Improved Model Predictive-Based Underwater Trajectory Tracking Control for the Biomimetic Spherical Robot under Constraints

Abstract

To improve the autonomy of the biomimetic sphere robot (BSR), an underwater trajectory tracking problem was studied. Considering the thrusters saturation of the BSR, an improved model predictive control (MPC) algorithm that features processing multiple constraints was designed. With the proposed algorithm, the kinematic and dynamic models of the BSR are combined in order to establish the predictive model, and a new state-space model is designed that is based on an increment of the control input. Furthermore, to avoid the infeasibility of the cost function in the MPC controller design, a new term with a slack variable is added to the objective function, which enables the constraints to be imposed as soft constraints. The simulation results illustrate that the BSR was able to track the desired trajectory accurately and stably while using the improved MPC algorithm. Furthermore, a comparison with the traditional MPC shows that the designed MPC-based increment of the control input is small. In addition, a comparative simulation using the backstepping method verifies the effectiveness of the proposed method. Unlike previous studies that only focused on the simulation validations, in this study a series of experiments were carried out that further demonstrate the effectiveness of the improved MPC for underwater trajectory tracking of the BSR. The experimental results illustrate that the improved MPC is able to drive the BSR to quickly track the reference trajectory. When compared with a traditional MPC and the backstepping method used in the experiment, the proposed MPC-based trajectory is closer to the reference trajectory.

Keywords

• biomimetic spherical robot
• trajectory tracking control
• constraint
• model predictive control