Hybrid bacterial foraging and particle swarm optimisation for fuzzy precompensated control of flexible manipulator

This paper presents hybrid approach combining the social foraging behaviour of Escherichia coli bacteria and particle swarm optimisation for optimising hybrid fuzzy precompensated proportional-derivative (PD) controller in trajectory control of two-link rigid-flexible manipulator. Numerical simulation using the dynamic model of the two-link rigid-flexible manipulator shows the effectiveness of the approach in trajectory tracking problems. The use of fuzzy precompensation has superior performance in terms of improvement in transient and steady state response, robustness to variations in loading conditions and ease to use in practice. Comparative evaluation with respect to genetic algorithm, particle swarm and bacterial foraging-based optimisation is presented to validate the controller design. The proposed algorithm performs local search through the chemotactic movement operation of bacterial foraging whereas the global search over the entire search space is accomplished by a particle swarm operator and so satisfactory tracking precision could be achieved using the approach.