Collision-avoidance steering control for autonomous vehicles using neural network-based adaptive integral terminal sliding mode

2020 ◽  
Vol 39 (3) ◽  
pp. 4689-4702
Author(s):  
Zhe Sun ◽  
Jiayang Zou ◽  
Defeng He ◽  
Zhihong Man ◽  
Jinchuan Zheng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Collision Avoidance ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
Kinematic Model ◽  
Avoidance Performance ◽  
Steering Control ◽  
Terminal Sliding Mode ◽  
Control Scheme

Due to the complex driving conditions confronted by an autonomous vehicle, it is significant for the vehicle to possess a robust control system to achieve effective collision-avoidance performance. This paper proposes a neural network-based adaptive integral terminal sliding mode (NNAITSM) control scheme for the collision-avoidance steering control of an autonomous vehicle. In order to describe the vehicle’s lateral dynamics and path tracking characteristics, a two-degrees-of-freedom (2DOF) dynamic model and a kinematic model are adopted. Then, an NNAITSM controller is designed, where a radial basis function neural network (RBFNN) scheme is utilized to online approximate the optimal upper bound of lumped system uncertainties such that prior knowledge about the uncertainties is not required. The stability of the control system is proved via Lyapunov, and the selection guideline of control parameters is provided. Last, Matlab-Carsim co-simulations are executed to test the performance of the designed controller under different road conditions and vehicle velocities. Simulation results show that compared with conventional sliding mode (CSM) and nonsingular terminal sliding mode (NTSM) control, the proposed NNAITSM control scheme owns evident superiority in not only higher tracking precision but also stronger robustness against various road surfaces and vehicle velocities.

Multi-Agent Tracking of Non-Holonomic Mobile Robots via Non-Singular Terminal Sliding Mode Control

Robotica ◽  
2019 ◽  
Vol 38 (11) ◽  
pp. 1984-2000 ◽  
Author(s):  
Bilal M. Yousuf ◽  
Abdul Saboor Khan ◽  
Aqib Noor
Keyword(s):  
Mobile Robots ◽  
Formation Control ◽  
Sliding Mode ◽  
External Disturbance ◽  
Tracking Error ◽  
Kinematic Model ◽  
Tracking Problem ◽  
Terminal Sliding Mode ◽  
Control Scheme ◽  
Multi Agent

SUMMARYThis paper deals with the problem of the formation control of nonholonomic mobile robots in the leader–follower scenario without considering the leader information, as a result of its velocity and position. The kinematic model is reformulated as a formation model by incorporating the model uncertainties and external disturbance. The controller is presented in the two-step process. Firstly, the tracking problem is taken into consideration, which can be used as a platform to design a controller for the multi-agents. The proposed controller is designed based on a non-singular fast terminal sliding mode controller (FTSMC), which drives the tracking error to zero in finite time. It not only ensures the tracking but also handles the problem related to non-singularities. Moreover, the design control scheme is modified using high-gain observer to resolve the undefined fluctuations due to man-made errors in sensors. Secondly, the multi-agent tracking problem is considered; hence, a novel formation control is designed using FTSMC, which ensures the formation pattern as well as tracking. Furthermore, the obstacle avoidance algorithm is incorporated to avoid the collision, inside the region of interest. With the Lyapunov analysis, the stability of the proposed algorithm is verified. As a result, simulated graphs are shown to prove the efficacy of the proposed control scheme.

scholarly journals Adaptive Backstepping Fuzzy Neural Network Fractional-Order Control of Microgyroscope Using a Nonsingular Terminal Sliding Mode Controller

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Juntao Fei ◽  
Xiao Liang
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Terminal Sliding Mode ◽  
Nonsingular Terminal Sliding Mode ◽  
Control Scheme ◽  
Fuzzy Neural ◽  
Neural Network Compensator

An adaptive fractional-order nonsingular terminal sliding mode controller for a microgyroscope is presented with uncertainties and external disturbances using a fuzzy neural network compensator based on a backstepping technique. First, the dynamic of the microgyroscope is transformed into an analogical cascade system to guarantee the application of a backstepping design. Then, a fractional-order nonsingular terminal sliding mode surface is designed which provides an additional degree of freedom, higher precision, and finite convergence without a singularity problem. The proposed control scheme requires no prior knowledge of the unknown dynamics of the microgyroscope system since the fuzzy neural network is utilized to approximate the upper bound of the lumped uncertainties and adaptive algorithms are derived to allow online adjustment of the unknown system parameters. The chattering phenomenon can be reduced simultaneously by the fuzzy neural network compensator. The stability and finite time convergence of the system can be established by the Lyapunov stability theorem. Finally, simulation results verify the effectiveness of the proposed controller and the comparison of root mean square error between different fractional orders and integer order is given to signify the high precision tracking performance of the proposed control scheme.

RBF Neural Network-Based Terminal Sliding Mode Control for Reentry Warhead

2011 ◽  
Vol 63-64 ◽  
pp. 381-384
Author(s):  
Hong Chao Zhao ◽  
Jie Chen ◽  
Hua Zhang Liu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Finite Time ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Research Result ◽  
Sliding Mode Controller ◽  
Moving Mass ◽  
Terminal Sliding Mode ◽  
Moving Mass Control

The existing moving mass control system of a nonspinning reentry warhead could not drive the system error to reach zero in finite time. In order to settle the finite time reach issue, an RBF neural network-based terminal sliding mode controller was presented to design the moving mass control system. It used a terminal sliding mode to ensure that the error reaches zero in finite time. The disturbance and coupled terms of the warhead were treated as uncertainties. An RBF neural network was used to estimate the uncertainties. A nonspinning warhead was taken in the simulation to test the performance of the presented controller. The simulation results show the presented controller has faster tracking speed and higher tracking precision than the former research result.

Independent wheel control system design for highly automated driving

2021 ◽  
pp. 095440702110065
Author(s):  
Shihuan Li ◽  
Lei Wang
Keyword(s):  
Control System ◽  
Control Method ◽  
Autonomous Driving ◽  
Control System Design ◽  
Steering Control ◽  
Automated Driving ◽  
Accuracy And Precision ◽  
Actual Observation ◽  
Control Scheme ◽  
Interference Measurement

For L4 and above autonomous driving levels, the automatic control system has been redundantly designed, and a new steering control method based on brake has been proposed; a new dual-track model has been established through multiple driving tests. The axle part of the model was improved, the accuracy of the transfer function of the model was verified again through acceleration-slide tests; a controller based on interference measurement was designed on the basis of the model, and the relationships between the controller parameters was discussed. Through the linearization of the controller, the robustness of uncertain automobile parameters is discussed; the control scheme is tested and verified through group driving test, and the results prove that the accuracy and precision of the controller meet the requirements, the robustness stability is good. Moreover, the predicted value of the model fits well with the actual observation value, the proposal of this method provides a new idea for avoiding car out of control.

Sign in / Sign up

Export Citation Format

Share Document