A General Attainable-Set Model Predictive Control Scheme. Application to AUV Operations

Gomes, Rui; Pereira, Fernando Lobo

doi:10.1016/j.ifacol.2018.11.402

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…In theoretical studies, the number of iterations required for solving optimal control problems is often omitted, but in engineering practice, it is a critical factor when applying theoretical algorithms to real-world UUVs. Some researchers have recognized this issue and proposed corresponding solutions, such as event-triggered control [39], off-line precomputation [40], and distributed computation schemes [41]. However, the existing study of MPC applied in UUVs mainly focuses on hard constraints, while soft constraints are often omitted or considered less important in the cost function.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Prescribed Performance Tracking Control of UUV via PMPC and RBFNN-FTTSMC

Xia

et al. 2023

JMSE

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To address the search-and-docking problem in multi-stage prescribed performance switching (MPPS) scenarios, this paper presents a novel compound control method for three-dimensional (3D) underwater trajectory tracking control of unmanned underwater vehicles (UUVs) subjected to unknown disturbances. The proposed control framework can be divided into two parts: kinematics control and dynamics control. In the kinematics control loop, a novel parallel model predictive control (PMPC) law is proposed, which is composed of a soft-constrained model predictive controller (SMPC) and hard-constrained model predictive controller (HMPC), and utilizes a weight allocator to enable switching between soft and hard constraints based on task goals, thus achieving global optimal control in MPPS scenarios. In the dynamics control loop, a finite-time terminal sliding mode control (FTTSMC) method combining a finite-time radial basis function neural network adaptive disturbance observer (RBFNN-FTTSMC) is proposed to achieve disturbance estimation and fast convergence of velocity tracking errors. The simulation results demonstrate that the proposed PMPC-FTTSMC approach achieved an average improvement of 33% and 80% in the number of iterations compared with MPC with sliding mode control (MPC-SMC) and traditional MPC methods, respectively. Furthermore, the approach improved the speed of response by 35% and 44%, respectively, while accurately achieving disturbance observation and enhancing the system robustness.

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Section: Introductionmentioning

confidence: 99%

Three-Dimensional Prescribed Performance Tracking Control of UUV via PMPC and RBFNN-FTTSMC

Xia

et al. 2023

JMSE

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“…MPC implementations on AUVs include trajectory planning (Shen et al, 2015), trajectory tracking (Shen et al, 2016, 2019; Yan et al, 2020), formation control (Gomes & Pereira, 2018, 2019), manipulation (Nikou et al, 2018), and docking (Nielsen et al, 2018). Most of these developments have been demonstrated in simulation studies, and very few cases of experimental and field demonstrations exist.…”

Section: Introductionmentioning

confidence: 99%

“…First, the dimension of the optimization problem can be too large for real‐time control if the full dynamics model (with over 12 states) is used. This computational issue can be addressed by combining reduced order models of weakly interacting subsystems (Shen et al, 2016), or by generating precomputed lookup tables of attainable controls and trajectories (Gomes & Pereira, 2018). Second, uncertainty in state measurement and hydrodynamic modeling is high due to sensing constraints (low visibility, limited localization, lack of GPS) and the nature of the underwater environment (unsteady flow, currents, disturbances).…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

Bhat

Panteli

Stenius

et al. 2023

Journal of Field Robotics

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Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under‐ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV‐MPC) is derived from the nonlinear model to enable real‐time control. In simulations, NMPC and LTV‐MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise‐time, overshoot, steady‐state error, and robustness. The LTV‐MPC shows satisfactory real‐time performance in experimental validation. The paper further also demonstrates experimentally that LTV‐MPC can be run real‐time on the AUV in performing hydrobatic maneouvers.

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Enhancing AUV Autonomy with Model Predictive Path Integral Control

Nicolay,

Petillot,

Marfeychuk

et al. 2023

OCEANS 2023 - MTS/IEEE U.S. Gulf Coast

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A General Attainable-Set Model Predictive Control Scheme. Application to AUV Operations

Cited by 3 publications

References 16 publications

Three-Dimensional Prescribed Performance Tracking Control of UUV via PMPC and RBFNN-FTTSMC

Three-Dimensional Prescribed Performance Tracking Control of UUV via PMPC and RBFNN-FTTSMC

Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

Enhancing AUV Autonomy with Model Predictive Path Integral Control

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