Robust Adaptive Control of an Uninhabited Surface Vehicle
Robust Adaptive Control of an Uninhabited Surface Vehicle
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DOI：
10.1007/s10846-014-0057-2
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发表时间：
2015-05-01
影响因子：
3.3
通讯作者：
Sharma, S.
中科院分区：
文献类型：
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作者：
Annamalai, A. S. K.;Sutton, R.;Sharma, S.
提出了一种基于模型预测控制器（MPC）的无人水面航行器（USV）鲁棒自适应自动驾驶仪。新型自动驾驶仪能够处理系统动态的突然变化。在真实的生活情况下，动力学的突然变化经常导致任务中止，并且必须在无人驾驶车辆对附近的其他船舶造成损害之前对其进行救援。这个问题已经通过这种创新设计得到了适当的解决。MPC采用在线自适应性质，分别利用三种算法：梯度下降，最小二乘和加权最小二乘（WLS）。即使是随机初始化，显着的改善，其他算法的方法，实现了WLS通过保持系统参数的间歇性连续值和定期重新初始化的协方差矩阵。此外，25秒的时间范围似乎是在模拟研究中重新初始化参数的最佳时间范围。这种新颖的方法使自动驾驶仪能够很好地科普系统动态的重大变化，并使USV能够完成其期望的任务。
A robust adaptive autopilot for uninhabited surface vehicles (USV) based on a model predictive controller (MPC) is presented in this paper. The novel autopilot is capable of handling sudden changes in system dynamics. In real life situations, very often a sudden change in dynamics results in missions being aborted and the uninhabited vehicles have to be rescued before they cause damage to other marine craft in the vicinity. This problem has been suitably dealt with by this innovative design. The MPC adopts an online adaptive nature by utilising three algorithms, individually: gradient descent, least squares and weighted least squares (WLS). Even with random initialisation, significant improvements over the other algorithmic approach were achieved by WLS by maintaining the intermittent continuous values of system parameters and periodically reinitialising the covariance matrix. Also, a time frame of 25 seconds appears to be the optimum to reinitialise the parameters in simulation studies. This novel approach enables the autopilot to cope well with significant changes in the system dynamics and empowers USVs to accomplish their desired missions.