Research On The Optimal Thrust Allocation Of The DP Semi-submersible Platform Based On Particle Bee Colony Algorithm

With the development of science and technology, mankind has entered the era of ocean economy, the development and exploration of the ocean has been ranged more widely Traditional anchor position has failed to meet the needs of working on the sea. Dynamic positioning system emerges as the times requires. It’s a closed-loop control system which can resist environmental disturbance and keep ship and platform steadily in the expected position as far as possible with the power of propellers. Thrust allocation system plays an important role in the DPS. Its role is to allocate the speed and angle of propellers, the speed and angle are expected from control system. The optimal problem of thrust allocation can be seen as a nonlinear constrained problem. The optimal thrust allocation is not only good for enhancing the DPS performance, but also playing an active role to reduce the error of the thrust and the energy consumption.The engineering background of this dissertation is a semi-submersible platform equipped with eight azimuth thrusters. For its thrust allocation problem, the mathematic model is established. In the past, the methods such as SQP are applied to solve this kind of problems, but SQP has the disadvantage of local convergence and poor robustness. Artificial bee colony (ABC) is a relatively novel swarm intelligence based algorithm. According to the development trend of algorithms, we apply ABC algorithm to solve the optimal thrust allocation problem and hope to obtain a better result.ABC has the advantage of global convergence, simple structure and less control parameters. However, it also has the disadvantage of premature and not very fast convergence rate. In order to solve the thrust allocation problem better, in this dissertation, we make several improvements on traditional ABC algorithm and propose a new algorithm called PHPBC. The main improvements include adopting global optimal mechanism of PSO to speed up convergence, adopting adjustable pressure selection to maintain the diversity of the algorithm at the early periods and accelerate the convergence at the late periods, adopting the idea of parallel evolution to suppress premature and enhance the global searching capabilities. In order to exanimate the performance of the PHPBC, firstly we apply it to the classical function optimization problems and results demonstrate the proposed algorithm is feasible and effective. And then the proposed algorithm is applied to the optimization problem of DP platform thrust allocation whose mathematical model is established above. Two typical environments are selected for making the corresponding simulation tests. Relevant simulation proves that the PHPBC can allocate the expected value of the controller system reasonably and the results are satisfied. Furthermore, two different parameters of the proposed algorithm are selected for comparing and analyzing its optimization performance in different cases.Related work show that the PHPBC is effective for thrust allocation optimization problems and it can obtain better solutions. This study has a certain theoretical significance and application values.