A Survey of Swarm Intelligence-based Optimization Algorithms for Tuning of Cascade Control Systems: Concepts, Models and Applications

Abstract

Nowadays, cascade control is still one of the most used control strategies in the manufacturing and process industries. The new requirements of precision and robustness of position and trajectory tracking in control systems for manufacturing components at micro-scale, influenced by hard nonlinearities such as friction and backlash, have motivated the effort toward the development of algorithms for optimal tuning of control parameters. This paper presents a literature review of the algorithms and methods used to solve this problem. Swarm intelligence inspired optimization algorithms, namely particle swarm optimization algorithm (PSO) and grey wolf optimization algorithm (GWO), are applied for tuning of P-PI cascade controllers of CNC machine tool servo system in the presence of friction and backlash. The objective of the optimization is to minimize the maximum position error during the reversal of the axes. A comparative analysis of proposed algorithms with a standard industry-based fine tune (FT) method is also provided. Simulation study as well as real-world experiments carried out on a CNC machine tool controller show a remarkable improvement in the performance of the cascade control system using the proposed swarm intelligence-based strategy.