Single Neuron PID Control of Agricultural Robot Steering System Based on Online Identification

Jiao, Jun; Chen, Jing; Qiao, Yaojun; Wang, Wenzhou; Wang, Chao; Gu, Lichuan

doi:10.1109/bigdataservice.2018.00036

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…The improved SNPID controller was adaptive and had autonomous learning capability to adjust the value of each component weighting coefficient ω i online, which can adaptively adjust the control intensity of proportional, integral, and differential links of PID. Single-neuron theory is an effective way to solve this problem for variable fertilization with strong coupling and natural instability [42]. The block diagram of the adaptive SNPID control system based on the learning correction of weighting coefficients is shown in Figure 5.…”

Section: Weighted Coefficient Learning-modified Single-neuron Pid Con...mentioning

confidence: 99%

Single-Neuron PID UAV Variable Fertilizer Application Control System Based on a Weighted Coefficient Learning Correction

Yao

et al. 2022

Agriculture

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Agricultural unmanned aerial vehicles (UAVs), which are a new type of fertilizer application technology, have been rapidly developed internationally. This study combines the agronomic characteristics of rice fertilization with weighted coefficient learning-modified single-neuron adaptive proportional–integral–differential (PID) control technology to study and design an aerial real-time variable fertilizer application control system that is suitable for rice field operations in northern China. The nitrogen deficiency at the target plot is obtained from a map based on a fertilizer prescription map, and the amount of fertilizer is calculated by a variable fertilizer application algorithm. The advantages and disadvantages of the two control algorithms are analyzed by a MATLAB simulation in an indoor test, which is integrated into the spreading system to test the effect of actual spreading. A three-factor, three-level orthogonal test of fertilizer-spreading performance is designed for an outdoor test, and the coefficient of variation of particle distribution Cv (a) as well as the relative error of fertilizer application λ (b) are the evaluation indices. The spreading performance of the spreading system is the best and can effectively achieve accurate variable fertilizer application when the baffle opening is 4%, spreading disc speed is 600 r/min, and flight height is 2 m, with a and b of evaluation indexes of 11.98% and 7.02%, respectively. The control error of the spreading volume is 7.30%, and the monitoring error of the speed measurement module is less than 30 r/min. The results show that the centrifugal variable fertilizer spreader improves the uniformity of fertilizer spreading and the accuracy of fertilizer application, which enhances the spreading performance of the centrifugal variable fertilizer spreader.

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Section: Weighted Coefficient Learning-modified Single-neuron Pid Con...mentioning

confidence: 99%

Single-Neuron PID UAV Variable Fertilizer Application Control System Based on a Weighted Coefficient Learning Correction

Yao

et al. 2022

Agriculture

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“…The single neuron controllers have three inputs, which are the proportional, derivative, and integral errors. The output of the neuron represents the control action (Rivera-Meja, Léon-Rubio & Arzabala-Contreras, 2012;Jiao et al, 2018;Tang et al, 2020). The multilayer controllers consist of a network with one hidden layer and one node at the output layer.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural PD controllers for mobile manipulator trajectory tracking

Hernández-Barragán¹,

Rios²,

Gómez-Avila³

et al. 2021

PeerJ Computer Science

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Artificial intelligence techniques have been used in the industry to control complex systems; among these proposals, adaptive Proportional, Integrative, Derivative (PID) controllers are intelligent versions of the most used controller in the industry. This work presents an adaptive neuron PD controller and a multilayer neural PD controller for position tracking of a mobile manipulator. Both controllers are trained by an extended Kalman filter (EKF) algorithm. Neural networks trained with the EKF algorithm show faster learning speeds and convergence times than the training based on backpropagation. The integrative term in PID controllers eliminates the steady-state error, but it provokes oscillations and overshoot. Moreover, the cumulative error in the integral action may produce windup effects such as high settling time, poor performance, and instability. The proposed neural PD controllers adjust their gains dynamically, which eliminates the steady-state error. Then, the integrative term is not required, and oscillations and overshot are highly reduced. Removing the integral part also eliminates the need for anti-windup methodologies to deal with the windup effects. Mobile manipulators are popular due to their mobile capability combined with a dexterous manipulation capability, which gives them the potential for many industrial applications. Applicability of the proposed adaptive neural controllers is presented by simulating experimental results on a KUKA Youbot mobile manipulator, presenting different tests and comparisons with the conventional PID controller and an existing adaptive neuron PID controller.

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“…• Single neuron PID controllers Examples of this group are works [6,[18][19][20][21]. These adaptive controllers are base on a single neuron whose inputs are the proportional error (P), integral of the error (I), and derivative of the error (D) (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

et al. 2020

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In this paper, an adaptive single neuron Proportional–Integral–Derivative (PID) controller based on the extended Kalman filter (EKF) training algorithm is proposed. The use of EKF training allows online training with faster learning and convergence speeds than backpropagation training method. Moreover, the propose adaptive PID approach includes a back-calculation anti-windup scheme to deal with windup effects, which is a common problem in PID controllers. The performance of the proposed approach is shown by presenting both simulation and experimental tests, giving results that are comparable to similar and more complex implementations. Tests are performed for a four wheeled omnidirectional mobile robot. Tests show the superiority of the proposed adaptive PID controller over the conventional PID and other adaptive neural PID approaches. Experimental tests are performed on a KUKA® Youbot® omnidirectional platform.

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Single Neuron PID Control of Agricultural Robot Steering System Based on Online Identification

Cited by 5 publications

References 12 publications

Single-Neuron PID UAV Variable Fertilizer Application Control System Based on a Weighted Coefficient Learning Correction

Single-Neuron PID UAV Variable Fertilizer Application Control System Based on a Weighted Coefficient Learning Correction

Adaptive neural PD controllers for mobile manipulator trajectory tracking

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

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