LQR Pendulation Reduction Control of Ship-Mounted Crane Based on Improved Grey Wolf Optimization Algorithm

The poor adaptability matrix of traditional LQR controller causes the problems of large payload swing and slow response for ship-mounted cranes during operation. To solve such problems, an LQR controller based on an improved grey wolf optimization algorithm (IGWO-LQR) is proposed. Firstly, the dynamics model of ship-mounted crane is constructed, the pendulum reduction problem is transformed into the LQR quadratic performance index problem, and IGWO is used to optimize the weight matrix. At the same time, the RBF neural network is applied to compensate for the non-linear wave disturbances in the system. Finally, the pendulum reduction efficiency of the controller under different parameters and conditions is verified by numerical simulation. Compared with the traditional LQR controller, the simulation results show that the control accuracy of the IGWO-LQR controller is improved by about 5%, and the response speed is improved by about 5–10 s. This method can significantly reduce the payload swing and improve work efficiency.