Neural Network Based Sliding Mode Lateral Control For Autonomous Vehicle

2020 ◽  
Author(s):  
Lhoussain EL HAJJAMI ◽  
El Mehdi MELLOULI ◽  
Mohammed BERRADA
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
Lateral Control

scholarly journals Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller

2018 ◽  
Vol 233 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Armin Norouzi ◽  
Milad Masoumi ◽  
Ali Barari ◽  
Saina Farrokhpour Sani
Keyword(s):  
Optimization Algorithm ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
Lane Change ◽  
Lateral Control ◽  
Lane Changing ◽  
Dynamic Constraints ◽  
Bicycle Model ◽  
Double Lane Change ◽  
Backstepping Controller

In this paper, a novel Lyapunov-based robust controller by using meta-heuristic optimization algorithm has been proposed for lateral control of an autonomous vehicle. In the first step, double lane change path has been designed using a fifth-degree polynomial (quantic) function and dynamic constraints. A lane changing path planning method has been used to design the double lane change manoeuvre. In the next step, position and orientation errors have been extracted based on the two-degree-of-freedom vehicle bicycle model. A combination of sliding mode and backstepping controllers has been used to control the steering in this paper. Overall stability of the combined controller has been analytically proved by defining a Lyapunov function and based on Lyapunov stability theorem. The proposed controller includes some constant parameters which have effects on controller performance; therefore, particle swarm optimization algorithm has been used for finding optimum values of these parameters. The comparing result of the proposed controller with backstepping controller illustrated the better performance of the proposed controller, especially in the low road frictions. Simulation of designed controllers has been conducted by linking CarSim software with Matlab/Simulink which provides a nonlinear full vehicle model. The simulation was performed for manoeuvres with different durations and road frictions. The proposed controller has outperformed the backstepping controller, especially in low frictions.

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.

scholarly journals Neural Network Based Robust Lateral Control for an Autonomous Vehicle

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 510
Author(s):  
Subrat Kumar Swain ◽  
Jagat J. Rath ◽  
Kalyana C. Veluvolu
Keyword(s):  
Neural Network ◽  
Environmental Conditions ◽  
High Frequency ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
External Disturbances ◽  
High Frequency Oscillations ◽  
Equivalent Control ◽  
Adverse Environmental Conditions ◽  
Lane Keeping

The lateral motion of an Automated Vehicle (AV) is highly affected by the model’s uncertainties and unknown external disturbances during its navigation in adverse environmental conditions. Among the variety of controllers, the sliding mode controller (SMC), known for its robustness towards disturbances, is considered to generate a robust control signal under uncertainties. However, conventional SMC suffers from the issue of high frequency oscillations, called chattering. To address the issue of chattering and reduce the effect of unknown external disturbances in the absence of precise model information, a radial basis function neural network (RBFNN) is employed to estimate the equivalent control. Further, a higher order sliding mode (HOSM) based switching control is proposed in this paper to compensate for the effect of external disturbances. The effectiveness of the proposed controller in terms of lane-keeping and lateral stability is demonstrated through simulation in a high-fidelity Carsim-Matlab Simulink environment under a variety of road and environmental conditions.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

scholarly journals Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 40076-40085
Author(s):  
Ngoc Phi Nguyen ◽  
Nguyen Xuan Mung ◽  
Ha Le Nhu Ngoc Thanh ◽  
Tuan Tu Huynh ◽  
Ngoc Tam Lam ◽  
...  
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Adaptive Sliding Mode Control ◽  
Adaptive Sliding Mode ◽  
Mode Control

scholarly journals Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

Sign in / Sign up

Export Citation Format

Share Document