International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-2, July 2019 4205 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: B3404078219/19©BEIESP DOI: 10.35940/ijrte.B3404.078219 Abstract: This paper attempts to use GSO algorithm to tune a PID controller that can be used to control a satellite using reaction wheels. These have a higher order transfer function and the controller will be more difficult to tune due to this. To do this, a satellite is chosen which controls its attitude using reactions wheels. An axis is chosen and the reaction wheel along this axis is taken into consideration. A PID controller is then attached to this system. PID (Proportional-Integral-Derivative) are one of the most popular controllers used due to their broad applications and easy to design nature. The PID controller is tuned using Glowworm Swarm Optimization (GSO) algorithm and then the system is checked against a step input. The optimization of the controller is done by minimizing the time weighed absolute error cost function. Index Terms: Glowworm Swarm Optimisation (GSO) Algorithm, Optimisation, PID Tuning, Satellite attitude control I. INTRODUCTION Satellites today carry a wide variety of loads and there are many launched each year with new methods and new hardware for attitude control. Reaction wheels have proven to be one of the best methods for attitude adjustment of a satellite using no extra fuel, but only electricity to run its motors. Electricity, which can be readily made available using solar power available on almost all satellites. In references [4] and [8], they have attempted to optimize a PID controller for 2 different cases using Fruit Fly Optimization algorithm. In the papers [12], [2] and [3], PSO algorithms are used to tune PID controllers used in different systems. The results they achieved are positive, however the system transfer functions were of a lower order than the ones in the case of a satellite. Reference [9] tests the PSO algorithm on various fitness functions and finds it to be on par with the other, though sometimes it could not arrive at the optimum value. It highlights the problems of choosing the correct fitness function and shows how increasing the generation size after a certain level only results in marginal improvements in the result. Reference [1] compares PSO with Zeigler Nichols and PSO obtains better results. This paper is an attempt to control a satellite using GSO, which can find the global optima, as opposed to PSO, which may sometimes arrive on the local optima and miss the global Revised Manuscript Received on July 15, 2019. Pulkit Sharma, Department of Aeronautical and Automobile Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India 576104. Vishnu G Nair, Department of Aeronautical and Automobile Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India 576104. optimum. This paper is an attempt to control a satellite’s attitude using Glowworm Swarm Optimisation. The same is then done using Genetic Algorithm for comparison. The satellite is assumed to be a rigid body and the dynamics is expressed in the form of a MIMO system. One of the SISO systems is then selected from these. This SISO system is then connected with a classic PID controller. The optimization is carried out using GSO, which is used to find the optimal values of the PID constants. II. METHODS In a chosen satellite with reaction wheels, one axis is chosen and our PID controller is attached to it. This controller is then tuned using GSO algorithm to find the optimum values of the PID constants. This is then compared with the constants obtained using genetic algorithm. This is done by deriving the dynamics equation of the satellite and reaction wheels, the disturbance is modelled and then attached with it the transfer function of the PID controller. A. Glowworm Swarm Optimisation (GSO) This is an evolutionary optimization that is used to find the global maxima or minima of a given multi-modal function. It is based on the glowworm in which the less luminous glow worms move towards the more luminous glow worms that are nearby [6]. The algorithm of GSO consists of several steps defining how each glowworm behaves and how they are used to determine the peaks of a multi-modal function. There are three basic mechanisms at work [6]. Fitness broadcast is carried in the luminescent pigment of the glowworm, called luciferin. It is proportional to the value of the function at their current position and this works under the assumption that the luciferin sensed by other glowworms does not change with distance [6]. A glowworm tends to move towards its neighbour if the neighbour glows brighter than itself. In case of multiple such neighbours, a probabilistic mechanism is used [6]. A glowworm will take another glowworm to be in its neighbourhood only if it is within its neighbourhood radius. This radius is modulated by using a heuristic and it can only vary within a certain range [6]. GSO starts by placing n well dispersed glowworms in the workspace. All the glowworms initially contain an amount l o of luciferin. Each cycle consists of three phases, namely the luciferin update phase, the movement phase and the neighbourhood range update phase. These phases together form the GSO algorithm [6]. Tuning of PID Controller using Glowworm Swarm Optimisation on a Satellite Attitude Control Reaction Wheel Pulkit Sharma, Vishnu G Nair,