Ant Colony Optimization Based Fuzzy Sliding Mode Controller for the Twin Rotor MIMO system

The ant colony optimization (ACO) algorithm is an evolutionary computational technique based on the behavior of a set of ants that communicate through the deposit of pheromone. It involves a node choice probability which is a function of pheromone strength and inter-node distance to construct a path through a node-arc graph. The algorithm allows fast near optimal solutions to be found compared to other stochastic methods. In this paper, a novel approach for angel positions control of a Twin Rotor Multi input–multi output System (TRMS) by means of a sliding mode controller (SMC), fuzzy logic controller (FLC) and ACO is investigated. For decreasing the chattering problem of the SMC, a FLC is used to replace the discontinuity in the sign function of the reaching law in the SMC. For selecting sliding surface constants and FLC input/output membership function parameters, the basic ACO algorithm is employed to search these parameters. To verify the feasibility and robustness of the control method, simulation results from a TRMS with the designed controller are given as an illustration. Furthermore, the performance comparisons with the Proportional Integral Differential (PID) controller tuned using the same method are provided to show that the control method has better performance in the aspect of tracking error and control signals energy.