Adaptive MPC trajectory tracking for AUV based on Laguerre function

Wang, Weiran; Yan, Jinghao; Wang, Hui; Ge, Huilin; Zhu, Zhiyu; Yang, Guanjun

doi:10.1016/j.oceaneng.2022.111870

Cited by 32 publications

(9 citation statements)

References 22 publications

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“…As shown in Figure 4, when the other control parameters remain unchanged, the larger the prediction time domain is, the larger the range predicted by the controller, which can obtain more lawn mower state information. However, if the prediction time domain is too large, it will increase the error of the lawn mower at a distant position, thereby reducing the tracking accuracy of the lawn mower at a nearby position [23]. In addition, an excessively large prediction time domain will also increase the computational complexity of the MPC algorithm and reduce the real-time performance of the system [24].…”

Section: Adaptive Time Domain Module Designmentioning

confidence: 99%

Research on Path Tracking for an Orchard Mowing Robot Based on Cascaded Model Predictive Control and Anti-Slip Drive Control

Wang

Zhang

et al. 2023

Agronomy

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In complex orchard environments, orchard mowing robots are prone to longitudinal slippage because of the characteristics of tires and the adhesion conditions of the road surface, which makes it difficult for the robots to maintain high-precision path tracking and autonomous navigation positioning. This not only affects the accuracy of path tracking but also leads to unstable motion for the mowing robots. To solve the above problems, we take an orchard mowing robot as the control object and establish a cascaded path-tracking controller and an adaptive time domain model based on a kinematics model. By designing a linear error model, an objective function, and constraint conditions for the mowing robot, the optimal linear velocity and angular velocity of the mower are obtained and converted into the speed of the driving wheel. Then, an anti-slip driving controller is designed based on fuzzy control of the slip rate. The slip-rate-based fuzzy controller is constructed according to the real-time speed of the mower and the reference speed of the driving wheel solved by the model predictive controller, and anti-slip driving control is implemented through a combination of a PID controller and a tire dynamics model. To verify the effectiveness of the proposed method, simulation and field experiments are conducted. The experimental results show that the slip rate of the driving wheel of the mower remains within the target slip rate range in the orchard working environment, avoiding excessive driving wheel sliding. Furthermore, the average lateral error of the path-tracking controller is controlled within 0.05 m, and the average value of the longitudinal error is kept within 0.04 m, which satisfies the control accuracy requirements of lawn mower operations. The proposed method provides a reference optimization scheme for improving the path-tracking and motion stability of a mowing robot.

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Section: Adaptive Time Domain Module Designmentioning

confidence: 99%

Research on Path Tracking for an Orchard Mowing Robot Based on Cascaded Model Predictive Control and Anti-Slip Drive Control

Wang

Zhang

et al. 2023

Agronomy

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“…Note that this means that we are doing a first order approximation to the real transition model f (s n , u n ) if f is non-linear, but in many cases, as our simulations will show, this approximation suffices [21,22]. Moreover, a linear model is required in order to use an MPC controller as the one in (15) [34].…”

Section: Estimation Of the Model And Disturbancesmentioning

confidence: 99%

An Efficient Underwater Navigation Method Using MPC with Unknown Kinematics and Non-Linear Disturbances

Barreno

Parras

Zazo

2023

JMSE

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Many Autonomous Underwater Vehicles (AUVs) need to cope with hazardous underwater medium using a limited computational capacity while facing unknown kinematics and disturbances. However, most algorithms proposed for navigation in such conditions fail to fulfil all conditions at the same time. In this work, we propose an optimal control method, based on a receding horizon approach, namely MPC (Model Predictive Control). Our model also estimates the kinematics of the medium and its disturbances, using efficient tools that rely on the use of linear algebra and first-order optimization methods. We also test our ideas using an extensive set of simulations, which show that the proposed ideas are very competitive in terms of cost and computational efficiency in cases of total and partial observability.

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“…This approach decreases computational costs in complex optimization problems. An adaptive predictive control method based on the Laguerre function is proposed to improve the performance of autonomous underwater vehicles in complex hydrological conditions . The method consists of an AMPC module for accurate tracking and a Laguerre function module to reduce computations.…”

Section: Introductionmentioning

confidence: 99%

“…An adaptive predictive control method based on the Laguerre function is proposed to improve the performance of autonomous underwater vehicles in complex hydrological conditions. 21 The method consists of an AMPC module for accurate tracking and a Laguerre function module to reduce computations. The RLS algorithm is used for identifying the model parameters to enhance accuracy and robustness.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Simulation Investigation of an Adaptive Model Predictive Control Scheme: Model Parametrized by Orthonormal Basis Function

Madakyaru

2024

ACS Omega

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The closed-loop system's performance in synthesizing model predictive control (MPC) heavily relies on the model used for prediction. In continuously operating plants, a linear model-based MPC is designed based on the operating point's linear model during the commissioning stage. However, if the plant requires significant transitions from its normal operating point, the linear model-based MPC may not be effective. Therefore, to maintain the MPC performance under changing nominal operating conditions, the model (deterministic and stochastic components) needs to be updated to predict every sampling instant. This study focuses on designing an adaptive MPC (AMPC) scheme based on the linear model estimated from the input−output perturbation data under nominal operating conditions. The OBF−ARX (generalized orthonormal basis filters with ARX structure) parametrizes the observer's dynamic components. The proposed fixed and variable pole AMPC schemes' efficacy is demonstrated using a simulation study on a binary distillation column and experimental evaluation studies on a benchmark two-tank heater setup. The efficacy of the proposed AMPC schemes in addressing both servo and regulator problems has been demonstrated through simulation and experimental results. Specifically, these schemes have been shown to effectively track set points while simultaneously rejecting disturbances. These findings suggest that the AMPC schemes hold promise for use in a variety of applications in which precise control is required.

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Adaptive MPC trajectory tracking for AUV based on Laguerre function

Cited by 32 publications

References 22 publications

Research on Path Tracking for an Orchard Mowing Robot Based on Cascaded Model Predictive Control and Anti-Slip Drive Control

Research on Path Tracking for an Orchard Mowing Robot Based on Cascaded Model Predictive Control and Anti-Slip Drive Control

An Efficient Underwater Navigation Method Using MPC with Unknown Kinematics and Non-Linear Disturbances

Experimental and Simulation Investigation of an Adaptive Model Predictive Control Scheme: Model Parametrized by Orthonormal Basis Function

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