Quantum potential swarm optimization of PD controller for cargo ship steering - Loo, C. K.; Mastorakis, Nikos E. (2007)
This paper delineates the first attempt to combine the ideas from Centroidal Voronoi Tessellation (CVT), Quantum-Oscillator based Particle Swarm Optimization (QOPSO) and Quantum Clustering (QC) to realize a novel optimization approach named as Quantum Potential Swarm Optimization (QPOSO) based on quantum mechanics principle. The particles in standard PSO move along a determined trajectory in Newtonian mechanics, but in QPOS, the particles will exhibit quantum behaviour and bound to work in different principle. In addition, Centroidal Voronoi Tessellation (CVT) is implemented to distribute numerous quantum particles uniformly to ensure full coverage of search space. Quantum potential wells can be induced from the solutions given by quantum particles using quantum clustering technique. The strategic starting positions of quantum particle are then selected based on the minimal of quantum potential wells. The implementation of QPOSO to optimize a PD-type autopilot for a cargo ship is presented. The tuning of the PD controller parameters are considered to be difficult and tedious due to the high nonlinearity of the ship dynamic model and the external disturbances. However, QPOSO can provide a very promising technique for its simplicity and ease of use. The promising results from the experiment provide direct evidence for the feasibility and effectiveness of QPOSO for autopilot control of cargo ship.