## 1 Introduction
A model reference adaptive control approach for an UVMS was proposed in [20]. [PERSON] et al. [21] proposed an adaptive controller based on virtual decomposition; however, a regressor matrix corresponding to parameter vector is required in this method. An indirect adaptive controller based on the extended Kalman filter (EKF) was proposed in [22]; meanwhile the performance would be degraded due to the estimated error via EKF. To eliminate the bias from the EKF estimation, [PERSON] et al. [23] introduced a Hoo control in the indirect adaptive controller to achieve robust performance. However, this method results in a residual error when the bounds of the disturbance cannot be known prior. To reduce or omit the estimation error of the uncertainties and disturbances, a fuzzy compensator based on estimations was utilized [24]. To handle state and input constraints of the UVMS, a robust predictive control (RMPC) [24], a nonlinear MPC (NMPC) [25], a tube-based robust MPC [26], a fast MPC (FMPC) [27] and a fast tube MPC (FTMPC) [28] were used for the UVMS trajectory tracking, but these MPC approaches do not permit the self-motion utilized to perform energy efficient trajectory tracking. For achieving precise and robust performance, the control design should be enhanced by the estimations of the system's uncertainties and disturbances. The popular estimation techniques include time delay control (TDC) [29; 30], the extended state observer (ESO) [31], the disturbance observer [32], the nonlinear disturbance observer (NDO) [33; 34] the uncertainty and disturbance estimator (UDE) [35; 36] (redefined as inertial delay control (IDC) [37]) and so on. Among them, because IDC is simple in design and easy to complete, it is widely used to estimate the effect of the lumped uncertainties and disturbances. Generally, the IDC is applied to the sliding mode control (SMC) for ensuring precise and robust performance. The combined method does not require the bounds of uncertainties and disturbances, and it does not use the discontinuous function in the control law. However, the above-mentioned methods are based on joint-space variables, which may not be suitable for a variety of underwater tasks with high-precision end-effector position requirements [38]. These task space control schemes can easily adapt to the online modification of the end-effector's motion [39; 40]. However, the task-space controllers also have disadvantages. (a) The kinematic redundancy of the UVMS cannot be exploited. (b) The output of the task-space controller should be mapped into the joint space so as to be realized by thrusters and actuators. [PERSON] et al. [34] proposed a hybrid strategy-based coordinated controller for the UVMS. The hybrid strategy is to transform the joint-space controller (to exploit the system's redundancy) to the task-space controller (to ensure high-accuracy tracking performance). Inspired by the above studies, new motion planning and coordinated control schemes of the UVMS are proposed in this paper. The contribution of this work is that the proposed scheme can ensure precise, energy-efficient and robust performance in the presence of model uncertainties, external disturbances, payload and sensory noises. First, a new redundancy resolution technique is proposed, where a new secondary task with a nonlinear function is inserted for generating energy-saving trajectories for the vehicle and manipulator. Second, an EKF estimation system is employed for resisting sensory noises. Third, a coordinated motion control with joint-space errors, end-effector errors, IDC and a fuzzy compensator is proposed as a robust tracking controller against uncertainties and disturbances. Last, the effectiveness of the proposed scheme is verified through numerical simulations. The rest of the paper is organized as follows. Section 2 is concerned with the kinematic and dynamic modeling of the UVMS. In Section 3, an improved redundancy resolution technique is presented. The proposed control scheme is proposed in Section 4.