GRID-BASED ACO ALGORITHM FOR PARAMETERS TUNING OF NLPID CONTROLLER AND ITS APPLICATION IN FLIGHT SIMULATOR

Abstract

This paper presents an application of Ant Colony Optimization (ACO) algorithm to tune the parameters in the design of a novel type of nonlinear proportional-integral-differential (NLPID) controller, which is used in flight simulator. A differential tracker and a reference generator are included in the NLPID controller. The differential tracker is used to track output position and its differential, while the reference generator is used to generate reference input signals. ACO algorithm is a novel heuristic algorithm, which is based on the process of real ants in the nature searching for food. In order to tune the parameters of the NLPID controller using the new proposed grid-based ACO algorithm, an objective function based on position tracing error is constructed. The parameters tuning of NLPID can be summed up as the typical continual spatial optimization problem, grid-based searching strategy is adopted in the improved ACO algorithm, and self-adaptive control strategy for the pheromone decay parameter is also adopted. In order to enhance the searching speed, pair ants which searched from two different mesh points at the same time are used. The body structure and control system architecture of a type of flight simulator with the grid-based ACO algorithm optimized NLPID are also proposed. The simulation results demonstrate that both for the standard and random input signals, the tracking error is very small, and the whole control system with grid-based ACO algorithm optimized NLPID has quick response performance and strong robustness.