Improved ant colony optimization for achieving self-balancing and position control for balancer systems
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ORIGINAL RESEARCH
Improved ant colony optimization for achieving self‑balancing and position control for balancer systems Rupam Singh1 · Bharat Bhushan1 Received: 11 April 2020 / Accepted: 18 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The balancer systems represent feedback in loop-based underactuated system which is electromechanical, multivariate, and nonlinear. This paper develops a self-balancing controller using an improved ant colony optimization (ACO) to optimize the proportional integral derivative controller (PID) controller. The proposed controller achieves self-balancing control for a ball on the plate by controlling the plate inclination angle. Initially, the modelling of the ball balancer system is achieved with the help of a two degree of freedom (2DoF) ball balancer system controlled by a PID controller. Further, ACO is employed to autonomously evaluate the condition of a process and find the optimal tuning parameters for the PID controller. The transition probability of the ACO is revised to improve the response and convergence speed of the algorithm resulting in an improved ACO. The developed control schemes were applied with the 2DoF ball balancer model both in simulation as well as for the real-time operation. The results depicted the performance of the proposed control scheme by analysing the characteristics such as transient response and steady-state error. Further, stability analysis has been done for the developed control schemes using describing function method for multiple frequencies. The results depicted the superiority of the improved ACO based PID controller over the conventional PID controller. Keywords Self-balancing control · Ball balancer setup · Proportional integral derivative control · Ant colony optimization
1 Introduction The approximation of underactuated nonlinear systems through automatic decision development and intelligent control methods (Murray et al. 2003) is an issue that appears in many problems (Nelles 2001) and can be tackled through various approaches. The diversity and complexity of these systems have led researchers in the field to analyse the action of various linear, nonlinear, model-free, passivity, and intelligent controllers. These controllers are focussed at achieving self-balancing control and steady-state operation for various systems like inverted pendulum (Boubaker 2012; Moness et al. 2020), the twin rotor multi input multi output system (TRMS) (Chalupa et al. 2015), the ball beam system (Andreev et al. 2002; Nowopolski 2013), hovercraft (Aranda * Rupam Singh [email protected] Bharat Bhushan [email protected] 1
Department of Electrical Engineering, Delhi Technological University, Shahbad Daulatpur, Delhi 110042, India
et al. 2006), Furuta pendulum (Acosta 2010), and ball and plate system (Awtar et al. 2002). In general, linear controllers offer a simple way of designing closed-loop control for these systems. Various linear controllers were available in the literature to solve the problems in under
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