Optimization of Sliding Mode and Back-Stepping Controllers for AMB Systems Using Gorilla Troops Algorithm

Abstract

An active magnetic bearing (AMB) is a frictionless bearing used in high-speed motors and other electromechanical products. Due to its open loop instability, utilization of controller is essential to stabilize the system. In this paper, a comparative study between sliding mode control (SMC) and back-stepping control (BSC) are presented for AMB systems. These two controller techniques have been applied for various dynamical systems to obtain stable control systems. On the basis of avoiding the chattering in the SMC design, the power rate reaching is introduced in the design of the control action of SMC. In terms of BSC design, Lyapunov-stability theorem is utilized to derive the control low of the controller. A gorilla troops optimization (GTO) has been applied to tune the adjustable parameters of the proposed controllers. According to the computer simulation based on MATLAB software, the results indicate a superior performance and improved in the system response of the SMC as compared to the BSC controller. In addition, the SMC strategy has a good disturbance rejection capability as compared to the BSC strategy.

An Optimized PID Controller Using Enhanced Bat Algorithm in Drilling Processes

“…Nowadays, there is an increasing trend of using optimization in solving industrial problems and engineering research [17][18][19][20][21]. Taking advantage of recent optimization techniques, several tuning methods based on swam based optimization has been proposed for finding the best setting of numerous controllers [22,23]. In the context of finding the optimal tuning of the PID controller in drilling process, different swarm optimization techniques have been proposed such as Simulated Annealing (SA) [11], PSO [13], WOA [15] and Enhanced Flower Pollination (EFP) [16].…”