Stochastic Model Predictive Control for the Set Point Tracking of Unmanned Surface Vehicles

Yuan, Tun; Cai, Guorong; Li, Bin; Teo, Kok Lay; Wang, Song

doi:10.1109/access.2019.2962061

Cited by 9 publications

(5 citation statements)

References 37 publications

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“…Stochastic model predictive control approaches were inspired by this type of systems, in which δ and w are stochastic in nature, independent and with known probability distributions. Since this statistical information is taken into account in the solution of the OCP [18][19][20][21], stochastic predictive control has been widely accepted and has been applied in different areas such as building air conditioning [37][38][39], renewable energy management [40,41], process control [3,42], robotics and automotive [5,22,[43][44][45]. A more extensive review of these and other applications is presented in [18,19,21,25,46], where network control systems, air traffic, finance, path planning and training control are discussed.…”

Section: Stochastic Mpcmentioning

confidence: 99%

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A Comparative Study of Stochastic Model Predictive Controllers

et al. 2020

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A comparative study of two state-of-the-art stochastic model predictive controllers for linear systems with parametric and additive uncertainties is presented. On the one hand, Stochastic Model Predictive Control (SMPC) is based on analytical methods and solves an optimal control problem (OCP) similar to a classic Model Predictive Control (MPC) with constraints. SMPC defines probabilistic constraints on the states, which are transformed into equivalent deterministic ones. On the other hand, Scenario-based Model Predictive Control (SCMPC) solves an OCP for a specified number of random realizations of uncertainties, also called scenarios. In this paper, Classic MPC, SMPC and SCMPC are compared through two numerical examples. Thanks to several Monte-Carlo simulations, performances of classic MPC, SMPC and SCMPC are compared using several criteria, such as number of successful runs, number of times the constraints are violated, integral absolute error and computational cost. Moreover, a Stochastic Model Predictive Control Toolbox was developed by the authors, available on MATLAB Central, in which it is possible to simulate a SMPC or a SCMPC to control multivariable linear systems with additive disturbances. This software was used to carry out part of the simulations of the numerical examples in this article and it can be used for results reproduction.

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Section: Stochastic Mpcmentioning

confidence: 99%

“…Model Predictive Control (MPC) is a widely used strategy for the control of industrial processes [1][2][3], robotics and automation [4][5][6], energy efficiency of buildings and renewable energies [7][8][9][10][11]. This is due to its "ability to predict" the future behavior of the real process, using an explicit model of it.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Stochastic Model Predictive Controllers

et al. 2020

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“…Furthermore, it has concurrently optimized closed-loop performance with reinforcement learning-based and system-identification methods. To convert chance constraints into deterministic convex constraints, a convex conditional value of risk approximation has been introduced [27]. The converted constraints were further transformed into second-order cone constraints.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Predictive Control for Unmanned Surface Vehicles with Improved Nonlinear Disturbance Observer

Fu,

Yao,

Cajo

et al. 2023

JMSE

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The motion of unmanned surface vehicles (USVs) is frequently disturbed by ocean wind, waves, and currents. A poorly designed controller will cause failures and safety problems during actual navigation. To obtain a satisfactory motion control performance for the USVs, a model predictive control (MPC) method based on an improved Nonlinear Disturbance Observer (NDO) is proposed. First, the USV model is approximately linearized and MPC is designed for the multivariable system with constraints. To compensate for the influence of disturbances, an improved NDO is designed where the calculation time for MPC is reduced. Finally, comparison simulations are conducted between MPC with the original NDO and MPC with an improved NDO, and the results show that they have similar performances to the USVs. However, the proposed method has fewer parameters that need to be tuned and is much more time-saving compared to MPC with a traditional NDO.

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“…Moreover, a model-based event-triggered control method is proposed by Deng et al [27], where a neural networks approximator is designed to estimate uncertainties from both internal system and external environment. To process the external environment disturbances, a control scheme based on model predictive method is proposed by Tan et al [28] with the consideration of USV's physical constraints, which can be calculated and implemented online. To improve the tracking performance and robustness, a control strategy with the integrate consideration of portcontrolled hamiltonian method and back-stepping method is proposed by Lv et al [29], which provides the experience to develop the comprehensive application of multiple control methods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control Design for Nonlinear Unmanned Surface Vessel Using RBFNN and Disturbance-Observer

et al. 2020

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Unmanned surface vessel(USV) has been applied in the maritime security inspection and resources exploration to execute complex works with its advantages in automation and intelligence. While the nonlinear USV working in the complex ocean environment, the good trajectory tracking performance is an important capacity. However, the nonlinearity, modeling uncertainties (e.g., modeling error and parameter variations) and external disturbance (wind, wave, current, etc) are the main difficulties, which deteriorates the control performance. To solve this issue, most existing algorithms for USV's tracking have been developed based on the linearization of the USV's nonlinear dynamic model at specific equilibrium point. However, the integrated effect of nonlinearities, modeling uncertainties and external disturbance has not been well considered, which can degrade the USV's tracking performance. Therefore, to achieve the good tracking performance for USV, a nonlinear dynamic model is strictly derived in this paper with the integrate consideration of abovementioned issues, and an adaptive sliding mode control design using RBFNN(Radial Basis Function Neural Network) and disturbance-observer is subsequently developed, where a RBFNN approximator is designed to approximate and compensate modeling uncertainties, and a disturbance-observer is designed to estimate and compensate the effect of the external disturbance. Furthermore, the global stability of the overall closed-loop system of USV are strictly guaranteed. The comparative simulation is carried out to validate the fast response, better transient performance and robustness of our proposed control design via comparing with the existing methods. INDEX TERMS Adaptive sliding mode control, neural network, unmanned surface vessel(USV), Lyapunov stability theorem, disturbance observer.

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Stochastic Model Predictive Control for the Set Point Tracking of Unmanned Surface Vehicles

Cited by 9 publications

References 37 publications

A Comparative Study of Stochastic Model Predictive Controllers

A Comparative Study of Stochastic Model Predictive Controllers

Trajectory Tracking Predictive Control for Unmanned Surface Vehicles with Improved Nonlinear Disturbance Observer

Adaptive Sliding Mode Control Design for Nonlinear Unmanned Surface Vessel Using RBFNN and Disturbance-Observer

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