Model Predictive Control for Underwater Robots in Ocean Waves
نویسندگان
چکیده
Underwater robots beneath ocean waves can benefit from feedforward control to reduce position error. This paper proposes a method using Model Predictive Control (MPC) to predict and counteract future disturbances from an ocean wave field. The MPC state estimator employs a Linear Wave Theory (LWT) solver to approximate the component fluid dynamics under a wave field. Wave data from deployed ocean buoys is used to construct the simulated wave field. The MPC state estimator is used to optimize a set of control actions by gradient descent along a prediction horizon. The optimized control input minimizes a global cost function, the squared distance from the target state. The robot then carries out the optimized trajectory with an emphasis on real-time execution. Several prediction horizons are compared, with a horizon of 0.8 seconds selected as having a good balance of low error and fast computation. The controller with the chosen prediction horizon is simulated and found to show a 74% reduction in position error over traditional feedback control. Additional simulations are run where the MPC takes in noisy measurements of the wave field parameters. The MPC algorithm is shown to be resistant to sensor noise, showing a mean position error 44% lower than the noise-free feedback control case.
منابع مشابه
AN ABSTRACT OF THE THESIS OF Daniel C. Fernández for the degree of Master of Science in Robotics presented on September 14, 2015. Title: Model Predictive Control for Underwater Robots in Ocean Waves
approved: Geoffrey A. Hollinger Underwater robots beneath ocean waves can benefit from feedforward control to reduce position error. This thesis proposes a method using Model Predictive Control (MPC) to predict and counteract future disturbances from an ocean wave field. The MPC state estimator employs a Linear Wave Theory (LWT) solver to approximate the component fluid dynamics under a wave fi...
متن کاملRobust Trajectory Free Model Predictive Control of Biped Robots with Adaptive Gait Length
This paper employs nonlinear disturbance observer (NDO) for robust trajectory-free Nonlinear Model Predictive Control (NMPC) of biped robots. The NDO is used to reject the additive disturbances caused by parameter uncertainties, unmodeled dynamics, joints friction, and external slow-varying forces acting on the biped robots. In contrary to the slow-varying disturbances, handling sudden pushing ...
متن کاملEvaluation of underwater acoustic propagation model (Ray theory) in a river using Fluvial Acoustic Tomography System
Underwater acoustics is widely used in many applications, such as oceanography, marine biology, hydrography, fishery, etc. Different models are introduced to simulate the underwater acoustic propagation in the oceans and the seas. In this study, the Ray Theory model is used to simulate the acoustic wave propagation in a shallow-freshwater river (Gono River) located in western part of Japan. The...
متن کاملDevelopment of Hovering Type Underwater Robot for Ecological Surveillance
Recently, various forms of ocean energy such as wave, tidal, marine current, and ocean thermal energy conversion are considered as source of alternative clean energy because of the increased oil price, international political issues, and global warming issues, etc. For this reason, underwater robot is believed to play a key role in both development and protection of the ocean. Until now, underw...
متن کاملA topology control algorithm for autonomous underwater robots in three-dimensional space using PSO
Recently, data collection from seabed by means of underwater wireless sensor networks (UWSN) has attracted considerable attention. Autonomous underwater vehicles (AUVs) are increasingly used as UWSNs in underwater missions. Events and environmental parameters in underwater regions have a stochastic nature. The target area must be covered by sensors to observe and report events. A ‘topology cont...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- IEEE Robotics and Automation Letters
دوره 2 شماره
صفحات -
تاریخ انتشار 2017