Research on Adaptive Model Predictive Control Algorithm for Remotely Operated Vehicle

Abstract

Aiming at the motion state coupling and model non-linearity of remotely operated vehicle (ROV) caused by the asymmetric structural layout of ROV and changes of center of gravity and center of buoyancy, this paper proposes a model identification algorithm considering the typical nonlinear characteristics of ROV. A model predictive control (MPC) method and its fast optimization solution method based on operator partition quadratic programming were studied and deduced. A mathematical simulation system including the nonlinear mathematical model of ROV, the parameter identification algorithm module and the MPC algorithm module were developed. The simulation was carried out under the scenarios of considering and ignoring nonlinear characteristics, and changing the position of center of buoyancy in the ROV nonlinear mathematical model. The effectiveness and advancement of the parameter identification algorithm proposed in this paper were verified. On the basis of the identification model, the simulation compared and analyzed the effect of underwater ROV attitude control under 4 scenarios, including the MPC algorithm-based identification model considering and ignoring the typical nonlinear characteristics of underwater ROV, MPC algorithm based on direct solution and MPC algorithm based on fast optimization approximate solution proposed in this paper. The effectiveness of the control algorithm proposed in this paper is verified.

Keywords

• auto-regressive with extra inputs (arx)
• nonlinear model identification
• model predictive control
• rov(remotely operated vehicle)
• adaptive control