Longitudinal speed control of autonomous vehicle based on a self‐adaptive PID of radial basis function neural network

Nie, Lei; Guan, Jianguo; Lu, Chihua; Zheng, Hao; Yin, Zhishuai

doi:10.1049/iet-its.2016.0293

Cited by 84 publications

(46 citation statements)

References 25 publications

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“…The PID controller’s input is the deviation of the actual steering angle from its target, while the output is the current to be executed by the electric motor. Three parameters k P , k I , k D of the PID controller are tuned adaptively with the RBFNN [ 35 , 36 ].…”

Section: The Rbfnn-pid Front Wheel Angle Tracking Controllermentioning

confidence: 99%

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

Yin

Huang

2020

Sensors

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This paper presents a two-layer controller for accurate and robust lateral path tracking control of highly automated vehicles. The upper-layer controller, which produces the front wheel steering angle, is implemented with a Linear Time-Varying MPC (LTV-MPC) whose prediction and control horizon are both optimized offline with particle swarm optimization (PSO) under varying working conditions. A constraint on the slip angle is imposed to prevent lateral forces from saturation to guarantee vehicle stability. The lower layer is a radial basis function neural network proportion-integral-derivative (RBFNN-PID) controller that generates electric current control signals executable by the steering motor to rapidly track the target steering angle. The nonlinear characteristics of the steering system are modeled and are identified on-line with the RBFNN so that the PID controller’s control parameters can be adjusted adaptively. The results of CarSim-Matlab/Simulink joint simulations show that the proposed hierarchical controller achieves a good level of path tracking accuracy while maintaining vehicle stability throughout the path tracking process, and is robust to dynamic changes in vehicle velocities and road adhesion coefficients.

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Section: The Rbfnn-pid Front Wheel Angle Tracking Controllermentioning

confidence: 99%

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

Yin

Huang

2020

Sensors

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“…The existing NN-PID methods include Back Propagation Neural Network based PID (BPNN-PID) and Radical Basis Function Neural Network based PID (RBFNN-PID) [24], [25], etc. The problems of the existing BPNN-PID and RBFNN-PID are as follows: (1) Some training processes of RBFNN-PID are unstable, which means that the system output cannot converge in the end of the control process.…”

Section: A Related Workmentioning

confidence: 99%

Multiple Linear Regression Prediction and Wavelet Neural Network Based Intelligent Online Tuning Control Method

Liu

Zhao

et al. 2020

IEEE Access

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Manual-based parameters tuning methods of control system are widely used in many industrial fields. However, better control performances such as faster control speed, more stable, less workload and safer working conditions are expected. Therefore, artificial intelligent based improved classical control (AI-CC) is highly valued in control fields. Main works are as follows: Firstly, wavelet neural network based PID (WNN-PID) method is proposed, BPNN of BPNN based PID method (BPNN-PID) is replaced by WNN. Secondly, multiple linear regression-based wavelet neural network PID (MR-WNN-PID) is proposed, the control parameters are adjusted according to the predicted output of control system. Thirdly, simulations are implemented to compare the performance of the proposed methods and the existing methods. Finally, the stability of the proposed methods is analyzed by theory. Effects are as follows: Firstly, the proposed AI-CC methods have better performances such as faster control speed, smaller overshoot, better ability of antiinterference. Secondly, theoretical analysis of stability of proposed methods is proven. INDEX TERMS Control parameters online tuning; Artificial intelligent control; Auto-tuning control; Wavelet neural network PID, Multiple linear regression-based prediction I.

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“…The PID controllers have been implemented in vehicles where the desired speed varies to satisfy changing needs, such as the 2005 DARPA Grand Challenge winner Stanley (Thrun et al, 2006). In order to operate under an increasing number of requirements, PID controllers have been modified so that their gains are adjusted via gain scheduling or adaptive control (Ioannou et al, 1993, Ioannou and Sun, 2012, Nie et al, 2018. Steering control algorithms have seen an even wider array of control methods.…”

Section: Throttle/brake and Steering Controllersmentioning

confidence: 99%

Artificial intelligence and machine learning for autonomous military vehicles

Vecherin¹,

Desmond²,

Hodgdon³

et al. 2020

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The U.S. Army Engineer Research and Development Center (ERDC) solves the nation's toughest engineering and environmental challenges. ERDC develops innovative solutions in civil and military engineering, geospatial sciences, water resources, and environmental sciences for the Army, the Department of Defense, civilian agencies, and our nation's public good. Find out more at www.erdc.usace.army.mil. To search for other technical reports published by ERDC, visit the ERDC online library at https://erdclibrary.on.worldcat.org/discovery.

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Longitudinal speed control of autonomous vehicle based on a self‐adaptive PID of radial basis function neural network

Cited by 84 publications

References 25 publications

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

Multiple Linear Regression Prediction and Wavelet Neural Network Based Intelligent Online Tuning Control Method

Artificial intelligence and machine learning for autonomous military vehicles

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