Hierarchical Fuzzy-Logic-Based Electrical Power Subsystem for Pico Satellite ERPSat-1

Neji, Bilel; Hamrouni, Chafaa; Alimi, Adel M.; Nakajima, Hiroshi; Aslan, A. R.

doi:10.1109/jsyst.2013.2285659

Cited by 9 publications

(7 citation statements)

References 22 publications

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“…The energy‐driven scheduling for a CubeSat platform is also addressed by a work that adopts a hardware built‐in MPPT based on a boost converter 21 . The authors used fuzzy logic and hierarchical fuzzy logic algorithms and showed that the approach gives better results than the P&O method.…”

Section: Related Workmentioning

confidence: 99%

On‐board energy scheduling optimization algorithm for nanosatellites

Martinez

Seman

Filho

et al. 2023

Circuit Theory & Apps

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SummaryThis work proposes a real‐time and online energy scheduling optimization algorithm for nanosatellites operating with a direct energy transfer (DET) architecture. The goal of the algorithm is to optimize energy utilization by maximizing energy harvesting, meeting task deadlines and priorities, and ensuring a minimum quality of service (QoS) for the system. The algorithm is composed of a modified perturb and observe algorithm to reach the best energy efficiency point of the solar panels, a task prioritization algorithm to guarantee their times and minimum QoS, and a backpack optimization problem to maximize the energy efficiency of the satellite. To demonstrate the effectiveness of the proposed algorithm, simulations of a nanosatellite operating in a given orbit, attitude, and thermal parameters were conducted and compared to other task scheduling strategies. The results showed that the proposed energy scheduling algorithm had the best performance in terms of energy balance and average power consumption, with an energy balance up to 16% higher compared to the other tested strategies. This demonstrates the ability of the proposed algorithm to optimize power consumption and energy utilization, as well as efficiently schedule tasks to maximize energy harvesting. The proposed energy scheduling optimization algorithm has the potential to be a useful tool for the design and optimization of future satellite missions and could potentially guide the design of electrical power systems for new CubeSat missions operating with a DET architecture. The algorithm's ability to optimize energy utilization and ensure a minimum QoS for the system could improve the overall efficiency and performance of the satellite.

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Section: Related Workmentioning

confidence: 99%

On‐board energy scheduling optimization algorithm for nanosatellites

Martinez

Seman

Filho

et al. 2023

Circuit Theory & Apps

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“…An interesting solution adopted for the ERPSAT‐1 picosatellite uses fuzzy logic to obtain energy harvesting maximization 23 . The developed subsystem has three main blocks, which are the fuzzy maximum power point tracker, the power conditioner, and the hierarchical fuzzy subsystem controller.…”

Section: State Of the Artmentioning

confidence: 99%

Nanosatellite electrical power system architectures: Models, simulations, and tests

Slongo

Martinez

Eiterer

et al. 2020

Circuit Theory & Apps

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KESSLER SLONGO ET AL. power point tracking (MPPT) techniques, high-efficiency solar cells, and energy-aware scheduling algorithms are some of the hot topics under research nowadays. Due to size restrictions and aiming low complexity in design, many nanosatellite designers adopt the direct energy transfer (DET) architecture. KySat-2 is an example of a successfully accomplished mission, based on this EPS topology. Designed by Morehead State University Space Science and Engineering group, KySat-2 EPS was powered by a set of gold-plated deployable solar panels with 26% of efficiency. It provided four high-power regulated rails (two 3.3 V and two 5 V) and a low-noise, low power 3.3V rail for noise-sensitive devices. The energy storage system was made of three 18650 lithium-ion cells connected in series, and the high-power rails reached efficiencies approaching 90%. 12,21 Aiming minimum space occupation and low power consumption, the EPS proposed for the AraMiS project has a boost regulator operating the solar panels close to their MPP. 16 In order to reduce the number of components, the designers opted by the constant voltage MPPT algorithm, using only analog devices. The EPS steps up solar panels voltage from 4.4 V to the distribution bus voltage level (14 ± 2 V). It provides power buses with 3.3 and 5 V voltage levels. The EPS efficiency analysis is demonstrated, calculating the main components power losses. Although this is a very detailed and well-described work, it does not compare the proposed solution with other EPS architectures. A 3U EPS efficiency analysis is presented by Gonzalez-Llorente et al. 22 The subsystem is based on a buck converter that steps down the solar panels voltage (15 or 7.5 V) to the litChium-ion battery range of 3.3 or 6.6 V. The authors define an optimum operation point for the DC-DC converter in order to achieve its maximum efficiency. The operation point is defined by the converter input voltage, output voltage, and output power. Therefore, the discussion is based on different solar cells connections configurations, and battery cells arrangement, that result in the most efficient EPS configuration. Different scenarios have been analyzed to achieve 98% of peak efficiency on the best configuration: input voltage of 7.4 V, output voltage of 3.3 V, and output current varying from 0.1 mA to 2.1 A. Only the buck converter topology is discussed in this paper, and it does not consider any MPPT technique. An interesting solution adopted for the ERPSAT-1 picosatellite uses fuzzy logic to obtain energy harvesting maximization. 23 The developed subsystem has three main blocks, which are the fuzzy maximum power point tracker, the power conditioner, and the hierarchical fuzzy subsystem controller. The fuzzy logic algorithm has been implemented in a dedicated microcontroller that controls a boost converter. Simulations have been performed to demonstrate the better efficiency of the proposed method when compared to the Perturb and Observe MPPT algorithm. Although a comparison is provided in this work,...

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“…This is in order to simultaneously reduce the ripples caused by the inaccurate external sensors and inappropriate weighting factor for the IM FCS-MPTC working over the highspeed range. The fuzzy control theories are employed in this paper as the fuzzy logic has the advantage of providing a solution to the machine control problem that can be cast in terms that human operators can understand [30][31][32][33][34][35], making it easier to mechanize tasks that are already successfully performed by humans. It deserves to be mentioned the adaptive weighting factor tuning method is able to solve the second internal problem above when it is executed alone.…”

Section: Introductionmentioning

confidence: 99%

“…This assists in evaluating the effectiveness of the proportional and integral factors designed in the previous period and ultimately provide guidelines for generating new control parameters. What needs to be mentioned is that although the fuzzy control strategies that are achieved by using the previous states or errors were studied previously in [33][34][35]. The proposed fuzzy controller is still pretty novel considering its structure and role in suppressing the steady-state ripples of an IM.…”

Section: Introductionmentioning

confidence: 99%

Ripple Attenuation for Induction Motor Finite Control Set Model Predictive Torque Control Using Novel Fuzzy Adaptive Techniques

et al. 2021

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Finite control set model predictive torque control (FCS-MPTC) strategy has been widely used in induction motor (IM) control due to its fast response characteristic. Although the dynamics of the FCS-MPTC method are highly commended, its steady-state performance—ripple deserves attention in the meantime. To improve the steady-state performance of the IM drives, this paper proposes an improved FCS-MPTC strategy, based on a novel fuzzy adaptive speed controller and an adaptive weighting factor, tuning strategy to reduce the speed, torque and flux ripples caused by different factors. Firstly, a discrete predicting plant model (PPM) with a new flux observer is established, laying the ground for achieving an FCS-MPTC algorithm accurately. Secondly, after analyzing the essential factors in establishing a fuzzy adaptive PI controller, with high ripple suppression capacity, an improved three-dimensional controller is designed. Simultaneously, the implementation procedures of the fuzzy adaptive PI controller-based FCS-MPTC are presented. Considering that a weighting factor must be employed in the cost function of an FCS-MPTC method, system ripples increase if the value of the weighting factor is inappropriate. Then, on that basis, a novel fuzzy adaptive theory-based weighting factor tuning strategy is proposed, with the real-time torque and flux performance balanced. Finally, both simulation and hardware-in-loop (HIL) test are conducted on a 1.1 kW IM drive to verify the proposed ripple reduction algorithms.

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Hierarchical Fuzzy-Logic-Based Electrical Power Subsystem for Pico Satellite ERPSat-1

Cited by 9 publications

References 22 publications

On‐board energy scheduling optimization algorithm for nanosatellites

On‐board energy scheduling optimization algorithm for nanosatellites

Nanosatellite electrical power system architectures: Models, simulations, and tests

Ripple Attenuation for Induction Motor Finite Control Set Model Predictive Torque Control Using Novel Fuzzy Adaptive Techniques

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