Path tracking control of a self‐driving wheel excavator via an enhanced data‐driven model‐free adaptive control approach

Liu, Shida; Hou, Zhongsheng; Tian, Taotao; Deng, Zhidong; Guo, Lei

doi:10.1049/iet-cta.2018.5493

Cited by 28 publications

(16 citation statements)

References 36 publications

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“…e attenuation rate, σ, of the error can be calculated by combining (24) and (25). en, the attenuation rate, σ, of the error is compared with the set threshold value of the attenuation rate, σ.…”

Section: Hp-adaptive Update Algorithmmentioning

confidence: 99%

“…Based on MPC, Regolin et al proposed a linear controller for tracking a given trajectory [24]. Liu et al proposed an adaptive control algorithm for path tracking control by considering time delays [25]. Jing et al designed three different controllers for the reverse motion of a tractor-trailer to solve the tractor-trailer path tracking problem for backwards motion [26].…”

Section: Introductionmentioning

confidence: 99%

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Optimum Path Tracking Control for the Inverse Problem of Vehicle Handling Dynamics Based on the hp-Adaptive Gaussian Pseudospectral Method

Liu

Sun

Cui

2021

Mathematical Problems in Engineering

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We propose a vehicle path-tracking method based on the hp-adaptive Gaussian pseudospectral method (GPM), which tackles the problem of the slow convergence speed of the optimal control of vehicle path tracking. First, we establish a kinematic vehicle model by considering the path constraints and boundary constraints during the process of tracking the described path of a vehicle. Subsequently, finding the minimum error of the lateral distance between the prescribed path and the expected trajectory is set as the performance objective function. Finally, the vehicle path tracking problem is transformed into an optimal control problem. The optimization algorithm is combined with the sequential quadratic programming algorithm to optimize problems related to the control and state variables and the boundary and path constraints. The simulated results show that the hp-adaptive GPM can improve the convergence rate of the optimal control problem for vehicle path tracking. The proposed hp-adaptive pseudospectral method has a higher solving efficiency compared with traditional approaches for the vehicle path-tracking problem. Concurrently, the verification results of a real vehicle test indicate the feasibility of the proposed algorithm for solving the vehicle path tracking problem. This research provides valuable insight into the design work of lane changes and is an important step towards the design of feedback control laws for path tracking.

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Section: Hp-adaptive Update Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimum Path Tracking Control for the Inverse Problem of Vehicle Handling Dynamics Based on the hp-Adaptive Gaussian Pseudospectral Method

Liu

Sun

Cui

2021

Mathematical Problems in Engineering

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“…Among these data‐driven methods, model‐free adaptive control (MFAC) approach has aroused a lot of attention because only the input and output data are used without employing explicit or implicit knowledge of the mathematic model. The MFAC method was first proposed by Hou 7 and has been continuously investigated and extended in these years 8‐11 . As a method to linearize nonlinear nonaffine system during MFAC, dynamic linearization (DL) technique has many advantages, such as data‐driven, just a few arguments are required to be renewed, and the obtained model is equal to the original system without any approximation 12 .…”

Section: Introductionmentioning

confidence: 99%

“…The MFAC method was first proposed by Hou 7 and has been continuously investigated and extended in these years. [8][9][10][11] As a method to linearize nonlinear nonaffine system during MFAC, dynamic linearization (DL) technique has many advantages, such as data-driven, just a few arguments are required to be renewed, and the obtained model is equal to the original system without any approximation. 12 Nevertheless, the data model obtained by the DL technique is proven to be over-linearized because no explicit nonlinear term is contained.…”

mentioning

confidence: 99%

Discrete‐time extended state observer‐based model‐free adaptive sliding mode control with prescribed performance

Huang

Dong

Zhang

et al. 2022

Intl J Robust & Nonlinear

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To tackle the trajectory tracking problem and achieve high control accuracy in many actual nonlinear systems with unknown disturbance, a novel discrete-time extended state observer-(DESO) based model-free adaptive sliding mode control strategy with prescribed tracking performance is studied, which only relies on the input/output data of the system rather than explicit model information. Firstly, a compact-form dynamic linearization method is used to reconstruct the discrete-time nonlinear process, where the time-varying parameter linearly connected with the control input is obtained by an adaptive method and the unknown nonlinear term is estimated by a DESO. Then, by considering the prescribed performance and using an unconstrained vector transformed from the constrained tracking error, one model-free sliding mode controller is designed. In addition, a rigorous stability analysis is presented to show the boundedness of the sliding mode function and the prescribed transient-state and steady-state performance of the output tracking error. Finally, the simulations with comparing results verify the effectiveness and superiority of the developed control scheme. K E Y W O R D Sdiscrete-time extended state observer, discrete-time system, model-free adaptive control, prescribed performance, sliding mode control INTRODUCTIONWith the continuous development of engineering technologies, the complexity of the practical system is intensively increasing, which leads to accurate system modeling becoming one of the most difficult tasks. 1 Thus, the control strategies based on mathematical models are not suitable for such complicated systems. To overcome this problem, data-driven control is proposed where only the input and output data are used. Data-driven methods have been applied in many practical scenes, for instance, quadrotor vehicles, 2 automated vehicles, 3 continuum robots, 4 and other industrial process systems. 5,6 Among these data-driven methods, model-free adaptive control (MFAC) approach has aroused a lot of attention because only the input and output data are used without employing explicit or implicit knowledge of the mathematic

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“…MFAC schemes are then implemented based on these equivalent virtual data models. In recent years, MFAC algorithms have been extensively applied in many fields, including wide‐area power systems [18], the syngas manufacturing industry [19], water level control systems [18–20], multiagent systems [21, 22], microgrids [23], and autonomous vehicles [24–26].…”

Section: Introductionmentioning

confidence: 99%

Model‐free adaptive control method for a class of unknown MIMO systems with measurement noise and application to quadrotor aircraft

Liu

Hou

Zhang

et al. 2020

IET Control Theory & Appl

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Path tracking control of a self‐driving wheel excavator via an enhanced data‐driven model‐free adaptive control approach

Cited by 28 publications

References 36 publications

Optimum Path Tracking Control for the Inverse Problem of Vehicle Handling Dynamics Based on the hp-Adaptive Gaussian Pseudospectral Method

Optimum Path Tracking Control for the Inverse Problem of Vehicle Handling Dynamics Based on the hp-Adaptive Gaussian Pseudospectral Method

Discrete‐time extended state observer‐based model‐free adaptive sliding mode control with prescribed performance

Model‐free adaptive control method for a class of unknown MIMO systems with measurement noise and application to quadrotor aircraft

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