Genetic algorithms for design of optimal velocity tracking controllers including PTO efficiencies

Genetic algorithms use the ideas of Darwinian evolutionary theory to find the optimal solution to a design problem. Here they are utilised in two scenarios. Firstly, finding the optimal power take-off (PTO) force for maximising the electrical power output of a device by accounting for PTO efficiencies. The genetic algorithm finds a solution marginally faster than a brute forcing method with the added benefit of not being constrained to a discrete grid of test points, hypothetically leading to a more accurate result. Secondly, these optimal power take-off forces are used with another genetic algorithm to fit a transfer function for use as part of a previously designed adapted optimal velocity tracking controller that accounts for PTO efficiencies. Along with the reduced requirement for control engineering expertise, the resultant transfer function is found to have a smaller average phase error, when compared to a manually fitted transfer function. Simulations are undertaken that find that using a genetic algorithm derived transfer function results in approximately the same, or better energy capture when compared to the manually fitted transfer function, depending on the sea state, with the largest improvement being an increase of 5.93%. These methods form the basis of a potential control co-design methodology.