The Electric Power System (EPS) and attitude control system (ACS) are the essential components of any satellite. EPS and ACS efficiency and compactness are substantial for the proper operation and performance of the satellite’s entire mission life. So, realizing the significance of EPS and ACS subsystems for any satellite, they have been assimilated and developed in modular forms focusing on efficiency and compactness. The EPS is comprised of three modules called the solar panel module (SPM), power conditioning module (PCM), and power distribution module (PDM) while the ACS has an embedded magnetorquer coil. For compactness and miniaturization purposes, the magnetorquer coil is embedded inside the SPM. The components used are commercial off-the-shelf (COTS) components emphasizing on their power efficiency, small dimensions, and weight. Latch-up protection systems have been designed and analyzed for CMOS-based COTS components, in order to make them suitable for space radioactive environment. The main design features are modularity, redundancy, power efficiency, and to avoid single component failure. The modular development of the EPS and ACS helps to reuse them for future missions, and as a result, the overall budget, development, and testing time and cost are reduced. A specific satellite mission can be achieved by reassembling the required subsystems.

Quantum behaved particle swarm optimization (QPSO) has been one of the most widely used algorithm in engineering world. Since its debut in 2004, QPSO has been used for resolving numerous complicated multimodal problems. Moreover, considering the adaptability and versatility, it has resolved a variety of real-world and test problems. To tackle numerical and engineering optimization problems, we introduce novel hybrid algorithm QPSODE. The novel hybrid algorithm integrates Quantum behaved particle swarm optimization (QPSO) with differential evolution (DE) strategy. A crossover and selection (influenced by DE) is used in the QPSODE’s position updating mechanism. During the selection process, the Boltzmann operator is applied to the position vectors of two randomly chosen particles, not to their individual optimum placements. Therefore, unlike the QPSO, a particle is only relocated to a new position if it has a higher fitness value, implying the application of a selection strategy across the whole search space. Additionally, the hybrid algorithm is improved by introducing proper parameters tuning, control parameter, path disparity. The hybrid algorithm enhances the algorithm’s performance by speeding up the convergence and avoiding the premature convergence, the main flaw in the earlier algorithms. The proposed algorithm is put to test, by using 19 well-known benchmark test functions and the engineering optimization problem for superconducting magnetic energy storage (SMES). In terms of the quality of the resulting outputs, QPSODE outperforms various state-of-the-art approaches.

Spacecrafts need to maneuver their solar panels towards the sun and antennas towards the ground station for maximum solar power harvesting and communication with the ground station. For tracking purpose, usually magnetorquer rods, reaction wheels and permanent magnets are used, but they are heavier, expensive, and occupy extra space on the spacecraft. Keeping in mind the dimension, budget and mass constraints of small satellites, a system compatible with small satellite is worth consideration. Consequently, this paper focuses on designing and analyzing a solar panel module with embedded Air-Coil. Such an Air-Coil is an innovative idea for the replacement of heavier, bulky and expensive attitude control systems. The proposed Air-Coil is integrated in the internal layers of an eight layers solar panel PCB module. Complete degradation analyses of the solar panel have been done to ensure that it will meet the satellite power requirements at BOL (beginning of life) and EOL (end of life). The proposed embedded Air-Coil has been analyzed for the generated magnetic moment, resultant torque, power consumption and temperature increase of the complete solar panel unit. A steady state thermal model is proposed to measure the thermal resistance between top and bottom layers of the solar panel module, which gives an idea about the heat trapped inside the solar panel module. The designed embedded Air-Coil is fully reconfigurable where coil in each layer can be operated as a separate coil. The four coils in different layers can be attached or detached through switches in different configurations i.e. single coil, four in series, four in parallel, and their hybrid combinations. The analyses of power consumption, heat dissipation, temperature rise, magnetic moment and torque generation by different configurations of the designed Air-Coil have been performed. The generated magnetic moment is very high i.e. around 12Am², which is enough to rotate a microsatellite by 90° in 200s. The efficacy of the proposed module is significantly higher than the already available systems with respect to mass, price, power dissipation, heat generation, and dimension.

A miniaturized attitude control system (ACS), which is capable of attaining a high spin rate and good pointing accuracy, is an integral part of any small spacecraft. In this regard, embedded magnetorquers are gaining significant attention because they are inserted in printed circuit board (PCB) internal layers with no extra space occupation and result in almost no mass and development cost. In this article, an innovative embedded magnetorquer with asymmetric nonuniform geometric structure is presented. The proposed magnetorquer is optimized for various parameters of interest, i.e., magnetic moment, resultant generated torque, power consumed, and heat dissipation for a CubeSat standard nanosatellite by means of employing diverse nonunity track-width-ratio geometrical arrangements. The magnetorquer driver and reconfigurable circuit is implemented with commercial-off-the-shelf (COTS) components which are low cost and easily available from the market. Embedded coils with variable trace width were simulated in order to predict the accuracy and to achieve the maximum torque versus power dissipation ratio of the developed models. Time-varying rotational analyses are performed to evaluate the angular spin rates driven by the asymmetric coils. Due to embedded coil structure, a detailed thermal analysis is conducted for various coil configurations (i.e., series, parallel, hybrid, etc.) to preserve thermal limits and to ensure the efficacy of the system. The simulation results were validated through a comprehensive set of experimental measurements which also establishes the framework for selecting the optimal coils configuration based on specific mission requirements.

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.