A control system for driving an Autonomous Underwater Vehicle (AUV) performing docking operations in presence of tidal current disturbances is proposed. The nonlinear model of the vehicle has been modelled in a Linear Parameter-Varying (LPV) form. This is suitable for the design of the control system using a model-based approach. The LPV model was used for a Model Predictive Control (MPC) design for computing the set of forces and moments driving the nonlinear vehicle model. The LPV-MPC control action is mapped into the reference signals for the actuators by using a Thrust Allocation (TA) algorithm. This was based on the nonlinear models for the actuators and their position and orientation on the vehicle’s hull. The structural decomposition of MPC and TA reduces the computational burden involved in computing the control law on-line on an embedded control board. Both MPC and TA algorithms use the vehicle’s linear and angular positions, and velocities that are estimated by an LPV based Kalman Filter (KF). The proposed control system has been tested in different docking scenarios using various tidal current disturbances acting on the vehicle as an unmeasured disturbance. The simulation results show the controller is effective in controlling the AUV over the range of control scenarios meeting the constraints and specifications.

中文翻译：

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停靠场景的AUV线性参数变化模型预测控制

提出了一种在潮汐电流干扰下驱动对接操作的自主水下航行器（AUV）的控制系统。车辆的非线性模型已经以线性参数变化（LPV）形式建模。这适合使用基于模型的方法设计控制系统。LPV模型用于模型预测控制（MPC）设计，以计算驱动非线性车辆模型的力和力矩集。LPV-MPC控制动作通过使用推力分配（TA）算法映射到执行器的参考信号中。这是基于执行器及其在车体上的位置和方向的非线性模型。MPC和TA的结构分解减少了嵌入式控制板上在线计算控制定律所涉及的计算负担。MPC和TA算法都使用车辆的线性和角位置以及由基于LPV的卡尔曼滤波器（KF）估算的速度。所提出的控制系统已经在各种对接场景中进行了测试，使用各种潮汐电流干扰作为无法测量的干扰作用在车辆上。仿真结果表明，该控制器可在满足约束条件和规格要求的控制场景范围内有效控制AUV。所提出的控制系统已经在各种对接场景中进行了测试，使用各种潮汐电流干扰作为无法测量的干扰作用在车辆上。仿真结果表明，该控制器可在满足约束条件和规格要求的控制场景范围内有效控制AUV。所提出的控制系统已经在各种对接场景中进行了测试，使用各种潮汐电流干扰作为无法测量的干扰作用在车辆上。仿真结果表明，该控制器可在满足约束条件和规格要求的控制场景范围内有效控制AUV。