To cite this article: Daqi Zhu , Qian Liu & Zhen Hu (2011) Fault-tolerant control algorithm of the manned submarine with multi-thruster based on quantum-behaved particle swarm optimisationA thruster reconfiguration control approach of manned submarine with 7000 m operation depth based on quantum-behaved particle swarm optimisation (QPSO) is presented in this article. The manned submarine has eight thrusters. When thruster faults happen, the corresponding weight matrix is updated to restrict the usage of the faulty thruster. But the solution with this method may become unfeasible (exceed the rated valve of the thruster) and cannot be directly applied to the thrusters. In order to complete an appropriate control law reconfiguration, a novel control reallocation method based on QPSO is proposed. Not only is the solution obtained by the approach of QPSO limited in the whole feasible space, but also the control error of the fault-tolerant control is very small. Eight dimensions of the particles are used in this article, and each particle represents the components of the control vector, it searches in the range of the restricted factor value to make sure that all the reconfiguration control solutions are feasible. Compared to the weighted pseudo-inverse method, the error of the obtained control vector with the QPSO is very small. Finally, simulation examples of multi-uncertain faults are given to illustrate the advantages of the proposed method.
A B S T R A C TDevelopment of the deep manned submersible Jiaolong was one of the key projects of the National 863 high-tech program. The program's target was to build a practical product to carry out a set of predefined missions. The 7,000 m sea trial was the last stage of the development process, a final phase that incorporated higher risks. During the preceding periods from August 6 to October 20, 2009, May 31 to July 18, 2010, and July 1 to August 18, 2011, the 1,000, 3,000, and 5,000 m depth class tests were successfully completed. The main purpose of this paper is to introduce the results of the 7,000 m depth class test, which was conducted from June 3 to July 16, 2012. The paper describes the general sea trial procedures, and technological and scientific achievements realized during the tests, main faults experienced in the field, along with corresponding troubleshooting methods and suggestions for future application of the Jiaolong submersible. In addition, several conclusions are drawn from all the experiences gained from these sea trials.
The trajectory tracking of Autonomous Underwater Vehicles (AUV) is an important research topic. However, in the traditional research into AUV trajectory tracking control, the AUV often follows human-set trajectories without obstacles, and trajectory planning and tracking are separated. Focusing on this separation, a trajectory re-planning controller based on Model Predictive Control (MPC) is designed and added into the trajectory tracking controller to form a new control system. Firstly, an obstacle avoidance function is set up for the design of an MPC trajectory re-planning controller, so that the re-planned trajectory produced by the re-planning controller can avoid obstacles. Then, the tracking controller in the MPC receives the re-planned trajectory and obtains the optimal tracking control law after calculating the object function with a Sequential Quadratic Programming (SQP) optimisation algorithm. Lastly, in a backstepping algorithm, the speed jump can be sharp while the MPC tracking controller can solve the speed jump problem. Simulation results of different obstacles and trajectories demonstrate the efficiency of the proposed MPC trajectory re-planning tracking control algorithm for AUVs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.