Fuzzy knowledge-based fractional order PID control implementation with nature inspired algorithms

Abstract

In this paper, we attempt to hybridise nature inspired optimisation techniques with fuzzy knowledge-based proportional integral derivative (PID) control for applications on fractional order systems. Two nature inspired approaches, namely, genetic algorithm and ant colony algorithms have been employed for tuning the parameters of the fuzzy knowledge-based fract-order PID controller offline. In the next stage, we fine tune the PID controller parameters using a fuzzy knowledge-based formulation. In our proposed nature inspired fractional fuzzy PID (NIFFPID) framework, GA has been used for optimising the inputs to the ANT controller. We illustrate effectiveness of our methodology by simulation results on four plants: one integer order and three fractional order ones having different orders. Simulation results and comparison of our approach against other approaches, viz., fractional order PID-ANT, fractional order PID-GA, fuzzy fractional PID-ANT and fuzzy fractional PID-GA, shows feasibility and effectiveness of our approach for fract order systems.