Planning the trajectory of an autonomous wheel loader and tracking its trajectory via adaptive model predictive control

Shi, Jing; Sun, Dongye; Qin, Datong; Hu, Minghui; Ma, Ke; Chen, Ruibo

doi:10.1016/j.robot.2020.103570

Cited by 41 publications

(22 citation statements)

References 25 publications

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“…Yin et al, [33] presented a data-driven multi-objective predictive control approach to increase power production and reduce fatigue loads in wind farms using evolutionary optimization. Shi et al, [34] analyzed the autonomous vehicle trajectory planning methods and constructed an adaptive model predictive control (AMPC) trajectory tracking system which considers disturbances in the path curvature. Gao et al, [35] proposed a data-driven predictive control strategy for a nonlinear system, tested on a continuous stirred tank heater (CSTH) benchmark.…”

Section: A Recent Advancements In Crane Spreader and Cargo Stabilizationmentioning

confidence: 99%

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

2021

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Smart control systems are mostly applied in industry to control the movements of heavy machinery while optimizing overall operational efficiency. Major shipping companies use large quay cranes to load and unload containers from ships and still rely on the experience of on-site operators to perform transportation control procedures using joysticks and visual contact methods. This paper presents the research results of an EU-funded project for the Klaipeda container terminal to develop a novel container transportation security and cargo safety assurance method and system. It was concluded that many risks arise during the container handling procedures performed by the quay cranes and operators. To minimize these risks, the authors proposed controlling the sway of the spreader using a model predictive control method which applies a multi-layer perceptron (MLP) neural network (NN). The paper analyzes current neural network architectures and case studies and provides the engineering community with a unique case study which applies real operation statistical data. Several key training algorithms were tested, and the initial results suggest that the Levenberg-Marquardt (LM) algorithm and variable learning rate backpropagation perform better than methods which use the multi-layer perceptron neural network structure.

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Section: A Recent Advancements In Crane Spreader and Cargo Stabilizationmentioning

confidence: 99%

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

2021

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“…In the aspect of mathematical modeling, the kinematics model of the downhole articulated scraper has been widely used in the field of path tracking because of its simple motion mechanism and the ability to obtain accurate model [6].…”

Section: Mathematical Model Of Underground Articulated Lhd (Scraper)mentioning

confidence: 99%

“…To sum up, the block diagram of LQR-AGA control system can be drawn shown as Figure 7[41]. Used for the simulation of the path as shown in Figure 8, for wavy roadway, the halfway point of the cross section of the roadway in the attachment for the ideal of scraper run path which control target path, this path has continuous turning and other complex road conditions, so for the controller detection needs to have a strict conditions, to embody the scraper movement in actual operation [42]. In addition, in order to ensure the safe operation of the scraper, the maximum lateral deviation, namely the safe distance, should be set within 0.6m [43].…”

Section: E Parameter Selectionmentioning

confidence: 99%

Research on LHD (Scraper) Path Tracking Control Based on LQR and Predict Pose Information

Zhang¹,

Zhao²,

Wang³

et al. 2021

Preprint

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With the depletion of shallow surface resources, the future mining work will develop towards the deep surface, and the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient exploitation of deep space cannot be separated from such mobile and flexible production and transportation equipment as scraper. In the new era, intelligence is the development trend of the LHD, and path tracking control technology is the key to the intelligent LHD, and it is also an urgent problem to be solved for its unmanned driving. This paper describes the realization of the automatic operation function of articulated LHD from two aspects of mathematical model and trajectory tracking control method, and focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, LQR controller. On this basis, the parameters of the LQR controller are optimized by combining different intelligent cluster algorithms to find the optimal solution of the LQR controller.

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“…Some of the researchers focused only on the kinematic model to design controllers [3][4][5][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Sharma et al's work [4] is a comparison between two kinematic models for model predictive control, first one was Successive linear model and second one was an error based linear kinematic model, Kanayama et al [5] used feedback linearization controller to control position; Nascimento et al [10] used a new nonlinear model predictive control approach and achieved better results with regards to the classical control approaches in which he assumed that each position and velocity given by the trajectory generator is the state of a virtual target to be tracked; Al Khatib et al [11] show a comparison of a feedback linearization controller (Input-Output State Feedback Linearization: IO-SFL), Adaptive Proportional Controller (APC) and a Nussbaum function based Adaptive controller (NA); Merabti et al's work [12] is a comparison of metaheuristics (particle swarm optimization, ant colony optimization, and gravitational search algorithms) to solve an MPC problem; [13][14][15] used sliding mode control; [16][17][18][19][20] used adaptive control to eliminate the control parameter problem; [20][21][22][23][24] used model predictive control techniques and had great success. However, as mentioned in [25], in mobile robotics, it is a good practice to include the dynamics of the system w...…”

Section: Introductionmentioning

confidence: 99%

The Impact of the Dynamic Model in Feedback Linearization Trajectory Tracking of a Mobile Robot

Benchouche

Mellah

Bennouna

2021

Period. Polytech. Elec. Eng. Comp. Sci.

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This paper proposes the impact of the Dynamic model in Input-Output State Feedback Linearization (IO-SFL) technique for trajectory tracking of differential drive mobile robots, which has been restricted to using just the kinematics in most of the previous approaches. To simplify the control problem, this paper develops a novel control approach based on the velocity and position control strategy. To improve the results, the dynamics are taken into account. The objective of this paper is to illustrate the flaws unseen when adopting the kinematics-only controllers because the nonlinear kinematic model will suffice for control design only when the inner velocity (dynamic) loop is faster than the slower outer control loop. This is a big concern when using kinematic controllers to robots that don’t have a low-level controller, Arduino robots for example. The control approach is verified using the Lyapunov stability analysis. MATLAB/SIMULINK is carried out to determine the impact of the proposed controller for the trajectory tracking problem, from the simulation, it was discovered that the proposed controller has an excellent dynamic characteristic, simple, rapid response, stable capability for trajectory-tracking, and ignorable tracking error. A comparison between the presence and absence of the dynamic model shows the error in tracking due to dynamic system that must be taken into account if our system doesn’t come with a built-in one, thus, confirming the superiority of the proposed approach in terms of precision, with a neglectable difference in computations.

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Planning the trajectory of an autonomous wheel loader and tracking its trajectory via adaptive model predictive control

Cited by 41 publications

References 25 publications

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

Research on LHD (Scraper) Path Tracking Control Based on LQR and Predict Pose Information

The Impact of the Dynamic Model in Feedback Linearization Trajectory Tracking of a Mobile Robot

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