A Robust Predictive Control Approach for Underwater Robotic Vehicles

Heshmati-alamdari, Shahab; Karras, George C.; Marantos, Panos; Kyriakopoulos, Kostas J.

doi:10.1109/tcst.2019.2939248

Cited by 66 publications

(34 citation statements)

References 35 publications

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“…In [76], a set-theoretic MPC is proposed to deal with disturbances and constraints in an uncertain dynamic driving environment. In [77,78], a motion controller to track designated waypoints based on robust nonlinear MPC (NMPC) with constrained workspace is proposed, which guarantees the robustness of the system against model uncertainties. In [79], the authors propose a motion control method using an integrated MPC and PID controller to deal with the changing velocity effects, in which a radial basis function-extreme learning machine (RBF-ELM) neural network is designed for high-accuracy prediction and an RBF neural network is used to tune the PID controller.…”

Section: Motion Control Of Single Agvs Using Mpcmentioning

confidence: 99%

Model predictive control for autonomous ground vehicles: a review

Hirche

Huang

et al. 2021

Auton. Intell. Syst.

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This paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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Section: Motion Control Of Single Agvs Using Mpcmentioning

confidence: 99%

Model predictive control for autonomous ground vehicles: a review

Hirche

Huang

et al. 2021

Auton. Intell. Syst.

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“…In addition, by employing all of the aforementioned motion control strategies, it is not always feasible or straightforward to incorporate input (generalized body forces/torques or thrust) and state (3D obstacles, velocities) constraints into the vehicle's closed-loop motion [20]. In that sense, the trajectory control problem of underwater robots continues to pose considerable challenges to system designers, especially in view of the high-demanding missions envisioned by the marine industry (e.g., surveillance of oil platforms, cable tracking, etc.).…”

Section: Introductionmentioning

confidence: 99%

“…In the aforementioned studies, the validation of the proposed strategies was conducted via simple simulation tests. Experimental validation of a NMPC scheme for robust stabilization of an AUV was presented in [20].…”

Section: Introductionmentioning

confidence: 99%

Robust Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles in Uncertain Environments

Heshmati-alamdari

Νίκου

Dimarogonas

2021

IEEE Trans. Automat. Sci. Eng.

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115

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This paper addresses the tracking control problem of 3D trajectories for underactuated underwater robotic vehicles operating in a constrained workspace including obstacles. More specifically, a robust Nonlinear Model Predictive Control (NMPC) scheme is presented for the case of underactuated Autonomous Underwater Vehicles (AUVs) (i.e., unicycle-like vehicles actuated only in surge, heave and yaw). The purpose of the controller is to steer the unicycle-like AUV to a desired trajectory with guaranteed input and state constraints (e.g., obstacles, predefined vehicle velocity bounds, thruster saturations) inside a partially known and dynamic environment where the knowledge of the operating workspace is constantly updated via the vehicle's on-board sensors. In particular, considering the sensing range of the vehicle, obstacle avoidance with any of the detected obstacles is guaranteed, by on-line generation of a collision-free trajectory tracking path, despite the model dynamic uncertainties and the presence of external disturbances representing ocean currents and waves. Finally, realistic simulation studies verify the performance and efficiency of the proposed framework.

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“…To improve the robustness against uncertainty, it is necessary to design a robust controller. In (Heshmati-Alamdari et al, 2020), a robust predictive controller is designed for underwater robotic vehicles which forms a high robust closed-loop system against parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Robust Vehicle Dynamics Control for a Sharp Curve With Uncertain Road Condition

et al. 2021

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Recently, more and more research has been conducted to develop Connected Autonomous Vehicles (CAVs) applications that ensures the safety driving of CAVs under some extreme situations. This brief presents a robust control strategy for CAVs to preserve a precise tracking performance and maintain the stability of lateral dynamics when passing a sharp curve with uncertain road friction coefficient changes. In the proposed robust lateral dynamics control, robust optimization-based lateral dynamics controller is designed to achieve the stability of the lateral dynamics with the consideration of the road friction coefficient uncertainty. Simulation validations are carried out to evaluate the proposed control strategy. The results show that the robust optimization-based lateral dynamics can improve the robustness even with the uncertainty of the road friction coefficient.

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A Robust Predictive Control Approach for Underwater Robotic Vehicles

Cited by 66 publications

References 35 publications

Model predictive control for autonomous ground vehicles: a review

Model predictive control for autonomous ground vehicles: a review

Robust Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles in Uncertain Environments

Robust Vehicle Dynamics Control for a Sharp Curve With Uncertain Road Condition

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