Extended-state-observer-based distributed model predictive formation control of under-actuated unmanned surface vehicles with collision avoidance

Lv, Guanghao; Peng, Zhouhua; Liu, Lu; Wang, Dan; Li, Tieshan

doi:10.1016/j.oceaneng.2021.109587

Cited by 51 publications

(7 citation statements)

References 56 publications

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“…Zhu applied the DMPC algorithm based on the alternating direction method of multipliers (ADMM) to the branching open canal irrigation systems 9 . This kind of research has received many applications in practice, such as multi‐vehicle formation control and cooperative tracking control 10–12 . Compared with centralized control, distributed model predictive control is more complex and difficult to control, requiring more computational, communication and control strategies, as well as more refined design and control algorithms.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based event‐triggered distributed model predictive control for a class of nonlinear interconnected systems

Li,

Su,

Guo

2024

Intl J Robust & Nonlinear

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In this paper, an observer‐based event‐triggered distributed model predictive control method is proposed for a class of nonlinear interconnected systems with bounded disturbances, considering unmeasurable states. First of all, the state observer is constructed. It is proved that the observation error is bounded. Second, distributed model predictive controller is designed by using observed value. Meanwhile, the event‐triggered mechanism is set by using the error between the actual output and the predicted output. The setting of event‐triggered mechanism not only ensures the error between the actual output and the predicted output within a certain range, but also reduces the calculation amounts of solving the optimization problem. The states of each subsystem enter the terminal invariant set by distributed model predictive control, and then are stabilized in the invariant set under the action of output feedback control law. In addition, sufficient conditions are given to ensure the feasibility of the algorithm and the stability of the closed‐loop system. Finally, the numerical example is given, and the simulation results verify the effectiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Observer‐based event‐triggered distributed model predictive control for a class of nonlinear interconnected systems

Li,

Su,

Guo

2024

Intl J Robust & Nonlinear

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“…To avoid collisions between USVs and obstacles, as well as among USVs themselves, in recent years, many substantial formation collision avoidance control schemes have been proposed by researchers for different practical problems (including unmeasurable state, limited resource-saving, model uncertainty, precision control, actuator nonlinearity, robust control, etc.). To mention a few, the output feedback formation control problem [37,38] is studied for USV fleets in the presence of unmeasurable states while considering collision avoidance and maintaining connectivity. Based on the onboard sensor systems, Dai et al in [39][40][41] studied precise formation control strategies for multiple USVs using prescribed performance techniques to ensure collision avoidance and connectivity maintenance.…”

Section: Introductionmentioning

confidence: 99%

Distributed Formation Maneuvering Quantized Control of Under-Actuated Unmanned Surface Vehicles with Collision and Velocity Constraints

Wang,

2024

JMSE

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This paper focuses on a distributed cooperative time-varying formation maneuvering issue of under-actuated unmanned surface vehicles (USVs). A fleet of USVs is guided by a parameterized path with a time-varying formation while avoiding collisions and preserving the connectivity in the environment with multiple obstacles. In some surface missions, due to the obstacles in the external environment, the bandwidth limitations of the communication channel, and the hardware components/performance constraints of the USVs themselves, each vehicle is considered to be subject to model uncertainty, actuator quantization, sensor dead zone, and velocity constraints. During the control design process, the radial basis function (RBF) neural networks (NNs) are utilized to deal with nonlinear terms. Based on a nonlinear decomposition method, the relationship between the control signal and the quantization one is established, which overcomes the difficulty arising from actuator quantization. A Nussbaum function is introduced to handle the unknown output dead zone problem caused by reduced sensor sensitivity. Moreover, a universal-constrained function is employed to satisfy both the constrained and unconstrained requirements during formation keeping and obstacle avoidance. The Lyapunov stability theory confirmed that the error signals are uniformly ultimately bounded (UUB). The simulation results demonstrate the effectiveness of the proposed distributed formation control of multiple USVs.

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“…Accordingly, various effective control methods of extend state observers have been proposed, including linear extended state observer, 46 nonlinear extended state observers, 47,48 reduced-order extended state observer, 18 full-order extended state observer, 49 event-triggered extended state observer, 50 and distributed extended state observers. [51][52][53] Nevertheless, the above studies 18,[46][47][48][49][50][51][52][53] are based on some prior knowledge of the input gains relevant to system parameters. The estimation of the system parameters requires a lot of data from practical experiments and may suffer from variations by load changes, which is tedious and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the control performance, it is desirable to estimate uncertainties and unknown external disturbances accurately and cancel them in the feedback loop. Accordingly, various effective control methods of extend state observers have been proposed, including linear extended state observer, 46 nonlinear extended state observers, 47,48 reduced‐order extended state observer, 18 full‐order extended state observer, 49 event‐triggered extended state observer, 50 and distributed extended state observers 51‐53 . Nevertheless, the above studies 18,46‐53 are based on some prior knowledge of the input gains relevant to system parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Further, the corresponding discrete‐time controller design and stability analysis method are investigated, which can be more practical. By using the CL technique, a new discrete‐time anti‐disturbance sampling controller design is proposed for the first time. In contrast to the ESOs proposed in References 18,46‐53, where the ASV control input gain needs to be known, two discrete‐time CLESOs are proposed, which can estimate not only the uncertain total disturbance, but also the unknown control input gain. Therefore, the proposed adaptive sampling control laws are able to applied in anti‐disturbance control design of ASVs since no information about the model parameters needs to be known in advance. In contrast to the parameter identification schemes for discrete‐time systems, 66,67 where the convergence of parameters can be guaranteed under a PE condition, the proposed discrete‐time CLESOs based on historical and current data can ensure the convergence of parameter estimation with relaxed finite excitation conditions.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive anti‐disturbance sampling control of autonomous surface vehicles based on discrete‐time concurrent learning extended state observers

Cui

et al. 2023

Intl J Robust & Nonlinear

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Summary This paper investigates the surge speed tracking control of an autonomous surface vehicle (ASV) subject to fully unknown internal dynamic, external disturbance, and unknown control input gain. An adaptive anti‐disturbance sampling control method is proposed for an ASV without using any model parameters. Specifically, by utilizing real‐time and historical data, discrete‐time reduced‐order and full‐order concurrent learning extended state observers (CLESOs) are designed to estimate the unknown ASV model parameters and ensure estimation convergence without requiring persistent excitation. Then, a surge speed tracking control law is designed based on the discrete‐time full‐order CLESO. Through Lyapunov stability analysis, the closed‐loop system is proven to be stable, and the estimation and tracking errors are bounded. Simulation and hardware‐in‐loop experimental results validate the effectiveness of the proposed discrete‐time CLESOs for the surge speed tracking of an ASV with fully unknown dynamic model.

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Extended-state-observer-based distributed model predictive formation control of under-actuated unmanned surface vehicles with collision avoidance

Cited by 51 publications

References 56 publications

Observer‐based event‐triggered distributed model predictive control for a class of nonlinear interconnected systems

Observer‐based event‐triggered distributed model predictive control for a class of nonlinear interconnected systems

Distributed Formation Maneuvering Quantized Control of Under-Actuated Unmanned Surface Vehicles with Collision and Velocity Constraints

Adaptive anti‐disturbance sampling control of autonomous surface vehicles based on discrete‐time concurrent learning extended state observers

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