems. Next, two convex optimisation based methods are proposed for designing admissible stationary linear descriptor filters for LPV descriptor systems which ensure a prescribed upper bound on the ℓ2-induced gain from the noise signal to the estimation error regardless of model uncertainties. The proposed filter design results were based on parameter-dependent generalised Lyapunov functions, and full-order, augmented-order and reduced-order filters were considered. Numerical examples are presented to show the effectiveness of the proposed filtering scheme. In particular, the proposed approach was used to estimate the state variables of a controlled horizontal 2-DOF robotic manipulator based on noisy measurements.

scholarly journals Feedback control of linear systems with optimal sensor and actuator selection

2020 ◽  
pp. 107754632093983
Author(s):  
Taranjitsingh Singh ◽  
Massimo De Mauri ◽  
Wilm Decré ◽  
Jan Swevers ◽  
Goele Pipeleers
Keyword(s):  
Feedback Control ◽  
Semidefinite Programming ◽  
Optimization Problem ◽  
Linear Time ◽  
Linear Parameter ◽  
Linear Parameter Varying ◽  
Linear Time Invariant ◽  
Combined Design ◽  
Parameter Varying ◽  
Time Invariant

This article demonstrates a combined [Formula: see text] feedback control design for linear time-invariant and linear parameter-varying systems and optimal sensors and actuator selection. The combined design problem is systematically constructed as a mixed Boolean semidefinite programming optimization problem. We impose Big-M reformulations to the non-deterministic polynomial-time-hard coupled problem to be solved as a convex optimization problem using the branch and bound algorithm. The combined design of dynamic output feedback control along with optimal actuator selection for a linear time-invariant seismic rejection controller design serves as an application for validation by simulation. In addition, active vibration control of a smart composite plate along with optimal sensor and actuator selection validates the developed approach for linear parameter-varying controller synthesis. On comparing this approach with exhaustive search, it is observed that mixed Boolean semidefinite programming approaches have faster computation time, and comparing with the iterative reweighted ℓ1 norm algorithm and mixed Boolean semidefinite programming using outer approximations, mixed Boolean semidefinite programming yields a global solution.

H∞Optimization-based robust decoupling control algorithm in linear parameter varying systems using Hadamard weighting

2018 ◽  
Vol 41 (7) ◽  
pp. 1833-1848
Author(s):  
Adnan Jafar ◽  
Aamer Iqbal Bhatti ◽  
Sarvat M Ahmad ◽  
Nisar Ahmed
Keyword(s):  
Multiple Input Multiple Output ◽  
Operating Conditions ◽  
Decoupling Control ◽  
Control Technique ◽  
Linear Matrix ◽  
Feedback Gain ◽  
Linear Parameter ◽  
Linear Parameter Varying ◽  
Parameter Varying ◽  
Gain Scheduled

This article proposes a novel gain scheduled control technique for a class of linear parameter varying (LPV) systems with main emphasis on reducing the cross coupling interaction in dynamics using the Hadamard weight. By employing the Hadamard and the conventional [Formula: see text] performance weighting, an extended [Formula: see text] closed loop norm LPV theorem is derived that involves traditional [Formula: see text] weights for input output shaping control and Hadamard weight for decoupling control. Furthermore, a robust dynamic output feedback gain scheduled control law is designed to solve the control optimization problem in the proposed theorem using the linear matrix inequality (LMI) approach. It is shown that the proposed theorem is suitable for the multivariable control of multiple input multiple output (MIMO) coupled non-linear systems. In particular, in presence of cross coupled dynamics, external disturbances, changing operating conditions and time varying parameters. The effectiveness of the proposed technique is demonstrated in simulation as well as validated with experiments.

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